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COVID_PAMLresults_332hits_plusBatScreens_plusDGINN_20200506.txt deleted 100644 → 0
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# Evolutionary history of SARS-CoV-2 interactome in bats and primates identifies key virus-host interfaces and conflicts
## Introduction
The current COVID-19 pandemic is caused by a novel coronavirus strain, SARS-CoV-2. It originated from the cross-species transmission of a coronavirus from the bat reservoir, directly or through an intermediate host to humans. This catastrophic spillover underlines the necessity to better understand how viruses and hosts have shaped one another over evolutionary time.
Pathogenic viruses put a selective pressure on the host-viral interacting proteins. Identifying which host genes bear signatures of such evolutionary conflict (e.g. positive selection) can lead to the identification of the proteins that have been the most relevant in the response to a virus family. Here, we have used this evolutionary framework to decipher which interactions between the SARS-CoV-2-like viruses and our cells have been important in vivo. In addition, identifying traces of positive selection in different hosts phylogenetic lineages also sheds lights on ancient epidemics and how virus-host determinants may be species specific. This may help to understand differences in susceptibility and pathogenicity to SARS-CoV-like viruses between hosts.
To achieve this, we characterized the evolutionary history of the SARS-CoV-2 interactome identified in in vitro studies: 332 host proteins identified by mass-spectrometry by [Gordon and collaborators](https://www.nature.com/articles/s41586-020-2286-9), as well as two essential SARS-CoV-2 entry factors, the angiotensin converting enzyme 2 (ACE2) and the transmembrane serine protease 2 (TMPRSS2) genes. We characterized their evolution in primates (tracing the human history) and in bats (the natural viral reservoir). To do so, we used [DGINN](https://academic.oup.com/nar/article/48/18/e103/5907962?login=true), a novel computational pipeline to Detect Genetic INNovations in protein-coding genes, which embeds gold-standard methods to perform phylogenetic and positive selection analyses in a high-throughput manner.
## Data formating
Requisite R packages: formatR, tinytex
~
Script to merge DGINN outputs from different batch of analysis and included or correct rows corresponding to genes ran on corrected alignmenents.
```
rnw_scripts/covid_comp_script0_table.pdf
```
Input tables in **data/**.
Output tables in **out_tab/**
The tables output from this script will be used for the following analysis steps.
## Primates Results
**Requisite R packages**: *Mondrian, UpSetR*
~
Script to compare primates screen with Gordon et al.'s positive selection analysis.
```
rnw_scripts/covid_comp_dataset.pdf
```
Main input tables in **out_tab/** and Young's result table in **data/**.
Output tables in **figure/1_xxx**
## Comparison between datasets primates and bats
**Requisite R packages**: *Mondrian, UpSetR, dendextend, ggraph, igraph, tidyverse, viridis.*
~
Script to compare bats and primates screen.
```
rnw_scripts/covid_comp_dataset.pdf
```
Input tables in **out_tab/**.
Output tables in **figure/2_xxx**
## Comparison with MAIC score and pancorona analysis
Script to compare the DGINN screen results to [MAIC](https://www.nature.com/articles/s41598-020-79033-3) score and [pancorona data](https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-020-02480-z).
```
rnw_scripts/covid_comp_maic_pancorona.pdf
```
Input tables in **out_tab/**.
Output tables in **figure/3_xxx**
## GWAS analysis
Get sites under positive selection in FYCO1.
```
rnw_scripts/covid_comp_gwas.pdf
```
Input tables in **out_tab/**.
Output tables in **figure/4_xxx**
covid_comp_bats.Rnw deleted 100644 → 0
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\documentclass[11pt, oneside]{article} % use "amsart" instead of "article" for AMSLaTeX format
%\usepackage{geometry} % See geometry.pdf to learn the layout options. There are lots.
%\geometry{letterpaper} % ... or a4paper or a5paper or ...
%\geometry{landscape} % Activate for for rotated page geometry
%\usepackage[parfill]{parskip} % Activate to begin paragraphs with an empty line rather than an indent
%\usepackage{graphicx} % Use pdf, png, jpg, or eps with pdflatex; use eps in DVI mode
% TeX will automatically convert eps --> pdf in pdflatex
%\usepackage{amssymb}
\usepackage[utf8]{inputenc}
%\usepackage[cyr]{aeguill}
%\usepackage[francais]{babel}
%\usepackage{hyperref}
\title{Positive selection on genes interacting with SARS-Cov2, comparison of different analysis}
\author{Marie Cariou}
\date{Mars 2021} % Activate to display a given date or no date
\begin{document}
\maketitle
\tableofcontents
\newpage
\section{Data}
Analysis were formatted by the script covid\_comp\_script0\_table.Rnw.
<<>>=
home<-"/home/adminmarie/Documents/"
workdir<-paste0(home,"CIRI_BIBS_projects/2020_05_Etienne_covid/")
tab<-read.delim(paste0(workdir,
"covid_comp/covid_comp_complete.txt"), h=T, sep="\t")
dim(tab)
tab$Gene.name<-as.character(tab$Gene.name.x)
tab$Gene.name[tab$PreyGene=="MTARC1"]<-"MTARC1"
@
\section{Comparison Bats}
\subsection{Cooper-bats results VS DGINN-bats results}
<<omegaM7M8bats>>=
tab$bats_omegaM0codeml[tab$bats_omegaM0codeml=="na"]<-NA
plot(tab$cooper.batsAverage_dNdS,
as.numeric(as.character(tab$bats_omegaM0codeml)),
xlab="Omega Cooper-bats",
ylab="Omega DGINN-bats")
abline(0,1)
abline(lm(as.numeric(as.character(tab$bats_omegaM0codeml))~
tab$cooper.batsAverage_dNdS),
col="red")
outlier<-tab[tab$cooper.batsAverage_dNdS>0.35 &
as.numeric(as.character(tab$bats_omegaM0codeml))<0.3,]
text(x=outlier$cooper.batsAverage_dNdS,
y=as.numeric(as.character(outlier$bats_omegaM0codeml)),
outlier$Gene.name)
@
\section{Overlap}
\subsection{Data}
<<subbats>>=
tmp<-na.omit(tab[,c("Gene.name", "bats_codemlM7M8_p.value",
"hawkins_Positive.Selection..M8vM8a.p.value",
"cooper.batsM7.M8_p_value", "bats_BUSTED",
"bats_BppM1M2", "bats_BppM7M8", "bats_codemlM1M2",
"bats_codemlM7M8")])
tmp$bats_codemlM7M8_p.value[tmp$bats_codemlM7M8_p.value=="na"]<-NA
tmp$bats_codemlM7M8_p.value<-as.numeric(
as.character(tmp$bats_codemlM7M8_p.value))
dim(tmp)
@
170 genes (present in the 3 experiments)
\subsection{Mondrian}
<<mondrianbats>>=
library(Mondrian)
monddata<-as.data.frame(tmp$Gene.name)
monddata$bats_hawkins<-ifelse(
tmp$hawkins_Positive.Selection..M8vM8a.p.value<0.05, 1, 0)
monddata$bats_cooper<-ifelse(
tmp$cooper.batsM7.M8_p_value<0.05, 1, 0)
dginntmp<-rowSums(cbind(tmp$bats_codemlM1M2=="Y",
tmp$bats_codemlM7M8=="Y",
tmp$bats_BppM1M2=="Y",
tmp$bats_BppM7M8=="Y",
tmp$bats_BUSTED=="Y"))
monddata$bats_dginn<-ifelse(dginntmp>=3, 1,0)
mondrian(monddata[,2:4],
labels=c("DGINN >=3", "hawkins", "Cooper"))
monddata$bats_dginn<-ifelse(dginntmp>=4, 1,0)
mondrian(monddata[,2:4],
labels=c("DGINN >=4", "hawkins", "Cooper"))
@
\subsection{subsetR}
<<subsetbats>>=
library(UpSetR)
upsetdata<-as.data.frame(tmp$Gene.name)
upsetdata$bats_hawkins<-ifelse(
tmp$hawkins_Positive.Selection..M8vM8a.p.value<0.05, 1, 0)
upsetdata$bats_cooper<-ifelse(
tmp$cooper.batsM7.M8_p_value<0.05, 1, 0)
upsetdata$bats_dginn<-ifelse(dginntmp>=3, 1,0)
upset(upsetdata, nsets = 3, matrix.color = "#DC267F",
main.bar.color = "#648FFF", sets.bar.color = "#FE6100")
upsetdata$bats_dginn<-ifelse(dginntmp>=4, 1,0)
upset(upsetdata, nsets = 3, matrix.color = "#DC267F",
main.bar.color = "#648FFF", sets.bar.color = "#FE6100")
@
<<>>=
source("covid_comp_shiny.R")
df<-read.delim(paste0(workdir,
"/data/DGINN_202005281649summary_cleaned.csv"),
fill=T, h=T, sep=",")
names(df)
dftmp<-tab[,c("bats_File", "bats_Name",
"Gene.name", "bats_GeneSize",
"bats_NbSpecies", "bats_omegaM0Bpp",
"bats_omegaM0codeml", "bats_BUSTED",
"bats_BUSTED_p.value", "bats_MEME_NbSites",
"bats_MEME_PSS", "bats_BppM1M2",
"bats_BppM1M2_p.value", "bats_BppM1M2_NbSites",
"bats_BppM1M2_PSS", "bats_BppM7M8",
"bats_BppM7M8_p.value", "bats_BppM7M8_NbSites",
"bats_BppM7M8_PSS", "bats_codemlM1M2",
"bats_codemlM1M2_p.value", "bats_codemlM1M2_NbSites",
"bats_codemlM1M2_PSS", "bats_codemlM7M8",
"bats_codemlM7M8_p.value", "bats_codemlM7M8_NbSites" ,
"bats_codemlM7M8_PSS")]
names(dftmp)<-names(df)
makeFig1(dftmp)
@
\end{document}
covid_comp_bats.pdf deleted 100644 → 0
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covid_comp_bats.tex deleted 100644 → 0
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\documentclass[11pt, oneside]{article}\usepackage[]{graphicx}\usepackage[]{color}
% maxwidth is the original width if it is less than linewidth
% otherwise use linewidth (to make sure the graphics do not exceed the margin)
\makeatletter
\def\maxwidth{ %
\ifdim\Gin@nat@width>\linewidth
\linewidth
\else
\Gin@nat@width
\fi
}
\makeatother
\definecolor{fgcolor}{rgb}{0.345, 0.345, 0.345}
\newcommand{\hlnum}[1]{\textcolor[rgb]{0.686,0.059,0.569}{#1}}%
\newcommand{\hlstr}[1]{\textcolor[rgb]{0.192,0.494,0.8}{#1}}%
\newcommand{\hlcom}[1]{\textcolor[rgb]{0.678,0.584,0.686}{\textit{#1}}}%
\newcommand{\hlopt}[1]{\textcolor[rgb]{0,0,0}{#1}}%
\newcommand{\hlstd}[1]{\textcolor[rgb]{0.345,0.345,0.345}{#1}}%
\newcommand{\hlkwa}[1]{\textcolor[rgb]{0.161,0.373,0.58}{\textbf{#1}}}%
\newcommand{\hlkwb}[1]{\textcolor[rgb]{0.69,0.353,0.396}{#1}}%
\newcommand{\hlkwc}[1]{\textcolor[rgb]{0.333,0.667,0.333}{#1}}%
\newcommand{\hlkwd}[1]{\textcolor[rgb]{0.737,0.353,0.396}{\textbf{#1}}}%
\let\hlipl\hlkwb
\usepackage{framed}
\makeatletter
\newenvironment{kframe}{%
\def\at@end@of@kframe{}%
\ifinner\ifhmode%
\def\at@end@of@kframe{\end{minipage}}%
\begin{minipage}{\columnwidth}%
\fi\fi%
\def\FrameCommand##1{\hskip\@totalleftmargin \hskip-\fboxsep
\colorbox{shadecolor}{##1}\hskip-\fboxsep
% There is no \\@totalrightmargin, so:
\hskip-\linewidth \hskip-\@totalleftmargin \hskip\columnwidth}%
\MakeFramed {\advance\hsize-\width
\@totalleftmargin\z@ \linewidth\hsize
\@setminipage}}%
{\par\unskip\endMakeFramed%
\at@end@of@kframe}
\makeatother
\definecolor{shadecolor}{rgb}{.97, .97, .97}
\definecolor{messagecolor}{rgb}{0, 0, 0}
\definecolor{warningcolor}{rgb}{1, 0, 1}
\definecolor{errorcolor}{rgb}{1, 0, 0}
\newenvironment{knitrout}{}{} % an empty environment to be redefined in TeX
\usepackage{alltt} % use "amsart" instead of "article" for AMSLaTeX format
%\usepackage{geometry} % See geometry.pdf to learn the layout options. There are lots.
%\geometry{letterpaper} % ... or a4paper or a5paper or ...
%\geometry{landscape} % Activate for for rotated page geometry
%\usepackage[parfill]{parskip} % Activate to begin paragraphs with an empty line rather than an indent
%\usepackage{graphicx} % Use pdf, png, jpg, or eps with pdflatex; use eps in DVI mode
% TeX will automatically convert eps --> pdf in pdflatex
%\usepackage{amssymb}
\usepackage[utf8]{inputenc}
%\usepackage[cyr]{aeguill}
%\usepackage[francais]{babel}
%\usepackage{hyperref}
\title{Positive selection on genes interacting with SARS-Cov2, comparison of different analysis}
\author{Marie Cariou}
\date{Mars 2021} % Activate to display a given date or no date
\IfFileExists{upquote.sty}{\usepackage{upquote}}{}
\begin{document}
\maketitle
\tableofcontents
\newpage
\section{Data}
Analysis were formatted by the script covid\_comp\_script0\_table.Rnw.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{home}\hlkwb{<-}\hlstr{"/home/adminmarie/Documents/"}
\hlstd{workdir}\hlkwb{<-}\hlkwd{paste0}\hlstd{(home,}\hlstr{"CIRI_BIBS_projects/2020_05_Etienne_covid/"}\hlstd{)}
\hlstd{tab}\hlkwb{<-}\hlkwd{read.delim}\hlstd{(}\hlkwd{paste0}\hlstd{(workdir,}
\hlstr{"covid_comp/covid_comp_complete.txt"}\hlstd{),} \hlkwc{h}\hlstd{=T,} \hlkwc{sep}\hlstd{=}\hlstr{"\textbackslash{}t"}\hlstd{)}
\hlkwd{dim}\hlstd{(tab)}
\end{alltt}
\begin{verbatim}
## [1] 332 141
\end{verbatim}
\begin{alltt}
\hlstd{tab}\hlopt{$}\hlstd{Gene.name}\hlkwb{<-}\hlkwd{as.character}\hlstd{(tab}\hlopt{$}\hlstd{Gene.name.x)}
\hlstd{tab}\hlopt{$}\hlstd{Gene.name[tab}\hlopt{$}\hlstd{PreyGene}\hlopt{==}\hlstr{"MTARC1"}\hlstd{]}\hlkwb{<-}\hlstr{"MTARC1"}
\end{alltt}
\end{kframe}
\end{knitrout}
\section{Comparison Bats}
\subsection{Cooper-bats results VS DGINN-bats results}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{tab}\hlopt{$}\hlstd{bats_omegaM0codeml[tab}\hlopt{$}\hlstd{bats_omegaM0codeml}\hlopt{==}\hlstr{"na"}\hlstd{]}\hlkwb{<-}\hlnum{NA}
\hlkwd{plot}\hlstd{(tab}\hlopt{$}\hlstd{cooper.batsAverage_dNdS,}
\hlkwd{as.numeric}\hlstd{(}\hlkwd{as.character}\hlstd{(tab}\hlopt{$}\hlstd{bats_omegaM0codeml)),}
\hlkwc{xlab}\hlstd{=}\hlstr{"Omega Cooper-bats"}\hlstd{,}
\hlkwc{ylab}\hlstd{=}\hlstr{"Omega DGINN-bats"}\hlstd{)}
\hlkwd{abline}\hlstd{(}\hlnum{0}\hlstd{,}\hlnum{1}\hlstd{)}
\hlkwd{abline}\hlstd{(}\hlkwd{lm}\hlstd{(}\hlkwd{as.numeric}\hlstd{(}\hlkwd{as.character}\hlstd{(tab}\hlopt{$}\hlstd{bats_omegaM0codeml))}\hlopt{~}
\hlstd{tab}\hlopt{$}\hlstd{cooper.batsAverage_dNdS),}
\hlkwc{col}\hlstd{=}\hlstr{"red"}\hlstd{)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{cooper.batsAverage_dNdS}\hlopt{>}\hlnum{0.35} \hlopt{&}
\hlkwd{as.numeric}\hlstd{(}\hlkwd{as.character}\hlstd{(tab}\hlopt{$}\hlstd{bats_omegaM0codeml))}\hlopt{<}\hlnum{0.3}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{cooper.batsAverage_dNdS,}
\hlkwc{y}\hlstd{=}\hlkwd{as.numeric}\hlstd{(}\hlkwd{as.character}\hlstd{(outlier}\hlopt{$}\hlstd{bats_omegaM0codeml)),}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/omegaM7M8bats-1}
\end{knitrout}
\subsection{Cooper-bats VS Hawkins-bats and DGINN-bats VS Hawkins-bats}
\section{Overlap}
\subsection{Data}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{tmp}\hlkwb{<-}\hlkwd{na.omit}\hlstd{(tab[,}\hlkwd{c}\hlstd{(}\hlstr{"Gene.name"}\hlstd{,} \hlstr{"bats_codemlM7M8_p.value"}\hlstd{,}
\hlstr{"hawkins_Positive.Selection..M8vM8a.p.value"}\hlstd{,}
\hlstr{"cooper.batsM7.M8_p_value"}\hlstd{,} \hlstr{"bats_BUSTED"}\hlstd{,}
\hlstr{"bats_BppM1M2"}\hlstd{,} \hlstr{"bats_BppM7M8"}\hlstd{,} \hlstr{"bats_codemlM1M2"}\hlstd{,}
\hlstr{"bats_codemlM7M8"}\hlstd{)])}
\hlstd{tmp}\hlopt{$}\hlstd{bats_codemlM7M8_p.value[tmp}\hlopt{$}\hlstd{bats_codemlM7M8_p.value}\hlopt{==}\hlstr{"na"}\hlstd{]}\hlkwb{<-}\hlnum{NA}
\hlstd{tmp}\hlopt{$}\hlstd{bats_codemlM7M8_p.value}\hlkwb{<-}\hlkwd{as.numeric}\hlstd{(}
\hlkwd{as.character}\hlstd{(tmp}\hlopt{$}\hlstd{bats_codemlM7M8_p.value))}
\hlkwd{dim}\hlstd{(tmp)}
\end{alltt}
\begin{verbatim}
## [1] 174 9
\end{verbatim}
\end{kframe}
\end{knitrout}
170 genes (present in the 3 experiments)
\subsection{Mondrian}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{library}\hlstd{(Mondrian)}
\hlstd{monddata}\hlkwb{<-}\hlkwd{as.data.frame}\hlstd{(tmp}\hlopt{$}\hlstd{Gene.name)}
\hlstd{monddata}\hlopt{$}\hlstd{bats_hawkins}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(}
\hlstd{tmp}\hlopt{$}\hlstd{hawkins_Positive.Selection..M8vM8a.p.value}\hlopt{<}\hlnum{0.05}\hlstd{,} \hlnum{1}\hlstd{,} \hlnum{0}\hlstd{)}
\hlstd{monddata}\hlopt{$}\hlstd{bats_cooper}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(}
\hlstd{tmp}\hlopt{$}\hlstd{cooper.batsM7.M8_p_value}\hlopt{<}\hlnum{0.05}\hlstd{,} \hlnum{1}\hlstd{,} \hlnum{0}\hlstd{)}
\hlstd{dginntmp}\hlkwb{<-}\hlkwd{rowSums}\hlstd{(}\hlkwd{cbind}\hlstd{(tmp}\hlopt{$}\hlstd{bats_codemlM1M2}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tmp}\hlopt{$}\hlstd{bats_codemlM7M8}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tmp}\hlopt{$}\hlstd{bats_BppM1M2}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tmp}\hlopt{$}\hlstd{bats_BppM7M8}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tmp}\hlopt{$}\hlstd{bats_BUSTED}\hlopt{==}\hlstr{"Y"}\hlstd{))}
\hlstd{monddata}\hlopt{$}\hlstd{bats_dginn}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginntmp}\hlopt{>=}\hlnum{3}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlkwd{mondrian}\hlstd{(monddata[,}\hlnum{2}\hlopt{:}\hlnum{4}\hlstd{],}
\hlkwc{labels}\hlstd{=}\hlkwd{c}\hlstd{(}\hlstr{"DGINN >=3"}\hlstd{,} \hlstr{"hawkins"}\hlstd{,} \hlstr{"Cooper"}\hlstd{))}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/mondrianbats-1}
\begin{kframe}\begin{alltt}
\hlstd{monddata}\hlopt{$}\hlstd{bats_dginn}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginntmp}\hlopt{>=}\hlnum{4}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlkwd{mondrian}\hlstd{(monddata[,}\hlnum{2}\hlopt{:}\hlnum{4}\hlstd{],}
\hlkwc{labels}\hlstd{=}\hlkwd{c}\hlstd{(}\hlstr{"DGINN >=4"}\hlstd{,} \hlstr{"hawkins"}\hlstd{,} \hlstr{"Cooper"}\hlstd{))}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/mondrianbats-2}
\end{knitrout}
\subsection{subsetR}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{library}\hlstd{(UpSetR)}
\hlstd{upsetdata}\hlkwb{<-}\hlkwd{as.data.frame}\hlstd{(tmp}\hlopt{$}\hlstd{Gene.name)}
\hlstd{upsetdata}\hlopt{$}\hlstd{bats_hawkins}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(}
\hlstd{tmp}\hlopt{$}\hlstd{hawkins_Positive.Selection..M8vM8a.p.value}\hlopt{<}\hlnum{0.05}\hlstd{,} \hlnum{1}\hlstd{,} \hlnum{0}\hlstd{)}
\hlstd{upsetdata}\hlopt{$}\hlstd{bats_cooper}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(}
\hlstd{tmp}\hlopt{$}\hlstd{cooper.batsM7.M8_p_value}\hlopt{<}\hlnum{0.05}\hlstd{,} \hlnum{1}\hlstd{,} \hlnum{0}\hlstd{)}
\hlstd{upsetdata}\hlopt{$}\hlstd{bats_dginn}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginntmp}\hlopt{>=}\hlnum{3}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlkwd{upset}\hlstd{(upsetdata,} \hlkwc{nsets} \hlstd{=} \hlnum{3}\hlstd{,} \hlkwc{matrix.color} \hlstd{=} \hlstr{"#DC267F"}\hlstd{,}
\hlkwc{main.bar.color} \hlstd{=} \hlstr{"#648FFF"}\hlstd{,} \hlkwc{sets.bar.color} \hlstd{=} \hlstr{"#FE6100"}\hlstd{)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/subsetbats-1}
\begin{kframe}\begin{alltt}
\hlstd{upsetdata}\hlopt{$}\hlstd{bats_dginn}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginntmp}\hlopt{>=}\hlnum{4}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlkwd{upset}\hlstd{(upsetdata,} \hlkwc{nsets} \hlstd{=} \hlnum{3}\hlstd{,} \hlkwc{matrix.color} \hlstd{=} \hlstr{"#DC267F"}\hlstd{,}
\hlkwc{main.bar.color} \hlstd{=} \hlstr{"#648FFF"}\hlstd{,} \hlkwc{sets.bar.color} \hlstd{=} \hlstr{"#FE6100"}\hlstd{)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/subsetbats-2}
\end{knitrout}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{source}\hlstd{(}\hlstr{"covid_comp_shiny.R"}\hlstd{)}
\hlstd{df}\hlkwb{<-}\hlkwd{read.delim}\hlstd{(}\hlkwd{paste0}\hlstd{(workdir,}
\hlstr{"/data/DGINN_202005281649summary_cleaned.csv"}\hlstd{),}
\hlkwc{fill}\hlstd{=T,} \hlkwc{h}\hlstd{=T,} \hlkwc{sep}\hlstd{=}\hlstr{","}\hlstd{)}
\hlkwd{names}\hlstd{(df)}
\end{alltt}
\begin{verbatim}
## [1] "File" "Name" "Gene"
## [4] "GeneSize" "NbSpecies" "omegaM0Bpp"
## [7] "omegaM0codeml" "BUSTED" "BUSTED.p.value"
## [10] "MEME.NbSites" "MEME.PSS" "BppM1M2"
## [13] "BppM1M2.p.value" "BppM1M2.NbSites" "BppM1M2.PSS"
## [16] "BppM7M8" "BppM7M8.p.value" "BppM7M8.NbSites"
## [19] "BppM7M8.PSS" "codemlM1M2" "codemlM1M2.p.value"
## [22] "codemlM1M2.NbSites" "codemlM1M2.PSS" "codemlM7M8"
## [25] "codemlM7M8.p.value" "codemlM7M8.NbSites" "codemlM7M8.PSS"
\end{verbatim}
\begin{alltt}
\hlstd{dftmp}\hlkwb{<-}\hlstd{tab[,}\hlkwd{c}\hlstd{(}\hlstr{"bats_File"}\hlstd{,} \hlstr{"bats_Name"}\hlstd{,}
\hlstr{"Gene.name"}\hlstd{,} \hlstr{"bats_GeneSize"}\hlstd{,}
\hlstr{"bats_NbSpecies"}\hlstd{,} \hlstr{"bats_omegaM0Bpp"}\hlstd{,}
\hlstr{"bats_omegaM0codeml"}\hlstd{,} \hlstr{"bats_BUSTED"}\hlstd{,}
\hlstr{"bats_BUSTED_p.value"}\hlstd{,} \hlstr{"bats_MEME_NbSites"}\hlstd{,}
\hlstr{"bats_MEME_PSS"}\hlstd{,} \hlstr{"bats_BppM1M2"}\hlstd{,}
\hlstr{"bats_BppM1M2_p.value"}\hlstd{,} \hlstr{"bats_BppM1M2_NbSites"}\hlstd{,}
\hlstr{"bats_BppM1M2_PSS"}\hlstd{,} \hlstr{"bats_BppM7M8"}\hlstd{,}
\hlstr{"bats_BppM7M8_p.value"}\hlstd{,} \hlstr{"bats_BppM7M8_NbSites"}\hlstd{,}
\hlstr{"bats_BppM7M8_PSS"}\hlstd{,} \hlstr{"bats_codemlM1M2"}\hlstd{,}
\hlstr{"bats_codemlM1M2_p.value"}\hlstd{,} \hlstr{"bats_codemlM1M2_NbSites"}\hlstd{,}
\hlstr{"bats_codemlM1M2_PSS"}\hlstd{,} \hlstr{"bats_codemlM7M8"}\hlstd{,}
\hlstr{"bats_codemlM7M8_p.value"}\hlstd{,} \hlstr{"bats_codemlM7M8_NbSites"} \hlstd{,}
\hlstr{"bats_codemlM7M8_PSS"}\hlstd{)]}
\hlkwd{names}\hlstd{(dftmp)}\hlkwb{<-}\hlkwd{names}\hlstd{(df)}
\hlkwd{makeFig1}\hlstd{(dftmp)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/unnamed-chunk-2-1}
\end{knitrout}
\end{document}
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\documentclass[11pt, oneside]{article}\usepackage[]{graphicx}\usepackage[]{color}
% maxwidth is the original width if it is less than linewidth
% otherwise use linewidth (to make sure the graphics do not exceed the margin)
\makeatletter
\def\maxwidth{ %
\ifdim\Gin@nat@width>\linewidth
\linewidth
\else
\Gin@nat@width
\fi
}
\makeatother
\definecolor{fgcolor}{rgb}{0.345, 0.345, 0.345}
\newcommand{\hlnum}[1]{\textcolor[rgb]{0.686,0.059,0.569}{#1}}%
\newcommand{\hlstr}[1]{\textcolor[rgb]{0.192,0.494,0.8}{#1}}%
\newcommand{\hlcom}[1]{\textcolor[rgb]{0.678,0.584,0.686}{\textit{#1}}}%
\newcommand{\hlopt}[1]{\textcolor[rgb]{0,0,0}{#1}}%
\newcommand{\hlstd}[1]{\textcolor[rgb]{0.345,0.345,0.345}{#1}}%
\newcommand{\hlkwa}[1]{\textcolor[rgb]{0.161,0.373,0.58}{\textbf{#1}}}%
\newcommand{\hlkwb}[1]{\textcolor[rgb]{0.69,0.353,0.396}{#1}}%
\newcommand{\hlkwc}[1]{\textcolor[rgb]{0.333,0.667,0.333}{#1}}%
\newcommand{\hlkwd}[1]{\textcolor[rgb]{0.737,0.353,0.396}{\textbf{#1}}}%
\let\hlipl\hlkwb
\usepackage{framed}
\makeatletter
\newenvironment{kframe}{%
\def\at@end@of@kframe{}%
\ifinner\ifhmode%
\def\at@end@of@kframe{\end{minipage}}%
\begin{minipage}{\columnwidth}%
\fi\fi%
\def\FrameCommand##1{\hskip\@totalleftmargin \hskip-\fboxsep
\colorbox{shadecolor}{##1}\hskip-\fboxsep
% There is no \\@totalrightmargin, so:
\hskip-\linewidth \hskip-\@totalleftmargin \hskip\columnwidth}%
\MakeFramed {\advance\hsize-\width
\@totalleftmargin\z@ \linewidth\hsize
\@setminipage}}%
{\par\unskip\endMakeFramed%
\at@end@of@kframe}
\makeatother
\definecolor{shadecolor}{rgb}{.97, .97, .97}
\definecolor{messagecolor}{rgb}{0, 0, 0}
\definecolor{warningcolor}{rgb}{1, 0, 1}
\definecolor{errorcolor}{rgb}{1, 0, 0}
\newenvironment{knitrout}{}{} % an empty environment to be redefined in TeX
\usepackage{alltt} % use "amsart" instead of "article" for AMSLaTeX format
%\usepackage{geometry} % See geometry.pdf to learn the layout options. There are lots.
%\geometry{letterpaper} % ... or a4paper or a5paper or ...
%\geometry{landscape} % Activate for for rotated page geometry
%\usepackage[parfill]{parskip} % Activate to begin paragraphs with an empty line rather than an indent
%\usepackage{graphicx} % Use pdf, png, jpg, or eps with pdflatex; use eps in DVI mode
% TeX will automatically convert eps --> pdf in pdflatex
%\usepackage{amssymb}
\usepackage[utf8]{inputenc}
%\usepackage[cyr]{aeguill}
%\usepackage[francais]{babel}
%\usepackage{hyperref}
\title{Positive selection on genes interacting with SARS-Cov2, comparison of different analysis, maic}
\author{Marie Cariou}
\date{March 2021} % Activate to display a given date or no date
\IfFileExists{upquote.sty}{\usepackage{upquote}}{}
\begin{document}
\maketitle
\tableofcontents
\newpage
\section{Data}
output from covid\_comp\_dataset.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{tablo}\hlkwb{<-}\hlkwd{read.table}\hlstd{(}\hlstr{"primatesVbats.csv"}\hlstd{,}
\hlkwc{h}\hlstd{=T,} \hlkwc{sep}\hlstd{=}\hlstr{","}\hlstd{)}
\end{alltt}
\end{kframe}
\end{knitrout}
Output MAIC formatted by Léa. This table includes the DGINN "score".
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{home}\hlkwb{<-}\hlstr{"/home/adminmarie/Documents/"}
\hlstd{workdir}\hlkwb{<-}\hlkwd{paste0}\hlstd{(home,} \hlstr{"CIRI_BIBS_projects/2020_05_Etienne_covid/"}\hlstd{)}
\hlstd{maic}\hlkwb{<-}\hlkwd{read.table}\hlstd{(}\hlkwd{paste0}\hlstd{(workdir,} \hlstr{"data/covid_comp_maic.txt"}\hlstd{),}
\hlkwc{h}\hlstd{=T)}
\end{alltt}
\end{kframe}
\end{knitrout}
\section{MAIC}
\subsection{Boxplot}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{par}\hlstd{(}\hlkwc{mfrow}\hlstd{=}\hlkwd{c}\hlstd{(}\hlnum{2}\hlstd{,}\hlnum{1}\hlstd{))}
\hlkwd{boxplot}\hlstd{(maic}\hlopt{$}\hlstd{rank}\hlopt{~}\hlstd{maic}\hlopt{$}\hlstd{nbats,} \hlkwc{notch}\hlstd{=}\hlnum{TRUE}\hlstd{,} \hlkwc{varwidth}\hlstd{=}\hlnum{TRUE}\hlstd{,} \hlkwc{xlab}\hlstd{=}\hlstr{"score DGINN"}\hlstd{,} \hlkwc{ylab}\hlstd{=}\hlstr{"rank MAIC"}\hlstd{,} \hlkwc{main}\hlstd{=}\hlstr{"Bats"}\hlstd{)}
\end{alltt}
{\ttfamily\noindent\color{warningcolor}{\#\# Warning in bxp(list(stats = structure(c(21, 825, 1664, 2860, 5392, 15, 625.5, : some notches went outside hinges ('box'): maybe set notch=FALSE}}\begin{alltt}
\hlkwd{stripchart}\hlstd{(maic}\hlopt{$}\hlstd{rank}\hlopt{~}\hlstd{maic}\hlopt{$}\hlstd{nbats,} \hlkwc{method}\hlstd{=}\hlstr{"jitter"}\hlstd{,} \hlkwc{vertical}\hlstd{=}\hlnum{TRUE}\hlstd{,} \hlkwc{pch}\hlstd{=}\hlnum{1}\hlstd{,} \hlkwc{cex}\hlstd{=}\hlnum{0.3}\hlstd{,} \hlkwc{add}\hlstd{=}\hlnum{TRUE}\hlstd{)}
\hlkwd{boxplot}\hlstd{(maic}\hlopt{$}\hlstd{rank}\hlopt{~}\hlstd{maic}\hlopt{$}\hlstd{nprimates,} \hlkwc{notch}\hlstd{=}\hlnum{TRUE}\hlstd{,} \hlkwc{xlab}\hlstd{=}\hlstr{"score DGINN"}\hlstd{,} \hlkwc{ylab}\hlstd{=}\hlstr{"rank MAIC"}\hlstd{,} \hlkwc{main}\hlstd{=}\hlstr{"Primates"}\hlstd{)}
\hlkwd{stripchart}\hlstd{(maic}\hlopt{$}\hlstd{rank}\hlopt{~}\hlstd{maic}\hlopt{$}\hlstd{nprimates,} \hlkwc{method}\hlstd{=}\hlstr{"jitter"}\hlstd{,} \hlkwc{vertical}\hlstd{=}\hlnum{TRUE}\hlstd{,} \hlkwc{pch}\hlstd{=}\hlnum{1}\hlstd{,} \hlkwc{cex}\hlstd{=}\hlnum{0.3}\hlstd{,} \hlkwc{add}\hlstd{=}\hlnum{TRUE}\hlstd{)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/boxplot-1}
\end{knitrout}
\subsection{Dotchart}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{tmp}\hlkwb{<-}\hlstd{maic[maic}\hlopt{$}\hlstd{nbats}\hlopt{>=}\hlnum{3}\hlstd{,} \hlkwd{c}\hlstd{(}\hlstr{"gene"}\hlstd{,} \hlstr{"rank"}\hlstd{,} \hlstr{"nbats"}\hlstd{)]}
\hlstd{tmp}\hlkwb{<-}\hlstd{tmp[}\hlkwd{order}\hlstd{(tmp}\hlopt{$}\hlstd{rank,} \hlkwc{decreasing} \hlstd{=} \hlnum{TRUE}\hlstd{),]}
\hlstd{tmp}\hlopt{$}\hlstd{col}\hlkwb{<-}\hlstr{"black"}
\hlstd{tmp}\hlopt{$}\hlstd{col[tmp}\hlopt{$}\hlstd{gene}\hlopt{==}\hlstr{"ACE2"}\hlstd{]}\hlkwb{<-}\hlstr{"red"}
\hlstd{tmp}\hlopt{$}\hlstd{col[tmp}\hlopt{$}\hlstd{gene}\hlopt{==}\hlstr{"TMPRSS2"}\hlstd{]}\hlkwb{<-}\hlstr{"red"}
\hlstd{tmp}\hlopt{$}\hlstd{pch[tmp}\hlopt{$}\hlstd{nbats}\hlopt{==}\hlnum{5}\hlstd{]}\hlkwb{<-}\hlnum{1}
\hlstd{tmp}\hlopt{$}\hlstd{pch[tmp}\hlopt{$}\hlstd{nbats}\hlopt{==}\hlnum{4}\hlstd{]}\hlkwb{<-}\hlnum{20}
\hlstd{tmp}\hlopt{$}\hlstd{pch[tmp}\hlopt{$}\hlstd{nbats}\hlopt{==}\hlnum{3}\hlstd{]}\hlkwb{<-}\hlnum{4}
\hlkwd{dotchart}\hlstd{(tmp}\hlopt{$}\hlstd{rank,} \hlkwc{main}\hlstd{=}\hlstr{"Bats DGINN >=3"}\hlstd{,} \hlkwc{xlab}\hlstd{=}\hlstr{"rank MAIC"}\hlstd{,} \hlkwc{labels}\hlstd{=tmp}\hlopt{$}\hlstd{gene,} \hlkwc{pch}\hlstd{=tmp}\hlopt{$}\hlstd{pch,} \hlkwc{col}\hlstd{=tmp}\hlopt{$}\hlstd{col)}
\hlkwd{legend}\hlstd{(}\hlstr{"topright"}\hlstd{,} \hlkwd{c}\hlstd{(}\hlstr{"5 (score DGINN)"}\hlstd{,} \hlstr{"4"}\hlstd{,} \hlstr{"3"}\hlstd{),} \hlkwc{pch}\hlstd{=}\hlkwd{c}\hlstd{(}\hlnum{1}\hlstd{,}\hlnum{20}\hlstd{,}\hlnum{4}\hlstd{))}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/dotbats-1}
\end{knitrout}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{tmp}\hlkwb{<-}\hlstd{maic[maic}\hlopt{$}\hlstd{nprimates}\hlopt{>=}\hlnum{3}\hlstd{,} \hlkwd{c}\hlstd{(}\hlstr{"gene"}\hlstd{,} \hlstr{"rank"}\hlstd{,} \hlstr{"nprimates"}\hlstd{)]}
\hlstd{tmp}\hlkwb{<-}\hlstd{tmp[}\hlkwd{order}\hlstd{(tmp}\hlopt{$}\hlstd{rank,} \hlkwc{decreasing} \hlstd{=} \hlnum{TRUE}\hlstd{),]}
\hlstd{tmp}\hlopt{$}\hlstd{pch[tmp}\hlopt{$}\hlstd{nprimates}\hlopt{==}\hlnum{5}\hlstd{]}\hlkwb{<-}\hlnum{1}
\hlstd{tmp}\hlopt{$}\hlstd{pch[tmp}\hlopt{$}\hlstd{nprimates}\hlopt{==}\hlnum{4}\hlstd{]}\hlkwb{<-}\hlnum{20}
\hlstd{tmp}\hlopt{$}\hlstd{pch[tmp}\hlopt{$}\hlstd{nprimates}\hlopt{==}\hlnum{3}\hlstd{]}\hlkwb{<-}\hlnum{4}
\hlstd{tmp}\hlopt{$}\hlstd{col}\hlkwb{<-}\hlstr{"black"}
\hlstd{tmp}\hlopt{$}\hlstd{col[tmp}\hlopt{$}\hlstd{gene}\hlopt{==}\hlstr{"ACE2"}\hlstd{]}\hlkwb{<-}\hlstr{"red"}
\hlstd{tmp}\hlopt{$}\hlstd{col[tmp}\hlopt{$}\hlstd{gene}\hlopt{==}\hlstr{"TMPRSS2"}\hlstd{]}\hlkwb{<-}\hlstr{"red"}
\hlkwd{dotchart}\hlstd{(tmp}\hlopt{$}\hlstd{rank,} \hlkwc{main}\hlstd{=}\hlstr{"Primates DGINN >=3"}\hlstd{,} \hlkwc{xlab}\hlstd{=}\hlstr{"rank MAIC"}\hlstd{,} \hlkwc{labels}\hlstd{=tmp}\hlopt{$}\hlstd{gene,} \hlkwc{pch}\hlstd{=tmp}\hlopt{$}\hlstd{pch,} \hlkwc{cex}\hlstd{=}\hlnum{0.8}\hlstd{,} \hlkwc{col}\hlstd{=tmp}\hlopt{$}\hlstd{col)}
\hlkwd{legend}\hlstd{(}\hlstr{"topright"}\hlstd{,} \hlkwd{c}\hlstd{(}\hlstr{"5 (score DGINN)"}\hlstd{,} \hlstr{"4"}\hlstd{,} \hlstr{"3"}\hlstd{),} \hlkwc{pch}\hlstd{=}\hlkwd{c}\hlstd{(}\hlnum{1}\hlstd{,}\hlnum{20}\hlstd{,}\hlnum{4}\hlstd{))}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/dotprimates-1}
\end{knitrout}
\section{Pan Corona}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{pancorona}\hlkwb{<-}\hlkwd{read.table}\hlstd{(}\hlkwd{paste0}\hlstd{(workdir,} \hlstr{"data/pancorona_S5.csv"}\hlstd{),}
\hlkwc{h}\hlstd{=T,} \hlkwc{fill} \hlstd{=} \hlnum{TRUE}\hlstd{,} \hlkwc{sep}\hlstd{=}\hlstr{"\textbackslash{}t"}\hlstd{)}
\hlkwd{names}\hlstd{(pancorona)}\hlkwb{<-}\hlkwd{c}\hlstd{(}\hlstr{"tmp.Gene.name"}\hlstd{,} \hlkwd{names}\hlstd{(pancorona)[}\hlopt{-}\hlnum{1}\hlstd{])}
\hlcom{# Genes en commun}
\hlstd{pancorona}\hlopt{$}\hlstd{tmp.Gene.name[pancorona}\hlopt{$}\hlstd{tmp.Gene.name} \hlopt{%in%} \hlstd{tablo}\hlopt{$}\hlstd{tmp.Gene.name]}
\end{alltt}
\begin{verbatim}
## [1] TBK1 MARK3 GIGYF2 MARK2 G3BP1 LARP1 ACE2 PABPC1
## [9] TMPRSS2 AP3B1 CLCC1 CSDE1 HECTD1 MARK1 MEPCE PDE4DIP
## [17] POR PRKAR2B RAB5C RTN4 SRP54 UBAP2 UBAP2L UBXN8
## [25] SPART BZW2 EIF4E2 SMOC1 STOML2 DDX21 FAM98A G3BP2
## [33] MOV10 PABPC4 UPF1
## 105 Levels: ACE2 ANPEP AP3B1 ATXN2L BTF3 BZW2 CKAP5 CLCC1 ... YTHDF2
\end{verbatim}
\begin{alltt}
\hlcom{# Uniquement dans le tableau pancorona}
\hlkwd{sort}\hlstd{(pancorona}\hlopt{$}\hlstd{tmp.Gene.name[(pancorona}\hlopt{$}\hlstd{tmp.Gene.name} \hlopt{%in%} \hlstd{tablo}\hlopt{$}\hlstd{tmp.Gene.name)}\hlopt{==}\hlnum{FALSE}\hlstd{])}
\end{alltt}
\begin{verbatim}
## [1] ANPEP ATXN2L BTF3 CKAP5 CTSB CTSL
## [7] CYB5R3 DDX1 DDX5 DDX58 DHX9 DNM1L
## [13] EEF1A1 EIF2A EIF3F EIF4B EZR FLNA
## [19] FURIN FUS GSK3A GSK3B HDLBP HNRNPA1
## [25] HNRNPD HNRNPF HNRNPU IFIH1 IGF2BP1 IKBKB
## [31] IKBKE IRF3 ISG15 KPNA3 KPNB1 MYH9
## [37] NCL POLD1 POLR2B PRKRA RBM14 RCHY1
## [43] RPL13A RPL26 RPS13 RPS17 RPS19 RPS9
## [49] SDCBP SERBP1 SGTA SLC1A5 SNAP47 SSB
## [55] STING1 SYNCRIP TANC1 TBCB TMPRSS11D TRAF3
## [61] TUBA4A TUBB2A TUBB4A TUBB6 USP10 VPS36
## [67] XRCC5 XRCC6 YBX1 YTHDF2
## 105 Levels: ACE2 ANPEP AP3B1 ATXN2L BTF3 BZW2 CKAP5 CLCC1 ... YTHDF2
\end{verbatim}
\begin{alltt}
\hlcom{## Uniquement dans tableau }
\hlkwd{sort}\hlstd{(tablo}\hlopt{$}\hlstd{tmp.Gene.name[(tablo}\hlopt{$}\hlstd{tmp.Gene.name} \hlopt{%in%} \hlstd{pancorona}\hlopt{$}\hlstd{tmp.Gene.name)}\hlopt{==}\hlnum{FALSE}\hlstd{])}
\end{alltt}
\begin{verbatim}
## [1] AAR2 AASS AATF ABCC1 ACAD9 ACADM
## [7] ACSL3 ADAM9 ADAMTS1 AGPS AKAP8 AKAP8L
## [13] AKAP9 ALG11 ALG5 ALG8 ANO6 AP2A2
## [19] AP2M1 ARF6 ATE1 ATP13A3 ATP1B1 ATP6AP1
## [25] ATP6V1A BAG5 BCKDK BRD2 BRD4 CCDC86
## [31] CDK5RAP2 CENPF CEP112 CEP135 CEP250 CEP350
## [37] CEP68 CHMP2A CHPF CHPF2 CISD3 CIT
## [43] CLIP4 CNTRL COL6A1 COLGALT1 COMT COQ8B
## [49] CRTC3 CSNK2A2 CSNK2B CUL2 CWC27 CYB5B
## [55] DCAF7 DCAKD DCTPP1 DDX10 DNAJC11 DNAJC19
## [61] DNMT1 DPH5 DPY19L1 ECSIT EDEM3 EIF4H
## [67] ELOC EMC1 ERC1 ERGIC1 ERLEC1 ERMP1
## [73] ERO1B ERP44 ETFA EXOSC2 EXOSC3 EXOSC5
## [79] EXOSC8 F2RL1 FAM162A FAM8A1 FAR2 FASTKD5
## [85] FBLN5 FBN1 FBN2 FBXL12 FKBP10 FKBP15
## [91] FKBP7 FOXRED2 FYCO1 GCC1 GCC2 GDF15
## [97] GFER GGCX GGH GHITM GLA GNB1
## [103] GNG5 GOLGA2 GOLGA3 GOLGA7 GOLGB1 GORASP1
## [109] GPAA1 GPX1 GRIPAP1 GRPEL1 GTF2F2 HDAC2
## [115] HEATR3 HMOX1 HOOK1 HS2ST1 HS6ST2 HSBP1
## [121] HYOU1 IDE IL17RA IMPDH2 INHBE INTS4
## [127] ITGB1 JAKMIP1 KDELC1 KDELC2 LARP4B LARP7
## [133] LMAN2 LOX MAP7D1 MARC1 MAT2B MDN1
## [139] MIB1 MIPOL1 MOGS MPHOSPH10 MRPS2 MRPS25
## [145] MRPS27 MRPS5 MTCH1 MYCBP2 NARS2 NAT14
## [151] NDFIP2 NDUFAF1 NDUFAF2 NDUFB9 NEK9 NEU1
## [157] NGDN NGLY1 NIN NINL NLRX1 NOL10
## [163] NPC2 NPTX1 NSD2 NUP210 NUP214 NUP54
## [169] NUP58 NUP62 NUP88 NUP98 NUTF2 OS9
## [175] PCNT PCSK5 PCSK6 PDZD11 PIGO PIGS
## [181] PITRM1 PKP2 PLAT PLD3 PLEKHA5 PLEKHF2
## [187] PLOD2 PMPCA PMPCB POFUT1 POLA1 POLA2
## [193] PPIL3 PPT1 PRIM1 PRIM2 PRKACA PRKAR2A
## [199] PRRC2B PSMD8 PTBP2 PTGES2 PUSL1 PVR
## [205] QSOX2 RAB10 RAB14 RAB18 RAB1A RAB2A
## [211] RAB7A RAB8A RAE1 RALA RAP1GDS1 RBM28
## [217] RBM41 RBX1 RDX REEP5 REEP6 RETREG3
## [223] RHOA RIPK1 RNF41 RPL36 RRP9 SAAL1
## [229] SBNO1 SCAP SCARB1 SCCPDH SDF2 SELENOS
## [235] SEPSECS SIL1 SIRT5 SLC25A21 SLC27A2 SLC30A6
## [241] SLC30A7 SLC30A9 SLC44A2 SLC9A3R1 SLU7 SNIP1
## [247] SRP19 SRP72 STC2 STOM SUN2 TAPT1
## [253] TARS2 TBCA TBKBP1 TCF12 THTPA TIMM10
## [259] TIMM10B TIMM29 TIMM8B TIMM9 TLE1 TLE3
## [265] TM2D3 TMED5 TMEM39B TMEM97 TOMM70 TOR1A
## [271] TOR1AIP1 TRIM59 TRMT1 TUBGCP2 TUBGCP3 TYSND1
## [277] UGGT2 USP54 VPS11 VPS39 WASHC4 WFS1
## [283] YIF1A ZC3H18 ZC3H7A ZDHHC5 ZNF318 ZNF503
## [289] ZYG11B
## 324 Levels: AAR2 AASS AATF ABCC1 ACAD9 ACADM ACE2 ACSL3 ... ZYG11B
\end{verbatim}
\end{kframe}
\end{knitrout}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{pancorona}\hlkwb{<-}\hlstd{pancorona[,}\hlkwd{c}\hlstd{(}\hlstr{"tmp.Gene.name"}\hlstd{,} \hlstr{"TOTAL"}\hlstd{)]}
\hlstd{pandginn}\hlkwb{<-}\hlkwd{na.omit}\hlstd{(}\hlkwd{merge}\hlstd{(pancorona, tablo,} \hlkwc{by}\hlstd{=}\hlstr{"tmp.Gene.name"}\hlstd{,} \hlkwc{all.x}\hlstd{=}\hlnum{TRUE}\hlstd{))}
\hlstd{pandginn}\hlkwb{<-}\hlstd{pandginn[}\hlkwd{order}\hlstd{(pandginn}\hlopt{$}\hlstd{nprimates),]}
\hlstd{pandginn}\hlkwb{<-}\hlstd{pandginn[}\hlkwd{order}\hlstd{(pandginn}\hlopt{$}\hlstd{TOTAL),]}
\hlkwd{dotchart}\hlstd{(}\hlkwd{as.matrix}\hlstd{(pandginn[,}\hlnum{2}\hlstd{]),} \hlkwc{labels} \hlstd{= pandginn}\hlopt{$}\hlstd{tmp.Gene.name,} \hlkwc{xlim}\hlstd{=}\hlkwd{c}\hlstd{(}\hlnum{0}\hlstd{,}\hlnum{5}\hlstd{))}
\hlkwd{points}\hlstd{(pandginn[,}\hlnum{4}\hlstd{],} \hlnum{1}\hlopt{:}\hlkwd{nrow}\hlstd{(pandginn),} \hlkwc{col}\hlstd{=}\hlstr{"blue"}\hlstd{,} \hlkwc{pch}\hlstd{=}\hlnum{20}\hlstd{,} \hlkwc{cex}\hlstd{=}\hlnum{0.7}\hlstd{)}
\hlkwd{points}\hlstd{(pandginn[,}\hlnum{3}\hlstd{],} \hlnum{1}\hlopt{:}\hlkwd{nrow}\hlstd{(pandginn),} \hlkwc{col}\hlstd{=}\hlstr{"blue"}\hlstd{,} \hlkwc{pch}\hlstd{=}\hlnum{4}\hlstd{)}
\hlkwd{legend}\hlstd{(}\hlstr{"bottomright"}\hlstd{,} \hlkwd{c}\hlstd{(}\hlstr{"pancorona score"}\hlstd{,} \hlstr{"dginn primate score"}\hlstd{,} \hlstr{"dginn bats score"}\hlstd{),} \hlkwc{pch}\hlstd{=}\hlkwd{c}\hlstd{(}\hlnum{1}\hlstd{,}\hlnum{20}\hlstd{,}\hlnum{4}\hlstd{),} \hlkwc{col}\hlstd{=}\hlkwd{c}\hlstd{(}\hlstr{"black"}\hlstd{,} \hlstr{"blue"}\hlstd{,} \hlstr{"blue"}\hlstd{))}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/pancorona-1}
\end{knitrout}
\end{document}
covid_comp_primate-Young-focus.Rnw deleted 100644 → 0
View file @ 77f559a1
\documentclass[11pt, oneside]{article} % use "amsart" instead of "article" for AMSLaTeX format
%\usepackage{geometry} % See geometry.pdf to learn the layout options. There are lots.
%\geometry{letterpaper} % ... or a4paper or a5paper or ...
%\geometry{landscape} % Activate for for rotated page geometry
%\usepackage[parfill]{parskip} % Activate to begin paragraphs with an empty line rather than an indent
%\usepackage{graphicx} % Use pdf, png, jpg, or eps with pdflatex; use eps in DVI mode
% TeX will automatically convert eps --> pdf in pdflatex
%\usepackage{amssymb}
\usepackage[utf8]{inputenc}
%\usepackage[cyr]{aeguill}
%\usepackage[francais]{babel}
%\usepackage{hyperref}
\title{Positive selection on genes interacting with SARS-Cov2, comparison of different analysis}
\author{Marie Cariou}
\date{March 2021} % Activate to display a given date or no date
\begin{document}
\maketitle
\tableofcontents
\newpage
\section{Files manipulations}
\subsection{Complete table}
<<>>=
home<-"/home/adminmarie/Documents/"
workdir<-paste0(home, "CIRI_BIBS_projects/2020_05_Etienne_covid/")
tab<-read.delim(paste0(workdir,
"covid_comp/covid_comp_complete.txt"), h=T, sep="\t")
dim(tab)
tab$Gene.name<-as.character(tab$Gene.name.x)
tab$Gene.name[tab$PreyGene=="MARC1"]<-"MARC1"
@
\subsection{Read DGINN Young table}
DGINN-Young-primate table correspond to DGINN results, on the SAME alignment as Young-primate.
I will merge the 2 tables.
<<>>=
dginnY<-read.delim(paste0(workdir,
"data/summary_primate_young.res"),
fill=T, h=T)
dim(dginnY)
names(dginnY)
@
<<>>=
add_col<-function(method="PamlM1M2"){
tmp<-dginnY[dginnY$Method==method,
c("Gene", "Omega", "PosSel", "PValue", "NbSites", "PSS")]
names(tmp)<-c("Gene.name", paste0("Omega_", method),
paste0("PosSel_", method), paste0("PValue_", method),
paste0("NbSites_", method), paste0("PSS_", method))
tab<-merge(tab, tmp, by="Gene.name")
return(tab)
}
tab<-add_col("PamlM1M2")
tab<-add_col("PamlM7M8")
tab<-add_col("BppM1M2")
tab<-add_col("BppM7M8")
# Manip pour la colonne BUSTED
tmp<-dginnY[dginnY$Method=="BUSTED",c("Gene", "Omega", "PosSel", "PValue")]
names(tmp)<-c("Gene.name", "Omega_BUSTED", "PosSel_BUSTED", "PValue_BUSTED")
tab<-merge(tab, tmp, by="Gene.name")
tmp<-dginnY[dginnY$Method=="MEME",c("Gene", "NbSites", "PSS")]
names(tmp)<-c("Gene.name", "NbSites_MEME", "PSS_MEME")
tab<-merge(tab, tmp, by="Gene.name")
dim(tab)
@
\section{Comparisons Primates}
\subsection{DGINN results on Janet Young's alignments (DGINN-Young-primate) VS Janet Young's results}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "omega" dans la sortie de dginn.
<<omegaM7M8>>=
tab$whole.gene.dN.dS.model.0<-as.numeric(
as.character(tab$whole.gene.dN.dS.model.0))
plot(tab$whole.gene.dN.dS.model.0, tab$Omega_PamlM7M8,
xlab="Omega Young-primate", ylab="Omega DGINN-Young-primate")
abline(0,1)
outlier<-tab[tab$whole.gene.dN.dS.model.0<0.2 & tab$Omega_PamlM7M8>0.4,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=(outlier$Omega_PamlM7M8+0.01),
outlier$Gene.name)
outlier<-tab[tab$whole.gene.dN.dS.model.0<0.6 & tab$Omega_PamlM7M8>0.7,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=(outlier$Omega_PamlM7M8+0.01),
outlier$Gene.name)
@
\subsection{DGINN results on Janet Young's alignments (DGINN-Young-primate) VS DGINN-full's results}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "omega" dans la sortie de dginn.
<<omegaM7M8_2>>=
tab$'dginn.primate_omegaM0Bpp'<-as.numeric(
as.character(tab$'dginn.primate_omegaM0Bpp'))
plot(tab$'dginn.primate_omegaM0Bpp', tab$Omega_PamlM7M8,
xlab="DGINN-full's", ylab="Omega DGINN-Young-primate")
abline(0,1)
outlier<-tab[tab$'dginn.primate_omegaM0Bpp'>0.4 & tab$Omega_PamlM7M8<0.2,]
text(x=outlier$'dginn.primate_omegaM0Bpp',
y=(outlier$Omega_PamlM7M8+0.01),
outlier$Gene.name)
outlier<-tab[tab$'dginn.primate_omegaM0Bpp'>0.5 & tab$Omega_PamlM7M8<0.4,]
text(x=outlier$'dginn.primate_omegaM0Bpp',
y=(outlier$Omega_PamlM7M8+0.01),
outlier$Gene.name)
outlier<-tab[tab$'dginn.primate_omegaM0Bpp'>0.2 & tab$Omega_PamlM7M8>0.6,]
text(x=outlier$'dginn.primate_omegaM0Bpp',
y=(outlier$Omega_PamlM7M8+0.01),
outlier$Gene.name)
@
\subsection{Janet Young's results (Young-primate) VS DGINN-full's results}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "omega" dans la sortie de dginn.
<<omegaM7M8_3>>=
plot(tab$whole.gene.dN.dS.model.0,
as.numeric(as.character(tab$'dginn.primate_omegaM0Bpp')),
xlab="Omega Young-primate", ylab="DGINN-full's")
abline(0,1)
outlier<-tab[tab$whole.gene.dN.dS.model.0<0.4 &
as.numeric(as.character(tab$'dginn.primate_omegaM0Bpp'))>0.5,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=outlier$'dginn.primate_omegaM0Bpp',
outlier$Gene.name)
outlier<-tab[tab$whole.gene.dN.dS.model.0>0.7 &
as.numeric(as.character(tab$'dginn.primate_omegaM0Bpp'))>0,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=outlier$'dginn.primate_omegaM0Bpp',
outlier$Gene.name)
outlier<-tab[tab$whole.gene.dN.dS.model.0<0.1 &
as.numeric(as.character(tab$'dginn.primate_omegaM0Bpp'))>0.3,]
text(x=outlier$whole.gene.dN.dS.model.0+0.03,
y=outlier$'dginn.primate_omegaM0Bpp',
outlier$Gene.name)
@
\section{Overlap}
\subsection{Mondrian}
<<mondrianprimates>>=
library(Mondrian)
monddata<-as.data.frame(tab$Gene.name)
dim(monddata)
dginnyoungtmp<-rowSums(cbind(tab$PosSel_PamlM1M2=="Y",
tab$PosSel_PamlM7M8=="Y",
tab$PosSel_BppM1M2=="Y",
tab$PosSel_BppM7M8=="Y",
tab$PosSel_BUSTED=="Y"))
dginnfulltmp<-rowSums(cbind(tab$'dginn.primate_BUSTED'=="Y",
tab$'dginn.primate_BppM1M2'=="Y",
tab$'dginn.primate_BppM7M8'=="Y",
tab$'dginn.primate_codemlM1M2'=="Y",
tab$'dginn.primate_codemlM7M8'=="Y"))
monddata$primates_young<-ifelse(tab$pVal.M8vsM7<0.05, 1, 0)
monddata$primates_dginn_young<-ifelse(dginnyoungtmp>=3, 1,0)
monddata$primates_dginn_full<-ifelse(dginnfulltmp>=3, 1,0)
mondrian(na.omit(monddata[,2:4]),
labels=c("Young", "DGINN-Young >=3", "DGINN-full >=3" ))
monddata$primates_dginn_young<-ifelse(dginnyoungtmp>=4, 1,0)
monddata$primates_dginn_full<-ifelse(dginnfulltmp>=4, 1,0)
mondrian(na.omit(monddata[,2:4]),
labels=c("Young", "DGINN-Young >=4", "DGINN-full >=4"))
@
Comparison of results with the same method.
<<>>=
monddata$primates_dginn_young<-tab$PosSel_BppM7M8=="Y"
monddata$primates_dginn_full<-tab$'dginn.primate_codemlM7M8'=="Y"
mondrian(na.omit(monddata[,2:4]),
labels=c("Young", "DGINN-Young", "DGINN-full"),
main="posel codeml M7M8")
@
\subsection{subsetR}
Just another representation of the same result, for now, I focuse on the gene positive in 3 methodes for DGINN analysis.
<<subsetprimates>>=
library(UpSetR)
upsetdata<-as.data.frame(tab$Gene.name)
upsetdata$primates_young<-ifelse(tab$pVal.M8vsM7<0.05, 1, 0)
###
upsetdata$primates_dginn_young<-ifelse(dginnyoungtmp>=3, 1,0)
upsetdata$primates_dginn_full<-ifelse(dginnfulltmp>=3, 1,0)
upset(na.omit(upsetdata), nsets = 3, matrix.color = "#DC267F",
main.bar.color = "#648FFF", sets.bar.color = "#FE6100")
@
\section{Gene List}
<<setup, include=FALSE, cache=FALSE, tidy=TRUE>>=
options(tidy=TRUE, width=70)
@
List of the 34 genes found under positive selection in all analysis.
<<>>=
upsetdata$`tab$Gene.name`[(upsetdata$primates_young==TRUE &
upsetdata$primates_dginn_young==TRUE &
upsetdata$primates_dginn_full==TRUE)]
@
List of the 13 genes found under positive selection in both Young analysis and DGINN-Young alignments (but not full-DGINN).
<<>>=
upsetdata$`tab$Gene.name`[(upsetdata$primates_young==TRUE &
upsetdata$primates_dginn_young==TRUE &
upsetdata$primates_dginn_full==FALSE)]
@
List of the 1 gene found under positive selection in both DGINN analysis, but not Young.
<<>>=
upsetdata$`tab$Gene.name`[(upsetdata$primates_young==FALSE &
upsetdata$primates_dginn_young==TRUE &
upsetdata$primates_dginn_full==TRUE)]
@
List of the 8 genes found under positive selection in both Young analysis and full-DGINN, but not DGINN-young.
<<>>=
upsetdata$`tab$Gene.name`[(upsetdata$primates_young==TRUE &
upsetdata$primates_dginn_young==FALSE &
upsetdata$primates_dginn_full==TRUE)]
@
List of the 18 genes found under positive selection ONLY in Young analysis.
<<>>=
upsetdata$`tab$Gene.name`[(upsetdata$primates_young==TRUE &
upsetdata$primates_dginn_young==FALSE &
upsetdata$primates_dginn_full==FALSE)]
@
List of the 1 genes found under positive selection ONLY in DGINN-Young.
<<>>=
upsetdata$`tab$Gene.name`[(upsetdata$primates_young==FALSE &
upsetdata$primates_dginn_young==TRUE &
upsetdata$primates_dginn_full==FALSE)]
@
List of the 44 genes found under positive selection ONLY in full-DGINN.
<<>>=
upsetdata$`tab$Gene.name`[(upsetdata$primates_young==FALSE &
upsetdata$primates_dginn_young==FALSE &
upsetdata$primates_dginn_full==TRUE)]
@
<<echo=FALSE, results="hide">>=
dginnfulltmp<-rowSums(cbind(tab$'dginn.primate_BUSTED'=="Y",
tab$'dginn.primate_BppM1M2'=="Y",
tab$'dginn.primate_BppM7M8'=="Y",
tab$'dginn.primate_codemlM1M2'=="Y",
tab$'dginn.primate_codemlM7M8'=="Y"))
tab$Gene.name[dginnfulltmp>=4 & is.na(dginnfulltmp)==F]
tab$Gene.name[dginnfulltmp>=3 & is.na(dginnfulltmp)==F]
tmp<-tab[dginnfulltmp>=4 & is.na(dginnfulltmp)==F,
c("Gene.name","dginn.primate_BUSTED", "dginn.primate_BppM1M2",
"dginn.primate_BppM7M8","dginn.primate_codemlM1M2","dginn.primate_codemlM7M8")]
write.table(tmp, "geneList_DGINN_full_primate_pos4.txt", row.names=F, quote=F)
@
<<shiny, fig.height=11, echo=FALSE, results="hide", fig="hide">>=
makeFig1 <- function(df){
# prepare data for colors etc
colMethods <- c("deepskyblue4", "darkorange" , "deepskyblue3" , "mediumseagreen" , "yellow3" , "black")
nameMethods <- c("BUSTED", "BppM1M2", "BppM7M8", "codemlM1M2", "codemlM7M8", "MEME")
metColor <- data.frame(Name = nameMethods , Col = colMethods , stringsAsFactors = FALSE)
# subset for this specific figure
#df <- df[df$nbY >= 1, ] # to drop genes found by 0 methods (big datasets)
xt <- df[, c("BUSTED", "BppM1M2", "BppM7M8", "codemlM1M2", "codemlM7M8")]
xt$Gene <- df$Gene
nbrMeth <- 5
# reverse order of dataframe so that genes with the most Y are at the bottom (to be on top of the barplot)
xt[,1:5] <- ifelse(xt[,1:5] == "Y", 1, 0)
# sort and Filter the 0 lines
xt<-xt[order(rowSums(xt[,1:5])),]
xt<-xt[rowSums(xt[,1:5])>2,]
row.names(xt)<-xt$Gene
xt<-xt[,1:5]
colFig1 <- metColor[which(metColor$Name %in% colnames(xt)) , ]
##### PART 1 : NUMBER OF METHODS
par(xpd = NA , mar=c(2,7,4,0) , oma = c(0,0,0,0) , mgp = c(3,0.3,0))
h = barplot(
t(xt),
border = NA ,
axes = F ,
col = adjustcolor(colFig1$Col, alpha.f = 1),
horiz = T ,
las = 2 ,
main = "Methods detecting positive selection" ,
cex.main = 0.85,
cex.names = min(50/nrow(xt), 1.5)
)
axis(3, line = 0, at = c(0:nbrMeth), label = c("0", rep("", nbrMeth -1), nbrMeth), tck = 0.02)
legend("bottomleft",
horiz = T,
border = colFig1$Col,
legend = colFig1$Name,
fill = colFig1$Col,
cex = 0.8,
bty = "n",
xpd = NA
)
}
df<-read.delim(paste0(workdir,
"/data/DGINN_202005281649summary_cleaned.csv"),
fill=T, h=T, sep=",")
#makeFig1(df)
@
\end{document}
covid_comp_primate-Young-focus.pdf deleted 100644 → 0
View file @ 77f559a1
File deleted
covid_comp_primate-Young-focus_backup.Rnw deleted 100644 → 0
View file @ 77f559a1
\documentclass[11pt, oneside]{article} % use "amsart" instead of "article" for AMSLaTeX format
%\usepackage{geometry} % See geometry.pdf to learn the layout options. There are lots.
%\geometry{letterpaper} % ... or a4paper or a5paper or ...
%\geometry{landscape} % Activate for for rotated page geometry
%\usepackage[parfill]{parskip} % Activate to begin paragraphs with an empty line rather than an indent
%\usepackage{graphicx} % Use pdf, png, jpg, or eps with pdflatex; use eps in DVI mode
% TeX will automatically convert eps --> pdf in pdflatex
%\usepackage{amssymb}
\usepackage[utf8]{inputenc}
%\usepackage[cyr]{aeguill}
%\usepackage[francais]{babel}
%\usepackage{hyperref}
\title{Positive selection on genes interacting with SARS-Cov2, comparison of different analysis}
\author{Marie Cariou}
\date{October 2020} % Activate to display a given date or no date
\begin{document}
\maketitle
\tableofcontents
\newpage
\section{Files manipulations}
\subsection{Read Janet Young's table}
<<>>=
workdir<-"/home/adminmarie/Documents/CIRI_BIBS_projects/2020_05_Etienne_covid/"
tab<-read.delim(paste0(workdir,
"data/COVID_PAMLresults_332hits_plusBatScreens_2020_Apr14.csv"),
fill=T, h=T, dec=",")
dim(tab)
#names(tab)
@
\subsection{Read DGINN Young table}
DGINN-Young-primate table correspond to DGINN results, on the SAME alignment as Young-primate.
I will merge the 2 tables.
<<>>=
dginnY<-read.delim(paste0(workdir,
"data/summary_primate_young.res"),
fill=T, h=T)
dim(dginnY)
names(dginnY)
@
\subsection{Joining Young and DGINN Young table}
\textit{I hide some code corresponding to verifications of gene names coherence between tables}
<<results="hide", echo=FALSE>>=
head(tab)[,1:5]
# gene avec un nom bizar dans certaines colomne
tab[158,1:10]
#
length(unique(dginnY$Gene))
length(unique(tab$PreyGene))
length(unique(tab$Gene.name))
#quelle paire de colonne contient le plus de noms identiques
sum(unique(dginnY$Gene) %in% unique(tab$PreyGene))
sum(unique(dginnY$Gene) %in% unique(tab$Gene.name))
# dginn$Gene et tab$Gene.name presque identiques sauf 1 ligne.
# Je soupçonne que c'est celle là:
tab[158,1:10]
# Verif:
tab[,1:10][(tab$Gene.name %in% unique(dginnY$Gene))==F,]
# yep
# Remplacement manuel par
as.character(unique(dginnY$Gene)[(unique(dginnY$Gene) %in% tab$Gene.name)==F])
# dans le tableau de Janet
val_remp=as.character(unique(dginnY$Gene)[(unique(dginnY$Gene) %in% tab$Gene.name)==F])
tab$Gene.name<-as.character(tab$Gene.name)
tab$Gene.name[158]<-val_remp
sum(unique(dginnY$Gene) %in% unique(tab$Gene.name))
@
<<>>=
add_col<-function(method="PamlM1M2"){
tmp<-dginnY[dginnY$Method==method,
c("Gene", "Omega", "PosSel", "PValue", "NbSites", "PSS")]
names(tmp)<-c("Gene.name", paste0("Omega_", method),
paste0("PosSel_", method), paste0("PValue_", method),
paste0("NbSites_", method), paste0("PSS_", method))
tab<-merge(tab, tmp, by="Gene.name")
return(tab)
}
tab<-add_col("PamlM1M2")
tab<-add_col("PamlM7M8")
tab<-add_col("BppM1M2")
tab<-add_col("BppM7M8")
# Manip pour la colonne BUSTED
tmp<-dginnY[dginnY$Method=="BUSTED",c("Gene", "Omega", "PosSel", "PValue")]
names(tmp)<-c("Gene.name", "Omega_BUSTED", "PosSel_BUSTED", "PValue_BUSTED")
tab<-merge(tab, tmp, by="Gene.name")
tmp<-dginnY[dginnY$Method=="MEME",c("Gene", "NbSites", "PSS")]
names(tmp)<-c("Gene.name", "NbSites_MEME", "PSS_MEME")
tab<-merge(tab, tmp, by="Gene.name")
@
\subsection{Read DGINN Table}
<<>>=
dginnT<-read.delim(paste0(workdir,
"/data/DGINN_202005281649summary_cleaned.csv"),
fill=T, h=T, sep=",")
dim(dginnT)
names(dginnT)
# Number of genes in dginn-primate output not present in the original table
dginnT[(dginnT$Gene %in% tab$Gene.name)==F,"Gene"]
# This includes paralogs, recombinations found by DGINN
# and additionnal genes included on purpose
# Number of genes from the original list not present in DGINN output
tab[(tab$Gene.name %in% dginnT$Gene)==F,"Gene.name"]
names(dginnT)<-c("File", "Name", "Gene.name", "GeneSize", "dginn-primate_NbSpecies", "dginn-primate_omegaM0Bpp",
"dginn-primate_omegaM0codeml", "dginn-primate_BUSTED", "dginn-primate_BUSTED.p.value",
"dginn-primate_MEME.NbSites", "dginn-primate_MEME.PSS", "dginn-primate_BppM1M2",
"dginn-primate_BppM1M2.p.value", "dginn-primate_BppM1M2.NbSites", "dginn-primate_BppM1M2.PSS",
"dginn-primate_BppM7M8", "dginn-primate_BppM7M8.p.value", "dginn-primate_BppM7M8.NbSites",
"dginn-primate_BppM7M8.PSS", "dginn-primate_codemlM1M2", "dginn-primate_codemlM1M2.p.value",
"dginn-primate_codemlM1M2.NbSites", "dginn-primate_codemlM1M2.PSS", "dginn-primate_codemlM7M8",
"dginn-primate_codemlM7M8.p.value", "dginn-primate_codemlM7M8.NbSites", "dginn-primate_codemlM7M8.PSS")
@
\subsection{Join Table and DGINN table}
<<>>=
tab<-merge(tab,dginnT, by="Gene.name", all.x=T)
@
\subsection{Write new table}
<<>>=
write.table(tab,
"COVID_PAMLresults_332hits_plusBatScreens_plusDGINN_20201014.txt",
row.names=F, quote=F, sep="\t")
@
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Comparisons Primates}
\subsection{DGINN results on Janet Young's alignments (DGINN-Young-primate) VS Janet Young's results}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "omega" dans la sortie de dginn.
<<omegaM7M8>>=
plot(tab$whole.gene.dN.dS.model.0, tab$Omega_PamlM7M8,
xlab="Omega Young-primate", ylab="Omega DGINN-Young-primate")
abline(0,1)
outlier<-tab[tab$whole.gene.dN.dS.model.0<0.2 & tab$Omega_PamlM7M8>0.4,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=(outlier$Omega_PamlM7M8+0.01),
outlier$Gene.name)
outlier<-tab[tab$whole.gene.dN.dS.model.0<0.6 & tab$Omega_PamlM7M8>0.7,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=(outlier$Omega_PamlM7M8+0.01),
outlier$Gene.name)
@
\subsection{DGINN results on Janet Young's alignments (DGINN-Young-primate) VS DGINN-full's results}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "omega" dans la sortie de dginn.
<<omegaM7M8_2>>=
tab$'dginn-primate_omegaM0Bpp'<-as.numeric(as.character(tab$'dginn-primate_omegaM0Bpp'))
plot(tab$'dginn-primate_omegaM0Bpp', tab$Omega_PamlM7M8,
xlab="DGINN-full's", ylab="Omega DGINN-Young-primate")
abline(0,1)
outlier<-tab[tab$'dginn-primate_omegaM0Bpp'>0.4 & tab$Omega_PamlM7M8<0.2,]
text(x=outlier$'dginn-primate_omegaM0Bpp',
y=(outlier$Omega_PamlM7M8+0.01),
outlier$Gene.name)
outlier<-tab[tab$'dginn-primate_omegaM0Bpp'>0.5 & tab$Omega_PamlM7M8<0.4,]
text(x=outlier$'dginn-primate_omegaM0Bpp',
y=(outlier$Omega_PamlM7M8+0.01),
outlier$Gene.name)
@
\subsection{Janet Young's results (Young-primate) VS DGINN-full's results}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "omega" dans la sortie de dginn.
<<omegaM7M8_3>>=
plot(tab$whole.gene.dN.dS.model.0, as.numeric(as.character(tab$'dginn-primate_omegaM0Bpp')),
xlab="Omega Young-primate", ylab="DGINN-full's")
abline(0,1)
outlier<-tab[tab$whole.gene.dN.dS.model.0<0.4 & tab$'dginn-primate_omegaM0Bpp'>0.5,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=outlier$'dginn-primate_omegaM0Bpp',
outlier$Gene.name)
@
\section{Overlap}
\subsection{Mondrian}
<<mondrianprimates>>=
library(Mondrian)
#######
monddata<-as.data.frame(tab$Gene.name)
dim(monddata)
dginnyoungtmp<-rowSums(cbind(tab$PosSel_PamlM1M2=="Y", tab$PosSel_PamlM7M8=="Y",
tab$PosSel_BppM1M2=="Y", tab$PosSel_BppM7M8=="Y", tab$PosSel_BUSTED=="Y"))
#monddata$primates_dginn_young<-ifelse(tmp$PosSel_PamlM7M8=="Y", 1,0)
dginnfulltmp<-rowSums(cbind(tab$'dginn-primate_BUSTED'=="Y", tab$'dginn-primate_BppM1M2'=="Y",
tab$'dginn-primate_BppM7M8'=="Y", tab$'dginn-primate_codemlM1M2'=="Y", tab$'dginn-primate_codemlM7M8'=="Y"))
monddata$primates_young<-ifelse(tab$pVal.M8vsM7<0.05, 1, 0)
#monddata$primates_cooper<-ifelse(tab$cooper.primates.M7.M8_p_val<0.05, 1, 0)
monddata$primates_dginn_young<-ifelse(dginnyoungtmp>=3, 1,0)
monddata$primates_dginn_full<-ifelse(dginnfulltmp>=3, 1,0)
mondrian(na.omit(monddata[,2:4]), labels=c("Young", "DGINN-Young >=3", "DGINN-full >=3" ))
#####
monddata$primates_dginn_young<-ifelse(dginnyoungtmp>=4, 1,0)
monddata$primates_dginn_full<-ifelse(dginnfulltmp>=4, 1,0)
mondrian(na.omit(monddata[,2:4]), labels=c("Young", "DGINN-Young >=4", "DGINN-full >=4"))
@
Comparison of results with the same method.
<<>>=
#####
monddata$primates_dginn_young<-tab$PosSel_BppM7M8=="Y"
monddata$primates_dginn_full<-tab$'dginn-primate_codemlM7M8'=="Y"
mondrian(na.omit(monddata[,2:4]), labels=c("Young", "DGINN-Young", "DGINN-full"), main="posel codeml M7M8")
@
\subsection{subsetR}
Just another representation of the same result.
<<subsetprimates>>=
library(UpSetR)
upsetdata<-as.data.frame(tab$Gene.name)
upsetdata$primates_young<-ifelse(tab$pVal.M8vsM7<0.05, 1, 0)
###
upsetdata$primates_dginn_young<-ifelse(dginnyoungtmp>=3, 1,0)
upsetdata$primates_dginn_full<-ifelse(dginnfulltmp>=3, 1,0)
upset(na.omit(upsetdata), nsets = 3, matrix.color = "#DC267F",
main.bar.color = "#648FFF", sets.bar.color = "#FE6100")
###
upsetdata$primates_dginn_young<-ifelse(dginnyoungtmp>=4, 1,0)
upsetdata$primates_dginn_full<-ifelse(dginnfulltmp>=4, 1,0)
upset(na.omit(upsetdata), nsets = 3, matrix.color = "#DC267F",
main.bar.color = "#648FFF", sets.bar.color = "#FE6100")
@
\section{Gene List}
Genes under positive selection for at least 4 methods.
<<>>=
dginnfulltmp<-rowSums(cbind(tab$'dginn-primate_BUSTED'=="Y",
tab$'dginn-primate_BppM1M2'=="Y",
tab$'dginn-primate_BppM7M8'=="Y",
tab$'dginn-primate_codemlM1M2'=="Y",
tab$'dginn-primate_codemlM7M8'=="Y"))
tab$Gene.name[dginnfulltmp>=4 & is.na(dginnfulltmp)==F]
tab$Gene.name[dginnfulltmp>=3 & is.na(dginnfulltmp)==F]
tmp<-tab[dginnfulltmp>=4 & is.na(dginnfulltmp)==F,
c("Gene.name","dginn-primate_BUSTED", "dginn-primate_BppM1M2",
"dginn-primate_BppM7M8","dginn-primate_codemlM1M2","dginn-primate_codemlM7M8")]
write.table(tmp, "geneList_DGINN_full_primate_pos4.txt", row.names=F, quote=F)
@
\section{Shiny like}
<<shiny, fig.height=11>>=
makeFig1 <- function(df){
# prepare data for colors etc
colMethods <- c("deepskyblue4", "darkorange" , "deepskyblue3" , "mediumseagreen" , "yellow3" , "black")
nameMethods <- c("BUSTED", "BppM1M2", "BppM7M8", "codemlM1M2", "codemlM7M8", "MEME")
metColor <- data.frame(Name = nameMethods , Col = colMethods , stringsAsFactors = FALSE)
# subset for this specific figure
#df <- df[df$nbY >= 1, ] # to drop genes found by 0 methods (big datasets)
xt <- df[, c("BUSTED", "BppM1M2", "BppM7M8", "codemlM1M2", "codemlM7M8")]
xt$Gene <- df$Gene
nbrMeth <- 5
# reverse order of dataframe so that genes with the most Y are at the bottom (to be on top of the barplot)
xt[,1:5] <- ifelse(xt[,1:5] == "Y", 1, 0)
# sort and Filter the 0 lines
xt<-xt[order(rowSums(xt[,1:5])),]
xt<-xt[rowSums(xt[,1:5])>2,]
row.names(xt)<-xt$Gene
xt<-xt[,1:5]
colFig1 <- metColor[which(metColor$Name %in% colnames(xt)) , ]
##### PART 1 : NUMBER OF METHODS
par(xpd = NA , mar=c(2,7,4,0) , oma = c(0,0,0,0) , mgp = c(3,0.3,0))
h = barplot(
t(xt),
border = NA ,
axes = F ,
col = adjustcolor(colFig1$Col, alpha.f = 1),
horiz = T ,
las = 2 ,
main = "Methods detecting positive selection" ,
cex.main = 0.85,
cex.names = min(50/nrow(xt), 1.5)
)
axis(3, line = 0, at = c(0:nbrMeth), label = c("0", rep("", nbrMeth -1), nbrMeth), tck = 0.02)
legend("bottomleft",
horiz = T,
border = colFig1$Col,
legend = colFig1$Name,
fill = colFig1$Col,
cex = 0.8,
bty = "n",
xpd = NA
)
}
df<-read.delim(paste0(workdir,
"/data/DGINN_202005281649summary_cleaned.csv"),
fill=T, h=T, sep=",")
makeFig1(df)
@
\end{document}
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\documentclass[11pt, oneside]{article}\usepackage[]{graphicx}\usepackage[]{color}
% maxwidth is the original width if it is less than linewidth
% otherwise use linewidth (to make sure the graphics do not exceed the margin)
\makeatletter
\def\maxwidth{ %
\ifdim\Gin@nat@width>\linewidth
\linewidth
\else
\Gin@nat@width
\fi
}
\makeatother
\definecolor{fgcolor}{rgb}{0.345, 0.345, 0.345}
\newcommand{\hlnum}[1]{\textcolor[rgb]{0.686,0.059,0.569}{#1}}%
\newcommand{\hlstr}[1]{\textcolor[rgb]{0.192,0.494,0.8}{#1}}%
\newcommand{\hlcom}[1]{\textcolor[rgb]{0.678,0.584,0.686}{\textit{#1}}}%
\newcommand{\hlopt}[1]{\textcolor[rgb]{0,0,0}{#1}}%
\newcommand{\hlstd}[1]{\textcolor[rgb]{0.345,0.345,0.345}{#1}}%
\newcommand{\hlkwa}[1]{\textcolor[rgb]{0.161,0.373,0.58}{\textbf{#1}}}%
\newcommand{\hlkwb}[1]{\textcolor[rgb]{0.69,0.353,0.396}{#1}}%
\newcommand{\hlkwc}[1]{\textcolor[rgb]{0.333,0.667,0.333}{#1}}%
\newcommand{\hlkwd}[1]{\textcolor[rgb]{0.737,0.353,0.396}{\textbf{#1}}}%
\let\hlipl\hlkwb
\usepackage{framed}
\makeatletter
\newenvironment{kframe}{%
\def\at@end@of@kframe{}%
\ifinner\ifhmode%
\def\at@end@of@kframe{\end{minipage}}%
\begin{minipage}{\columnwidth}%
\fi\fi%
\def\FrameCommand##1{\hskip\@totalleftmargin \hskip-\fboxsep
\colorbox{shadecolor}{##1}\hskip-\fboxsep
% There is no \\@totalrightmargin, so:
\hskip-\linewidth \hskip-\@totalleftmargin \hskip\columnwidth}%
\MakeFramed {\advance\hsize-\width
\@totalleftmargin\z@ \linewidth\hsize
\@setminipage}}%
{\par\unskip\endMakeFramed%
\at@end@of@kframe}
\makeatother
\definecolor{shadecolor}{rgb}{.97, .97, .97}
\definecolor{messagecolor}{rgb}{0, 0, 0}
\definecolor{warningcolor}{rgb}{1, 0, 1}
\definecolor{errorcolor}{rgb}{1, 0, 0}
\newenvironment{knitrout}{}{} % an empty environment to be redefined in TeX
\usepackage{alltt} % use "amsart" instead of "article" for AMSLaTeX format
%\usepackage{geometry} % See geometry.pdf to learn the layout options. There are lots.
%\geometry{letterpaper} % ... or a4paper or a5paper or ...
%\geometry{landscape} % Activate for for rotated page geometry
%\usepackage[parfill]{parskip} % Activate to begin paragraphs with an empty line rather than an indent
%\usepackage{graphicx} % Use pdf, png, jpg, or eps with pdflatex; use eps in DVI mode
% TeX will automatically convert eps --> pdf in pdflatex
%\usepackage{amssymb}
\usepackage[utf8]{inputenc}
%\usepackage[cyr]{aeguill}
%\usepackage[francais]{babel}
%\usepackage{hyperref}
\title{Positive selection on genes interacting with SARS-Cov2, comparison of different analysis}
\author{Marie Cariou}
\date{October 2020} % Activate to display a given date or no date
\IfFileExists{upquote.sty}{\usepackage{upquote}}{}
\begin{document}
\maketitle
\tableofcontents
\newpage
\section{Files manipulations}
\subsection{Read Janet Young's table}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{workdir}\hlkwb{<-}\hlstr{"/home/adminmarie/Documents/CIRI_BIBS_projects/2020_05_Etienne_covid/"}
\hlstd{tab}\hlkwb{<-}\hlkwd{read.delim}\hlstd{(}\hlkwd{paste0}\hlstd{(workdir,}
\hlstr{"data/COVID_PAMLresults_332hits_plusBatScreens_2020_Apr14.csv"}\hlstd{),}
\hlkwc{fill}\hlstd{=T,} \hlkwc{h}\hlstd{=T,} \hlkwc{dec}\hlstd{=}\hlstr{","}\hlstd{)}
\hlkwd{dim}\hlstd{(tab)}
\end{alltt}
\begin{verbatim}
## [1] 332 84
\end{verbatim}
\begin{alltt}
\hlcom{#names(tab)}
\end{alltt}
\end{kframe}
\end{knitrout}
\subsection{Read DGINN Young table}
DGINN-Young-primate table correspond to DGINN results, on the SAME alignment as Young-primate.
I will merge the 2 tables.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{dginnY}\hlkwb{<-}\hlkwd{read.delim}\hlstd{(}\hlkwd{paste0}\hlstd{(workdir,}
\hlstr{"data/summary_primate_young.res"}\hlstd{),}
\hlkwc{fill}\hlstd{=T,} \hlkwc{h}\hlstd{=T)}
\hlkwd{dim}\hlstd{(dginnY)}
\end{alltt}
\begin{verbatim}
## [1] 1992 7
\end{verbatim}
\begin{alltt}
\hlkwd{names}\hlstd{(dginnY)}
\end{alltt}
\begin{verbatim}
## [1] "Gene" "Omega" "Method" "PosSel" "PValue" "NbSites" "PSS"
\end{verbatim}
\end{kframe}
\end{knitrout}
\subsection{Joining Young and DGINN Young table}
\textit{I hide some code corresponding to verifications of gene names coherence between tables}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{add_col}\hlkwb{<-}\hlkwa{function}\hlstd{(}\hlkwc{method}\hlstd{=}\hlstr{"PamlM1M2"}\hlstd{)\{}
\hlstd{tmp}\hlkwb{<-}\hlstd{dginnY[dginnY}\hlopt{$}\hlstd{Method}\hlopt{==}\hlstd{method,}
\hlkwd{c}\hlstd{(}\hlstr{"Gene"}\hlstd{,} \hlstr{"Omega"}\hlstd{,} \hlstr{"PosSel"}\hlstd{,} \hlstr{"PValue"}\hlstd{,} \hlstr{"NbSites"}\hlstd{,} \hlstr{"PSS"}\hlstd{)]}
\hlkwd{names}\hlstd{(tmp)}\hlkwb{<-}\hlkwd{c}\hlstd{(}\hlstr{"Gene.name"}\hlstd{,} \hlkwd{paste0}\hlstd{(}\hlstr{"Omega_"}\hlstd{, method),}
\hlkwd{paste0}\hlstd{(}\hlstr{"PosSel_"}\hlstd{, method),} \hlkwd{paste0}\hlstd{(}\hlstr{"PValue_"}\hlstd{, method),}
\hlkwd{paste0}\hlstd{(}\hlstr{"NbSites_"}\hlstd{, method),} \hlkwd{paste0}\hlstd{(}\hlstr{"PSS_"}\hlstd{, method))}
\hlstd{tab}\hlkwb{<-}\hlkwd{merge}\hlstd{(tab, tmp,} \hlkwc{by}\hlstd{=}\hlstr{"Gene.name"}\hlstd{)}
\hlkwd{return}\hlstd{(tab)}
\hlstd{\}}
\hlstd{tab}\hlkwb{<-}\hlkwd{add_col}\hlstd{(}\hlstr{"PamlM1M2"}\hlstd{)}
\hlstd{tab}\hlkwb{<-}\hlkwd{add_col}\hlstd{(}\hlstr{"PamlM7M8"}\hlstd{)}
\hlstd{tab}\hlkwb{<-}\hlkwd{add_col}\hlstd{(}\hlstr{"BppM1M2"}\hlstd{)}
\hlstd{tab}\hlkwb{<-}\hlkwd{add_col}\hlstd{(}\hlstr{"BppM7M8"}\hlstd{)}
\hlcom{# Manip pour la colonne BUSTED}
\hlstd{tmp}\hlkwb{<-}\hlstd{dginnY[dginnY}\hlopt{$}\hlstd{Method}\hlopt{==}\hlstr{"BUSTED"}\hlstd{,}\hlkwd{c}\hlstd{(}\hlstr{"Gene"}\hlstd{,} \hlstr{"Omega"}\hlstd{,} \hlstr{"PosSel"}\hlstd{,} \hlstr{"PValue"}\hlstd{)]}
\hlkwd{names}\hlstd{(tmp)}\hlkwb{<-}\hlkwd{c}\hlstd{(}\hlstr{"Gene.name"}\hlstd{,} \hlstr{"Omega_BUSTED"}\hlstd{,} \hlstr{"PosSel_BUSTED"}\hlstd{,} \hlstr{"PValue_BUSTED"}\hlstd{)}
\hlstd{tab}\hlkwb{<-}\hlkwd{merge}\hlstd{(tab, tmp,} \hlkwc{by}\hlstd{=}\hlstr{"Gene.name"}\hlstd{)}
\hlstd{tmp}\hlkwb{<-}\hlstd{dginnY[dginnY}\hlopt{$}\hlstd{Method}\hlopt{==}\hlstr{"MEME"}\hlstd{,}\hlkwd{c}\hlstd{(}\hlstr{"Gene"}\hlstd{,} \hlstr{"NbSites"}\hlstd{,} \hlstr{"PSS"}\hlstd{)]}
\hlkwd{names}\hlstd{(tmp)}\hlkwb{<-}\hlkwd{c}\hlstd{(}\hlstr{"Gene.name"}\hlstd{,} \hlstr{"NbSites_MEME"}\hlstd{,} \hlstr{"PSS_MEME"}\hlstd{)}
\hlstd{tab}\hlkwb{<-}\hlkwd{merge}\hlstd{(tab, tmp,} \hlkwc{by}\hlstd{=}\hlstr{"Gene.name"}\hlstd{)}
\end{alltt}
\end{kframe}
\end{knitrout}
\subsection{Read DGINN Table}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{dginnT}\hlkwb{<-}\hlkwd{read.delim}\hlstd{(}\hlkwd{paste0}\hlstd{(workdir,}
\hlstr{"/data/DGINN_202005281649summary_cleaned.csv"}\hlstd{),}
\hlkwc{fill}\hlstd{=T,} \hlkwc{h}\hlstd{=T,} \hlkwc{sep}\hlstd{=}\hlstr{","}\hlstd{)}
\hlkwd{dim}\hlstd{(dginnT)}
\end{alltt}
\begin{verbatim}
## [1] 412 27
\end{verbatim}
\begin{alltt}
\hlkwd{names}\hlstd{(dginnT)}
\end{alltt}
\begin{verbatim}
## [1] "File" "Name" "Gene" "GeneSize" "NbSpecies" "omegaM0Bpp"
## [7] "omegaM0codeml" "BUSTED" "BUSTED.p.value" "MEME.NbSites" "MEME.PSS" "BppM1M2"
## [13] "BppM1M2.p.value" "BppM1M2.NbSites" "BppM1M2.PSS" "BppM7M8" "BppM7M8.p.value" "BppM7M8.NbSites"
## [19] "BppM7M8.PSS" "codemlM1M2" "codemlM1M2.p.value" "codemlM1M2.NbSites" "codemlM1M2.PSS" "codemlM7M8"
## [25] "codemlM7M8.p.value" "codemlM7M8.NbSites" "codemlM7M8.PSS"
\end{verbatim}
\begin{alltt}
\hlcom{# Number of genes in dginn-primate output not present in the original table}
\hlstd{dginnT[(dginnT}\hlopt{$}\hlstd{Gene} \hlopt{%in%} \hlstd{tab}\hlopt{$}\hlstd{Gene.name)}\hlopt{==}\hlstd{F,}\hlstr{"Gene"}\hlstd{]}
\end{alltt}
\begin{verbatim}
## [1] ACE2 ADAM9[0-3120] ADAM9[3119-3927] ATP5MGL C1H1ORF50 CEP135[0-3264] CEP135[3263-3678]
## [8] CEP43 COQ8B COQ8A CSNK2A1 CSNK2B[0-609] CSNK2B[608-2568] CYB5R1
## [15] DDX21[0-717] DDX21[716-2538] DDX50 DNAJC15 DPH5[0-702] DPH5[701-1326] DPY19L2
## [22] ELOC ERO1B EXOSC3[0-1446] EXOSC3[1445-1980] FBN3 GNB4 GNB2
## [29] GNB3 GOLGA7[0-312] GOLGA7[311-549] GPX1[0-1218] GPX1[1217-2946] HDAC1 HS6ST3
## [36] IMPDH1 ITGB1[0-2328] ITGB1[2327-2844] LMAN2L MRPS5[0-1569] MRPS5[1568-3783] MARC2
## [43] MGRN1 NDFIP2[0-768] NDFIP2[767-1314] NDUFAF2[0-258] NDUFAF2[257-744] NSD2 NUP58
## [50] NUP58[0-1824] NUP58[1823-2367] PABPC3 POTPABPC1 PABPC4L PABPC5 PCSK5
## [57] PRIM2[0-1071] PRIM2[1070-1902] PRKACB PRKACG PTGES2[0-1587] PTGES2[1586-2202] RAB8B
## [64] RAB13 RAB18[0-855] RAB18[854-1815] RAB2B RAB5A RAB5B RAB15
## [71] RALB EZR EZR[0-1458] EZR[1457-3771] MSN RETREG3 RHOB
## [78] RHOC SLC44A2[0-2577] SLC44A2[2576-3657] SPART SRP72[0-2604] SRP72[2603-3417] STOM[0-1047]
## [85] STOM[1046-1800] STOML3 TIMM29 TLE4 TLE2 TLE2[0-1302] TLE2[1301-3987]
## [92] TMPRSS2 TOMM70 TOR1B WASHC4 WFS1[0-2346] WFS1[2345-3216] YIF1B
## 411 Levels: AAR2 AASS AATF ABCC1 ACAD9 ACADM ACE2 ACSL3 ADAM9 ADAM9[0-3120] ADAM9[3119-3927] ADAMTS1 AES AGPS AKAP8 AKAP8L AKAP9 ... ZYG11B
\end{verbatim}
\begin{alltt}
\hlcom{# This includes paralogs, recombinations found by DGINN }
\hlcom{# and additionnal genes included on purpose}
\hlcom{# Number of genes from the original list not present in DGINN output}
\hlstd{tab[(tab}\hlopt{$}\hlstd{Gene.name} \hlopt{%in%} \hlstd{dginnT}\hlopt{$}\hlstd{Gene)}\hlopt{==}\hlstd{F,}\hlstr{"Gene.name"}\hlstd{]}
\end{alltt}
\begin{verbatim}
## [1] "ADCK4" "ARL6IP6" "ATP5L" "C19orf52" "C1orf50" "ERO1LB" "FAM134C" "FGFR1OP" "KIAA1033" "MFGE8" "NUPL1" "SIGMAR1"
## [13] "SPG20" "TCEB1" "TCEB2" "TOMM70A" "USP13" "VIMP" "WHSC1"
\end{verbatim}
\begin{alltt}
\hlkwd{names}\hlstd{(dginnT)}\hlkwb{<-}\hlkwd{c}\hlstd{(}\hlstr{"File"}\hlstd{,} \hlstr{"Name"}\hlstd{,} \hlstr{"Gene.name"}\hlstd{,} \hlstr{"GeneSize"}\hlstd{,} \hlstr{"dginn-primate_NbSpecies"}\hlstd{,} \hlstr{"dginn-primate_omegaM0Bpp"}\hlstd{,}
\hlstr{"dginn-primate_omegaM0codeml"}\hlstd{,} \hlstr{"dginn-primate_BUSTED"}\hlstd{,} \hlstr{"dginn-primate_BUSTED.p.value"}\hlstd{,}
\hlstr{"dginn-primate_MEME.NbSites"}\hlstd{,} \hlstr{"dginn-primate_MEME.PSS"}\hlstd{,} \hlstr{"dginn-primate_BppM1M2"}\hlstd{,}
\hlstr{"dginn-primate_BppM1M2.p.value"}\hlstd{,} \hlstr{"dginn-primate_BppM1M2.NbSites"}\hlstd{,} \hlstr{"dginn-primate_BppM1M2.PSS"}\hlstd{,}
\hlstr{"dginn-primate_BppM7M8"}\hlstd{,} \hlstr{"dginn-primate_BppM7M8.p.value"}\hlstd{,} \hlstr{"dginn-primate_BppM7M8.NbSites"}\hlstd{,}
\hlstr{"dginn-primate_BppM7M8.PSS"}\hlstd{,} \hlstr{"dginn-primate_codemlM1M2"}\hlstd{,} \hlstr{"dginn-primate_codemlM1M2.p.value"}\hlstd{,}
\hlstr{"dginn-primate_codemlM1M2.NbSites"}\hlstd{,} \hlstr{"dginn-primate_codemlM1M2.PSS"}\hlstd{,} \hlstr{"dginn-primate_codemlM7M8"}\hlstd{,}
\hlstr{"dginn-primate_codemlM7M8.p.value"}\hlstd{,} \hlstr{"dginn-primate_codemlM7M8.NbSites"}\hlstd{,} \hlstr{"dginn-primate_codemlM7M8.PSS"}\hlstd{)}
\end{alltt}
\end{kframe}
\end{knitrout}
\subsection{Join Table and DGINN table}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{tab}\hlkwb{<-}\hlkwd{merge}\hlstd{(tab,dginnT,} \hlkwc{by}\hlstd{=}\hlstr{"Gene.name"}\hlstd{,} \hlkwc{all.x}\hlstd{=T)}
\end{alltt}
\end{kframe}
\end{knitrout}
\subsection{Write new table}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{write.table}\hlstd{(tab,}
\hlstr{"COVID_PAMLresults_332hits_plusBatScreens_plusDGINN_20201014.txt"}\hlstd{,}
\hlkwc{row.names}\hlstd{=F,} \hlkwc{quote}\hlstd{=F,} \hlkwc{sep}\hlstd{=}\hlstr{"\textbackslash{}t"}\hlstd{)}
\end{alltt}
\end{kframe}
\end{knitrout}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Comparisons Primates}
\subsection{DGINN results on Janet Young's alignments (DGINN-Young-primate) VS Janet Young's results}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "omega" dans la sortie de dginn.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{plot}\hlstd{(tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0, tab}\hlopt{$}\hlstd{Omega_PamlM7M8,}
\hlkwc{xlab}\hlstd{=}\hlstr{"Omega Young-primate"}\hlstd{,} \hlkwc{ylab}\hlstd{=}\hlstr{"Omega DGINN-Young-primate"}\hlstd{)}
\hlkwd{abline}\hlstd{(}\hlnum{0}\hlstd{,}\hlnum{1}\hlstd{)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0}\hlopt{<}\hlnum{0.2} \hlopt{&} \hlstd{tab}\hlopt{$}\hlstd{Omega_PamlM7M8}\hlopt{>}\hlnum{0.4}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0,}
\hlkwc{y}\hlstd{=(outlier}\hlopt{$}\hlstd{Omega_PamlM7M8}\hlopt{+}\hlnum{0.01}\hlstd{),}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0}\hlopt{<}\hlnum{0.6} \hlopt{&} \hlstd{tab}\hlopt{$}\hlstd{Omega_PamlM7M8}\hlopt{>}\hlnum{0.7}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0,}
\hlkwc{y}\hlstd{=(outlier}\hlopt{$}\hlstd{Omega_PamlM7M8}\hlopt{+}\hlnum{0.01}\hlstd{),}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/omegaM7M8-1}
\end{knitrout}
\subsection{DGINN results on Janet Young's alignments (DGINN-Young-primate) VS DGINN-full's results}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "omega" dans la sortie de dginn.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{tab}\hlopt{$}\hlstr{'dginn-primate_omegaM0Bpp'}\hlkwb{<-}\hlkwd{as.numeric}\hlstd{(}\hlkwd{as.character}\hlstd{(tab}\hlopt{$}\hlstr{'dginn-primate_omegaM0Bpp'}\hlstd{))}
\end{alltt}
{\ttfamily\noindent\color{warningcolor}{\#\# Warning: NAs introduits lors de la conversion automatique}}\begin{alltt}
\hlkwd{plot}\hlstd{(tab}\hlopt{$}\hlstr{'dginn-primate_omegaM0Bpp'}\hlstd{, tab}\hlopt{$}\hlstd{Omega_PamlM7M8,}
\hlkwc{xlab}\hlstd{=}\hlstr{"DGINN-full's"}\hlstd{,} \hlkwc{ylab}\hlstd{=}\hlstr{"Omega DGINN-Young-primate"}\hlstd{)}
\hlkwd{abline}\hlstd{(}\hlnum{0}\hlstd{,}\hlnum{1}\hlstd{)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstr{'dginn-primate_omegaM0Bpp'}\hlopt{>}\hlnum{0.4} \hlopt{&} \hlstd{tab}\hlopt{$}\hlstd{Omega_PamlM7M8}\hlopt{<}\hlnum{0.2}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstr{'dginn-primate_omegaM0Bpp'}\hlstd{,}
\hlkwc{y}\hlstd{=(outlier}\hlopt{$}\hlstd{Omega_PamlM7M8}\hlopt{+}\hlnum{0.01}\hlstd{),}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstr{'dginn-primate_omegaM0Bpp'}\hlopt{>}\hlnum{0.5} \hlopt{&} \hlstd{tab}\hlopt{$}\hlstd{Omega_PamlM7M8}\hlopt{<}\hlnum{0.4}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstr{'dginn-primate_omegaM0Bpp'}\hlstd{,}
\hlkwc{y}\hlstd{=(outlier}\hlopt{$}\hlstd{Omega_PamlM7M8}\hlopt{+}\hlnum{0.01}\hlstd{),}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/omegaM7M8_2-1}
\end{knitrout}
\subsection{Janet Young's results (Young-primate) VS DGINN-full's results}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "omega" dans la sortie de dginn.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{plot}\hlstd{(tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0,} \hlkwd{as.numeric}\hlstd{(}\hlkwd{as.character}\hlstd{(tab}\hlopt{$}\hlstr{'dginn-primate_omegaM0Bpp'}\hlstd{)),}
\hlkwc{xlab}\hlstd{=}\hlstr{"Omega Young-primate"}\hlstd{,} \hlkwc{ylab}\hlstd{=}\hlstr{"DGINN-full's"}\hlstd{)}
\hlkwd{abline}\hlstd{(}\hlnum{0}\hlstd{,}\hlnum{1}\hlstd{)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0}\hlopt{<}\hlnum{0.4} \hlopt{&} \hlstd{tab}\hlopt{$}\hlstr{'dginn-primate_omegaM0Bpp'}\hlopt{>}\hlnum{0.5}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0,}
\hlkwc{y}\hlstd{=outlier}\hlopt{$}\hlstr{'dginn-primate_omegaM0Bpp'}\hlstd{,}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/omegaM7M8_3-1}
\end{knitrout}
\section{Overlap}
\subsection{Mondrian}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{library}\hlstd{(Mondrian)}
\hlcom{#######}
\hlstd{monddata}\hlkwb{<-}\hlkwd{as.data.frame}\hlstd{(tab}\hlopt{$}\hlstd{Gene.name)}
\hlkwd{dim}\hlstd{(monddata)}
\end{alltt}
\begin{verbatim}
## [1] 333 1
\end{verbatim}
\begin{alltt}
\hlstd{dginnyoungtmp}\hlkwb{<-}\hlkwd{rowSums}\hlstd{(}\hlkwd{cbind}\hlstd{(tab}\hlopt{$}\hlstd{PosSel_PamlM1M2}\hlopt{==}\hlstr{"Y"}\hlstd{, tab}\hlopt{$}\hlstd{PosSel_PamlM7M8}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstd{PosSel_BppM1M2}\hlopt{==}\hlstr{"Y"}\hlstd{, tab}\hlopt{$}\hlstd{PosSel_BppM7M8}\hlopt{==}\hlstr{"Y"}\hlstd{, tab}\hlopt{$}\hlstd{PosSel_BUSTED}\hlopt{==}\hlstr{"Y"}\hlstd{))}
\hlcom{#monddata$primates_dginn_young<-ifelse(tmp$PosSel_PamlM7M8=="Y", 1,0)}
\hlstd{dginnfulltmp}\hlkwb{<-}\hlkwd{rowSums}\hlstd{(}\hlkwd{cbind}\hlstd{(tab}\hlopt{$}\hlstr{'dginn-primate_BUSTED'}\hlopt{==}\hlstr{"Y"}\hlstd{, tab}\hlopt{$}\hlstr{'dginn-primate_BppM1M2'}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstr{'dginn-primate_BppM7M8'}\hlopt{==}\hlstr{"Y"}\hlstd{, tab}\hlopt{$}\hlstr{'dginn-primate_codemlM1M2'}\hlopt{==}\hlstr{"Y"}\hlstd{, tab}\hlopt{$}\hlstr{'dginn-primate_codemlM7M8'}\hlopt{==}\hlstr{"Y"}\hlstd{))}
\hlstd{monddata}\hlopt{$}\hlstd{primates_young}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(tab}\hlopt{$}\hlstd{pVal.M8vsM7}\hlopt{<}\hlnum{0.05}\hlstd{,} \hlnum{1}\hlstd{,} \hlnum{0}\hlstd{)}
\hlcom{#monddata$primates_cooper<-ifelse(tab$cooper.primates.M7.M8_p_val<0.05, 1, 0)}
\hlstd{monddata}\hlopt{$}\hlstd{primates_dginn_young}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginnyoungtmp}\hlopt{>=}\hlnum{3}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlstd{monddata}\hlopt{$}\hlstd{primates_dginn_full}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginnfulltmp}\hlopt{>=}\hlnum{3}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlkwd{mondrian}\hlstd{(}\hlkwd{na.omit}\hlstd{(monddata[,}\hlnum{2}\hlopt{:}\hlnum{4}\hlstd{]),} \hlkwc{labels}\hlstd{=}\hlkwd{c}\hlstd{(}\hlstr{"Young"}\hlstd{,} \hlstr{"DGINN-Young >=3"}\hlstd{,} \hlstr{"DGINN-full >=3"} \hlstd{))}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/mondrianprimates-1}
\begin{kframe}\begin{alltt}
\hlcom{#####}
\hlstd{monddata}\hlopt{$}\hlstd{primates_dginn_young}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginnyoungtmp}\hlopt{>=}\hlnum{4}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlstd{monddata}\hlopt{$}\hlstd{primates_dginn_full}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginnfulltmp}\hlopt{>=}\hlnum{4}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlkwd{mondrian}\hlstd{(}\hlkwd{na.omit}\hlstd{(monddata[,}\hlnum{2}\hlopt{:}\hlnum{4}\hlstd{]),} \hlkwc{labels}\hlstd{=}\hlkwd{c}\hlstd{(}\hlstr{"Young"}\hlstd{,} \hlstr{"DGINN-Young >=4"}\hlstd{,} \hlstr{"DGINN-full >=4"}\hlstd{))}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/mondrianprimates-2}
\end{knitrout}
Comparison of results with the same method.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlcom{#####}
\hlstd{monddata}\hlopt{$}\hlstd{primates_dginn_young}\hlkwb{<-}\hlstd{tab}\hlopt{$}\hlstd{PosSel_BppM7M8}\hlopt{==}\hlstr{"Y"}
\hlstd{monddata}\hlopt{$}\hlstd{primates_dginn_full}\hlkwb{<-}\hlstd{tab}\hlopt{$}\hlstr{'dginn-primate_codemlM7M8'}\hlopt{==}\hlstr{"Y"}
\hlkwd{mondrian}\hlstd{(}\hlkwd{na.omit}\hlstd{(monddata[,}\hlnum{2}\hlopt{:}\hlnum{4}\hlstd{]),} \hlkwc{labels}\hlstd{=}\hlkwd{c}\hlstd{(}\hlstr{"Young"}\hlstd{,} \hlstr{"DGINN-Young"}\hlstd{,} \hlstr{"DGINN-full"}\hlstd{),} \hlkwc{main}\hlstd{=}\hlstr{"posel codeml M7M8"}\hlstd{)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/unnamed-chunk-8-1}
\end{knitrout}
\subsection{subsetR}
Just another representation of the same result.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{library}\hlstd{(UpSetR)}
\hlstd{upsetdata}\hlkwb{<-}\hlkwd{as.data.frame}\hlstd{(tab}\hlopt{$}\hlstd{Gene.name)}
\hlstd{upsetdata}\hlopt{$}\hlstd{primates_young}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(tab}\hlopt{$}\hlstd{pVal.M8vsM7}\hlopt{<}\hlnum{0.05}\hlstd{,} \hlnum{1}\hlstd{,} \hlnum{0}\hlstd{)}
\hlcom{###}
\hlstd{upsetdata}\hlopt{$}\hlstd{primates_dginn_young}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginnyoungtmp}\hlopt{>=}\hlnum{3}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlstd{upsetdata}\hlopt{$}\hlstd{primates_dginn_full}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginnfulltmp}\hlopt{>=}\hlnum{3}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlkwd{upset}\hlstd{(}\hlkwd{na.omit}\hlstd{(upsetdata),} \hlkwc{nsets} \hlstd{=} \hlnum{3}\hlstd{,} \hlkwc{matrix.color} \hlstd{=} \hlstr{"#DC267F"}\hlstd{,}
\hlkwc{main.bar.color} \hlstd{=} \hlstr{"#648FFF"}\hlstd{,} \hlkwc{sets.bar.color} \hlstd{=} \hlstr{"#FE6100"}\hlstd{)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/subsetprimates-1}
\begin{kframe}\begin{alltt}
\hlcom{###}
\hlstd{upsetdata}\hlopt{$}\hlstd{primates_dginn_young}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginnyoungtmp}\hlopt{>=}\hlnum{4}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlstd{upsetdata}\hlopt{$}\hlstd{primates_dginn_full}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginnfulltmp}\hlopt{>=}\hlnum{4}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlkwd{upset}\hlstd{(}\hlkwd{na.omit}\hlstd{(upsetdata),} \hlkwc{nsets} \hlstd{=} \hlnum{3}\hlstd{,} \hlkwc{matrix.color} \hlstd{=} \hlstr{"#DC267F"}\hlstd{,}
\hlkwc{main.bar.color} \hlstd{=} \hlstr{"#648FFF"}\hlstd{,} \hlkwc{sets.bar.color} \hlstd{=} \hlstr{"#FE6100"}\hlstd{)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/subsetprimates-2}
\end{knitrout}
\section{Gene List}
Genes under positive selection for at least 4 methods.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{dginnfulltmp}\hlkwb{<-}\hlkwd{rowSums}\hlstd{(}\hlkwd{cbind}\hlstd{(tab}\hlopt{$}\hlstr{'dginn-primate_BUSTED'}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstr{'dginn-primate_BppM1M2'}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstr{'dginn-primate_BppM7M8'}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstr{'dginn-primate_codemlM1M2'}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstr{'dginn-primate_codemlM7M8'}\hlopt{==}\hlstr{"Y"}\hlstd{))}
\hlstd{tab}\hlopt{$}\hlstd{Gene.name[dginnfulltmp}\hlopt{>=}\hlnum{4} \hlopt{&} \hlkwd{is.na}\hlstd{(dginnfulltmp)}\hlopt{==}\hlstd{F]}
\end{alltt}
\begin{verbatim}
## [1] "ACADM" "BCS1L" "BRD4" "CDK5RAP2" "CEP135" "CEP68" "CLIP4" "DNMT1" "DPH5" "EMC1" "FYCO1"
## [12] "GCC2" "GGH" "GHITM" "GIGYF2" "GLA" "GOLGA7" "HECTD1" "IDE" "ITGB1" "LARP1" "LARP4B"
## [23] "LMAN2" "MARK1" "MIPOL1" "MPHOSPH10" "MYCBP2" "NDUFAF2" "NDUFB9" "PCNT" "POLA1" "PRIM2" "PRKAR2A"
## [34] "PVR" "REEP6" "RIPK1" "SAAL1" "SEPSECS" "SIRT5" "SLC25A21" "SLC27A2" "TMEM39B" "TOR1AIP1" "TUBGCP2"
## [45] "UBAP2" "UGGT2" "VPS39" "ZNF318"
\end{verbatim}
\begin{alltt}
\hlstd{tab}\hlopt{$}\hlstd{Gene.name[dginnfulltmp}\hlopt{>=}\hlnum{3} \hlopt{&} \hlkwd{is.na}\hlstd{(dginnfulltmp)}\hlopt{==}\hlstd{F]}
\end{alltt}
\begin{verbatim}
## [1] "ACADM" "ADAM9" "AP2A2" "ATE1" "BCS1L" "BRD4" "BZW2" "CDK5RAP2" "CEP135" "CEP68" "CLIP4"
## [12] "CNTRL" "DNMT1" "DPH5" "EDEM3" "EIF4E2" "EMC1" "EXOSC2" "FYCO1" "GCC2" "GGH" "GHITM"
## [23] "GIGYF2" "GLA" "GOLGA7" "GOLGB1" "GORASP1" "HDAC2" "HECTD1" "HS6ST2" "IDE" "ITGB1" "LARP1"
## [34] "LARP4B" "LARP7" "LMAN2" "MARK1" "MDN1" "MIPOL1" "MOV10" "MPHOSPH10" "MRPS5" "MYCBP2" "NAT14"
## [45] "NDUFAF2" "NDUFB9" "NGLY1" "NPC2" "PCNT" "PITRM1" "PLAT" "PLOD2" "PMPCB" "POLA1" "POR"
## [56] "PRIM2" "PRKAR2A" "PTBP2" "PVR" "RAB14" "RAB1A" "RAB2A" "RAP1GDS1" "RBX1" "REEP6" "RIPK1"
## [67] "RPL36" "SAAL1" "SCCPDH" "SEPSECS" "SIRT5" "SLC25A21" "SLC27A2" "STOM" "TIMM8B" "TMEM39B" "TOR1AIP1"
## [78] "TRIM59" "TRMT1" "TUBGCP2" "UBAP2" "UGGT2" "USP54" "VPS39" "ZNF318"
\end{verbatim}
\begin{alltt}
\hlstd{tmp}\hlkwb{<-}\hlstd{tab[dginnfulltmp}\hlopt{>=}\hlnum{4} \hlopt{&} \hlkwd{is.na}\hlstd{(dginnfulltmp)}\hlopt{==}\hlstd{F,}
\hlkwd{c}\hlstd{(}\hlstr{"Gene.name"}\hlstd{,}\hlstr{"dginn-primate_BUSTED"}\hlstd{,} \hlstr{"dginn-primate_BppM1M2"}\hlstd{,}
\hlstr{"dginn-primate_BppM7M8"}\hlstd{,}\hlstr{"dginn-primate_codemlM1M2"}\hlstd{,}\hlstr{"dginn-primate_codemlM7M8"}\hlstd{)]}
\hlkwd{write.table}\hlstd{(tmp,} \hlstr{"geneList_DGINN_full_primate_pos4.txt"}\hlstd{,} \hlkwc{row.names}\hlstd{=F,} \hlkwc{quote}\hlstd{=F)}
\end{alltt}
\end{kframe}
\end{knitrout}
\section{Shiny like}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{makeFig1} \hlkwb{<-} \hlkwa{function}\hlstd{(}\hlkwc{df}\hlstd{)\{}
\hlcom{# prepare data for colors etc}
\hlstd{colMethods} \hlkwb{<-} \hlkwd{c}\hlstd{(}\hlstr{"deepskyblue4"}\hlstd{,} \hlstr{"darkorange"} \hlstd{,} \hlstr{"deepskyblue3"} \hlstd{,} \hlstr{"mediumseagreen"} \hlstd{,} \hlstr{"yellow3"} \hlstd{,} \hlstr{"black"}\hlstd{)}
\hlstd{nameMethods} \hlkwb{<-} \hlkwd{c}\hlstd{(}\hlstr{"BUSTED"}\hlstd{,} \hlstr{"BppM1M2"}\hlstd{,} \hlstr{"BppM7M8"}\hlstd{,} \hlstr{"codemlM1M2"}\hlstd{,} \hlstr{"codemlM7M8"}\hlstd{,} \hlstr{"MEME"}\hlstd{)}
\hlstd{metColor} \hlkwb{<-} \hlkwd{data.frame}\hlstd{(}\hlkwc{Name} \hlstd{= nameMethods ,} \hlkwc{Col} \hlstd{= colMethods ,} \hlkwc{stringsAsFactors} \hlstd{=} \hlnum{FALSE}\hlstd{)}
\hlcom{# subset for this specific figure}
\hlcom{#df <- df[df$nbY >= 1, ] # to drop genes found by 0 methods (big datasets)}
\hlstd{xt} \hlkwb{<-} \hlstd{df[,} \hlkwd{c}\hlstd{(}\hlstr{"BUSTED"}\hlstd{,} \hlstr{"BppM1M2"}\hlstd{,} \hlstr{"BppM7M8"}\hlstd{,} \hlstr{"codemlM1M2"}\hlstd{,} \hlstr{"codemlM7M8"}\hlstd{)]}
\hlstd{xt}\hlopt{$}\hlstd{Gene} \hlkwb{<-} \hlstd{df}\hlopt{$}\hlstd{Gene}
\hlstd{nbrMeth} \hlkwb{<-} \hlnum{5}
\hlcom{# reverse order of dataframe so that genes with the most Y are at the bottom (to be on top of the barplot)}
\hlstd{xt[,}\hlnum{1}\hlopt{:}\hlnum{5}\hlstd{]} \hlkwb{<-} \hlkwd{ifelse}\hlstd{(xt[,}\hlnum{1}\hlopt{:}\hlnum{5}\hlstd{]} \hlopt{==} \hlstr{"Y"}\hlstd{,} \hlnum{1}\hlstd{,} \hlnum{0}\hlstd{)}
\hlcom{# sort and Filter the 0 lines}
\hlstd{xt}\hlkwb{<-}\hlstd{xt[}\hlkwd{order}\hlstd{(}\hlkwd{rowSums}\hlstd{(xt[,}\hlnum{1}\hlopt{:}\hlnum{5}\hlstd{])),]}
\hlstd{xt}\hlkwb{<-}\hlstd{xt[}\hlkwd{rowSums}\hlstd{(xt[,}\hlnum{1}\hlopt{:}\hlnum{5}\hlstd{])}\hlopt{>}\hlnum{2}\hlstd{,]}
\hlkwd{row.names}\hlstd{(xt)}\hlkwb{<-}\hlstd{xt}\hlopt{$}\hlstd{Gene}
\hlstd{xt}\hlkwb{<-}\hlstd{xt[,}\hlnum{1}\hlopt{:}\hlnum{5}\hlstd{]}
\hlstd{colFig1} \hlkwb{<-} \hlstd{metColor[}\hlkwd{which}\hlstd{(metColor}\hlopt{$}\hlstd{Name} \hlopt{%in%} \hlkwd{colnames}\hlstd{(xt)) , ]}
\hlcom{##### PART 1 : NUMBER OF METHODS}
\hlkwd{par}\hlstd{(}\hlkwc{xpd} \hlstd{=} \hlnum{NA} \hlstd{,} \hlkwc{mar}\hlstd{=}\hlkwd{c}\hlstd{(}\hlnum{2}\hlstd{,}\hlnum{7}\hlstd{,}\hlnum{4}\hlstd{,}\hlnum{0}\hlstd{) ,} \hlkwc{oma} \hlstd{=} \hlkwd{c}\hlstd{(}\hlnum{0}\hlstd{,}\hlnum{0}\hlstd{,}\hlnum{0}\hlstd{,}\hlnum{0}\hlstd{) ,} \hlkwc{mgp} \hlstd{=} \hlkwd{c}\hlstd{(}\hlnum{3}\hlstd{,}\hlnum{0.3}\hlstd{,}\hlnum{0}\hlstd{))}
\hlstd{h} \hlkwb{=} \hlkwd{barplot}\hlstd{(}
\hlkwd{t}\hlstd{(xt),}
\hlkwc{border} \hlstd{=} \hlnum{NA} \hlstd{,}
\hlkwc{axes} \hlstd{= F ,}
\hlkwc{col} \hlstd{=} \hlkwd{adjustcolor}\hlstd{(colFig1}\hlopt{$}\hlstd{Col,} \hlkwc{alpha.f} \hlstd{=} \hlnum{1}\hlstd{),}
\hlkwc{horiz} \hlstd{= T ,}
\hlkwc{las} \hlstd{=} \hlnum{2} \hlstd{,}
\hlkwc{main} \hlstd{=} \hlstr{"Methods detecting positive selection"} \hlstd{,}
\hlkwc{cex.main} \hlstd{=} \hlnum{0.85}\hlstd{,}
\hlkwc{cex.names} \hlstd{=} \hlkwd{min}\hlstd{(}\hlnum{50}\hlopt{/}\hlkwd{nrow}\hlstd{(xt),} \hlnum{1.5}\hlstd{)}
\hlstd{)}
\hlkwd{axis}\hlstd{(}\hlnum{3}\hlstd{,} \hlkwc{line} \hlstd{=} \hlnum{0}\hlstd{,} \hlkwc{at} \hlstd{=} \hlkwd{c}\hlstd{(}\hlnum{0}\hlopt{:}\hlstd{nbrMeth),} \hlkwc{label} \hlstd{=} \hlkwd{c}\hlstd{(}\hlstr{"0"}\hlstd{,} \hlkwd{rep}\hlstd{(}\hlstr{""}\hlstd{, nbrMeth} \hlopt{-}\hlnum{1}\hlstd{), nbrMeth),} \hlkwc{tck} \hlstd{=} \hlnum{0.02}\hlstd{)}
\hlkwd{legend}\hlstd{(}\hlstr{"bottomleft"}\hlstd{,}
\hlkwc{horiz} \hlstd{= T,}
\hlkwc{border} \hlstd{= colFig1}\hlopt{$}\hlstd{Col,}
\hlkwc{legend} \hlstd{= colFig1}\hlopt{$}\hlstd{Name,}
\hlkwc{fill} \hlstd{= colFig1}\hlopt{$}\hlstd{Col,}
\hlkwc{cex} \hlstd{=} \hlnum{0.8}\hlstd{,}
\hlkwc{bty} \hlstd{=} \hlstr{"n"}\hlstd{,}
\hlkwc{xpd} \hlstd{=} \hlnum{NA}
\hlstd{)}
\hlstd{\}}
\hlstd{df}\hlkwb{<-}\hlkwd{read.delim}\hlstd{(}\hlkwd{paste0}\hlstd{(workdir,}
\hlstr{"/data/DGINN_202005281649summary_cleaned.csv"}\hlstd{),}
\hlkwc{fill}\hlstd{=T,} \hlkwc{h}\hlstd{=T,} \hlkwc{sep}\hlstd{=}\hlstr{","}\hlstd{)}
\hlkwd{makeFig1}\hlstd{(df)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/shiny-1}
\end{knitrout}
\end{document}
covid_comp_primate.Rnw deleted 100644 → 0
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\documentclass[11pt, oneside]{article} % use "amsart" instead of "article" for AMSLaTeX format
%\usepackage{geometry} % See geometry.pdf to learn the layout options. There are lots.
%\geometry{letterpaper} % ... or a4paper or a5paper or ...
%\geometry{landscape} % Activate for for rotated page geometry
%\usepackage[parfill]{parskip} % Activate to begin paragraphs with an empty line rather than an indent
%\usepackage{graphicx} % Use pdf, png, jpg, or eps with pdflatex; use eps in DVI mode
% TeX will automatically convert eps --> pdf in pdflatex
%\usepackage{amssymb}
\usepackage[utf8]{inputenc}
%\usepackage[cyr]{aeguill}
%\usepackage[francais]{babel}
%\usepackage{hyperref}
\title{Positive selection on genes interacting with SARS-Cov2, comparison of different analysis}
\author{Marie Cariou}
\date{October 2020} % Activate to display a given date or no date
\begin{document}
\maketitle
\tableofcontents
\newpage
\section{Data}
Analysis were formatted by the script covid\_comp\_script0\_table.Rnw.
<<>>=
home<-"/home/adminmarie/Documents/"
workdir<-paste0(home, "CIRI_BIBS_projects/2020_05_Etienne_covid/")
tab<-read.delim(paste0(workdir,
"covid_comp/covid_comp_complete.txt"), h=T, sep="\t")
dim(tab)
tab$Gene.name<-as.character(tab$Gene.name.x)
tab$Gene.name[tab$PreyGene=="MTARC1"]<-"MTARC1"
@
\section{Comparisons Primates}
\subsection{Janet Young's results (Young-primate) VS DGINN-full's results}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "omega" dans la sortie de dginn.
<<omegaM7M8_1>>=
tab$dginn.primate_omegaM0Bpp[tab$dginn.primate_omegaM0Bpp=="na"]<-NA
tab$dginn.primate_omegaM0Bpp<-as.numeric(as.character(
tab$dginn.primate_omegaM0Bpp))
plot(tab$whole.gene.dN.dS.model.0,
tab$dginn.primate_omegaM0Bpp,
xlab="Omega Young-primate",
ylab="DGINN-full's",
cex=0.3)
abline(0,1)
abline(lm(tab$dginn.primate_omegaM0Bpp~tab$whole.gene.dN.dS.model.0),
col="red")
outlier<-tab[tab$whole.gene.dN.dS.model.0<0.4 &
tab$dginn.primate_omegaM0Bpp>0.5,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=outlier$dginn.primate_omegaM0Bpp,
outlier$Gene.name)
outlier<-tab[tab$whole.gene.dN.dS.model.0<0.1 &
tab$dginn.primate_omegaM0Bpp>0.3,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=outlier$dginn.primate_omegaM0Bpp,
outlier$Gene.name)
outlier<-tab[tab$whole.gene.dN.dS.model.0>0.33 &
tab$dginn.primate_omegaM0Bpp<0.2,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=outlier$dginn.primate_omegaM0Bpp,
outlier$Gene.name)
outlier<-tab[tab$whole.gene.dN.dS.model.0>0.6 &
tab$dginn.primate_omegaM0Bpp<0.6,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=outlier$dginn.primate_omegaM0Bpp,
outlier$Gene.name)
@
\subsection{Janet Young's results (Young-primate) VS Cooper's result}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "cooper.primates.Average\_dNdS".
<<omegaM7M8_2>>=
tab$cooper.primates.Average_dNdS<-as.numeric(as.character(
tab$cooper.primates.Average_dNdS))
plot(tab$whole.gene.dN.dS.model.0,
tab$cooper.primates.Average_dNdS,
xlab="Omega Young-primate",
ylab="Omega Cooper-primate",
cex=0.3)
abline(0,1)
abline(lm(tab$cooper.primates.Average_dNdS~tab$whole.gene.dN.dS.model.0),
col="red")
outlier<-tab[tab$whole.gene.dN.dS.model.0<0.15 &
tab$cooper.primates.Average_dNdS>0.4,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=outlier$cooper.primates.Average_dNdS,
outlier$Gene.name, cex=0.5)
outlier<-tab[tab$whole.gene.dN.dS.model.0<0.3 &
tab$cooper.primates.Average_dNdS>0.5,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=outlier$cooper.primates.Average_dNdS,
outlier$Gene.name, cex=0.5)
outlier<-tab[tab$whole.gene.dN.dS.model.0>0.3 &
tab$cooper.primates.Average_dNdS<0.1,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=outlier$cooper.primates.Average_dNdS,
outlier$Gene.name, cex=0.5)
@
\subsection{Cooper's results (Cooper-primate) VS DGINN-full's results}
Comparaison des Omega: colonne "cooper.primates.Average\_dNdS" VS colonne "omega" dans la sortie de dginn.
<<omegaM7M8_3>>=
plot(tab$cooper.primates.Average_dNd,
tab$dginn.primate_omegaM0Bpp,
xlab="Omega Cooper-primate",
ylab="DGINN-full's",
cex=0.3)
abline(0,1)
abline(lm(tab$dginn.primate_omegaM0Bpp~tab$cooper.primates.Average_dNd), col="red")
outlier<-tab[tab$cooper.primates.Average_dNd<0.4 &
tab$dginn.primate_omegaM0Bpp>0.5,]
text(x=outlier$cooper.primates.Average_dNd,
y=outlier$dginn.primate_omegaM0Bpp,
outlier$Gene.name, cex=0.5)
outlier<-tab[tab$cooper.primates.Average_dNd<0.1 &
tab$dginn.primate_omegaM0Bpp>0.3,]
text(x=outlier$cooper.primates.Average_dNd,
y=outlier$dginn.primate_omegaM0Bpp,
outlier$Gene.name, cex=0.5)
outlier<-tab[tab$cooper.primates.Average_dNd>0.7 &
tab$dginn.primate_omegaM0Bpp<0.3,]
text(x=outlier$cooper.primates.Average_dNd,
y=outlier$dginn.primate_omegaM0Bpp,
outlier$Gene.name, cex=0.5)
outlier<-tab[tab$cooper.primates.Average_dNd>0.45 &
tab$dginn.primate_omegaM0Bpp<0.2,]
text(x=outlier$cooper.primates.Average_dNd,
y=outlier$dginn.primate_omegaM0Bpp,
outlier$Gene.name, cex=0.5)
@
\section{Overlap}
\subsection{Mondrian}
<<mondrianprimates>>=
library(Mondrian)
monddata<-as.data.frame(tab$Gene.name)
dim(monddata)
dginnfulltmp<-rowSums(cbind(tab$dginn.primate_BUSTED=="Y",
tab$dginn.primate_BppM1M2=="Y",
tab$dginn.primate_BppM7M8=="Y",
tab$dginn.primate_codemlM1M2=="Y",
tab$dginn.primate_codemlM7M8=="Y"))
monddata$primates_young<-ifelse(
tab$pVal.M8vsM7<0.05, 1, 0)
monddata$primate_cooper<-ifelse(
tab$cooper.primates.M7.M8_p_value<0.05, 1, 0)
monddata$primates_dginn_full<-ifelse(
dginnfulltmp>=3, 1,0)
mondrian(na.omit(monddata[,2:4]),
labels=c("Young", "Cooper", "DGINN-full >=3" ))
monddata$primates_dginn_full<-ifelse(
dginnfulltmp>=4, 1,0)
mondrian(na.omit(monddata[,2:4]),
labels=c("Young", "Cooper", "DGINN-full >=4"))
@
\subsection{subsetR}
Just another representation of the same result.
<<subsetprimates>>=
library(UpSetR)
upsetdata<-as.data.frame(tab$Gene.name)
upsetdata$primates_young<-ifelse(tab$pVal.M8vsM7<0.05, 1, 0)
upsetdata$primate_cooper<-ifelse(
tab$cooper.primates.M7.M8_p_value<0.05, 1, 0)
upsetdata$primates_dginn_full<-ifelse(dginnfulltmp>=3, 1,0)
upset(na.omit(upsetdata), nsets = 3, matrix.color = "#DC267F",
main.bar.color = "#648FFF", sets.bar.color = "#FE6100")
###
upsetdata$primates_dginn_full<-ifelse(dginnfulltmp>=4, 1,0)
upset(na.omit(upsetdata), nsets = 3, matrix.color = "#DC267F",
main.bar.color = "#648FFF", sets.bar.color = "#FE6100")
@
\section{Gene List}
Genes under positive selection for at least 4 methods.
<<>>=
dginnfulltmp<-rowSums(cbind(tab$dginn.primate_BUSTED=="Y",
tab$dginn.primate_BppM1M2=="Y",
tab$dginn.primate_BppM7M8=="Y",
tab$dginn.primate_codemlM1M2=="Y",
tab$dginn.primate_codemlM7M8=="Y"))
tab$Gene.name[dginnfulltmp>=4 & is.na(dginnfulltmp)==F]
tab$Gene.name[dginnfulltmp>=3 & is.na(dginnfulltmp)==F]
tmp<-tab[dginnfulltmp>=4 & is.na(dginnfulltmp)==F,
c("Gene.name","dginn.primate_BUSTED", "dginn.primate_BppM1M2",
"dginn.primate_BppM7M8","dginn.primate_codemlM1M2","dginn.primate_codemlM7M8")]
write.table(tmp, "geneList_DGINN_full_primate_pos4.txt", row.names=F, quote=F)
@
\section{Shiny like}
<<shiny, fig.height=11>>=
makeFig1 <- function(df){
# prepare data for colors etc
colMethods <- c("deepskyblue4", "darkorange" , "deepskyblue3" , "mediumseagreen" , "yellow3" , "black")
nameMethods <- c("BUSTED", "BppM1M2", "BppM7M8", "codemlM1M2", "codemlM7M8", "MEME")
metColor <- data.frame(Name = nameMethods , Col = colMethods , stringsAsFactors = FALSE)
# subset for this specific figure
#df <- df[df$nbY >= 1, ] # to drop genes found by 0 methods (big datasets)
xt <- df[, c("BUSTED", "BppM1M2", "BppM7M8", "codemlM1M2", "codemlM7M8")]
xt$Gene <- df$Gene
nbrMeth <- 5
# reverse order of dataframe so that genes with the most Y are at the bottom (to be on top of the barplot)
xt[,1:5] <- ifelse(xt[,1:5] == "Y", 1, 0)
# sort and Filter the 0 lines
xt<-xt[order(rowSums(xt[,1:5])),]
xt<-xt[rowSums(xt[,1:5])>2,]
row.names(xt)<-xt$Gene
xt<-xt[,1:5]
colFig1 <- metColor[which(metColor$Name %in% colnames(xt)) , ]
##### PART 1 : NUMBER OF METHODS
par(xpd = NA , mar=c(2,7,4,0) , oma = c(0,0,0,0) , mgp = c(3,0.3,0))
h = barplot(
t(xt),
border = NA ,
axes = F ,
col = adjustcolor(colFig1$Col, alpha.f = 1),
horiz = T ,
las = 2 ,
main = "Methods detecting positive selection" ,
cex.main = 0.85,
cex.names = min(50/nrow(xt), 1.5)
)
axis(3, line = 0, at = c(0:nbrMeth), label = c("0", rep("", nbrMeth -1), nbrMeth), tck = 0.02)
legend("bottomleft",
horiz = T,
border = colFig1$Col,
legend = colFig1$Name,
fill = colFig1$Col,
cex = 0.8,
bty = "n",
xpd = NA
)
}
@
<<>>=
source("covid_comp_shiny.R")
df<-read.delim(paste0(workdir,
"/data/DGINN_202005281649summary_cleaned.csv"),
fill=T, h=T, sep=",")
names(df)
dftmp<-tab[,c("File", "Name", "Gene.name",
"GeneSize", "dginn.primate_NbSpecies", "dginn.primate_omegaM0Bpp",
"dginn.primate_omegaM0codeml", "dginn.primate_BUSTED", "dginn.primate_BUSTED.p.value",
"dginn.primate_MEME.NbSites", "dginn.primate_MEME.PSS", "dginn.primate_BppM1M2",
"dginn.primate_BppM1M2.p.value", "dginn.primate_BppM1M2.NbSites", "dginn.primate_BppM1M2.PSS",
"dginn.primate_BppM7M8", "dginn.primate_BppM7M8.p.value", "dginn.primate_BppM7M8.NbSites",
"dginn.primate_BppM7M8.PSS", "dginn.primate_codemlM1M2", "dginn.primate_codemlM1M2.p.value",
"dginn.primate_codemlM1M2.NbSites","dginn.primate_codemlM1M2.PSS", "dginn.primate_codemlM7M8",
"dginn.primate_codemlM7M8.p.value", "dginn.primate_codemlM7M8.NbSites" , "dginn.primate_codemlM7M8.PSS")]
names(dftmp)<-names(df)
makeFig1(dftmp)
@
\end{document}
covid_comp_primate.pdf deleted 100644 → 0
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covid_comp_primate.tex deleted 100644 → 0
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\documentclass[11pt, oneside]{article}\usepackage[]{graphicx}\usepackage[]{color}
% maxwidth is the original width if it is less than linewidth
% otherwise use linewidth (to make sure the graphics do not exceed the margin)
\makeatletter
\def\maxwidth{ %
\ifdim\Gin@nat@width>\linewidth
\linewidth
\else
\Gin@nat@width
\fi
}
\makeatother
\definecolor{fgcolor}{rgb}{0.345, 0.345, 0.345}
\newcommand{\hlnum}[1]{\textcolor[rgb]{0.686,0.059,0.569}{#1}}%
\newcommand{\hlstr}[1]{\textcolor[rgb]{0.192,0.494,0.8}{#1}}%
\newcommand{\hlcom}[1]{\textcolor[rgb]{0.678,0.584,0.686}{\textit{#1}}}%
\newcommand{\hlopt}[1]{\textcolor[rgb]{0,0,0}{#1}}%
\newcommand{\hlstd}[1]{\textcolor[rgb]{0.345,0.345,0.345}{#1}}%
\newcommand{\hlkwa}[1]{\textcolor[rgb]{0.161,0.373,0.58}{\textbf{#1}}}%
\newcommand{\hlkwb}[1]{\textcolor[rgb]{0.69,0.353,0.396}{#1}}%
\newcommand{\hlkwc}[1]{\textcolor[rgb]{0.333,0.667,0.333}{#1}}%
\newcommand{\hlkwd}[1]{\textcolor[rgb]{0.737,0.353,0.396}{\textbf{#1}}}%
\let\hlipl\hlkwb
\usepackage{framed}
\makeatletter
\newenvironment{kframe}{%
\def\at@end@of@kframe{}%
\ifinner\ifhmode%
\def\at@end@of@kframe{\end{minipage}}%
\begin{minipage}{\columnwidth}%
\fi\fi%
\def\FrameCommand##1{\hskip\@totalleftmargin \hskip-\fboxsep
\colorbox{shadecolor}{##1}\hskip-\fboxsep
% There is no \\@totalrightmargin, so:
\hskip-\linewidth \hskip-\@totalleftmargin \hskip\columnwidth}%
\MakeFramed {\advance\hsize-\width
\@totalleftmargin\z@ \linewidth\hsize
\@setminipage}}%
{\par\unskip\endMakeFramed%
\at@end@of@kframe}
\makeatother
\definecolor{shadecolor}{rgb}{.97, .97, .97}
\definecolor{messagecolor}{rgb}{0, 0, 0}
\definecolor{warningcolor}{rgb}{1, 0, 1}
\definecolor{errorcolor}{rgb}{1, 0, 0}
\newenvironment{knitrout}{}{} % an empty environment to be redefined in TeX
\usepackage{alltt} % use "amsart" instead of "article" for AMSLaTeX format
%\usepackage{geometry} % See geometry.pdf to learn the layout options. There are lots.
%\geometry{letterpaper} % ... or a4paper or a5paper or ...
%\geometry{landscape} % Activate for for rotated page geometry
%\usepackage[parfill]{parskip} % Activate to begin paragraphs with an empty line rather than an indent
%\usepackage{graphicx} % Use pdf, png, jpg, or eps with pdflatex; use eps in DVI mode
% TeX will automatically convert eps --> pdf in pdflatex
%\usepackage{amssymb}
\usepackage[utf8]{inputenc}
%\usepackage[cyr]{aeguill}
%\usepackage[francais]{babel}
%\usepackage{hyperref}
\title{Positive selection on genes interacting with SARS-Cov2, comparison of different analysis}
\author{Marie Cariou}
\date{October 2020} % Activate to display a given date or no date
\IfFileExists{upquote.sty}{\usepackage{upquote}}{}
\begin{document}
\maketitle
\tableofcontents
\newpage
\section{Data}
Analysis were formatted by the script covid\_comp\_script0\_table.Rnw.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{home}\hlkwb{<-}\hlstr{"/home/adminmarie/Documents/"}
\hlstd{workdir}\hlkwb{<-}\hlkwd{paste0}\hlstd{(home,} \hlstr{"CIRI_BIBS_projects/2020_05_Etienne_covid/"}\hlstd{)}
\hlstd{tab}\hlkwb{<-}\hlkwd{read.delim}\hlstd{(}\hlkwd{paste0}\hlstd{(workdir,}
\hlstr{"covid_comp/covid_comp_complete.txt"}\hlstd{),} \hlkwc{h}\hlstd{=T,} \hlkwc{sep}\hlstd{=}\hlstr{"\textbackslash{}t"}\hlstd{)}
\hlkwd{dim}\hlstd{(tab)}
\end{alltt}
\begin{verbatim}
## [1] 332 141
\end{verbatim}
\begin{alltt}
\hlstd{tab}\hlopt{$}\hlstd{Gene.name}\hlkwb{<-}\hlkwd{as.character}\hlstd{(tab}\hlopt{$}\hlstd{Gene.name.x)}
\hlstd{tab}\hlopt{$}\hlstd{Gene.name[tab}\hlopt{$}\hlstd{PreyGene}\hlopt{==}\hlstr{"MTARC1"}\hlstd{]}\hlkwb{<-}\hlstr{"MTARC1"}
\end{alltt}
\end{kframe}
\end{knitrout}
\section{Comparisons Primates}
\subsection{Janet Young's results (Young-primate) VS DGINN-full's results}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "omega" dans la sortie de dginn.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp[tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp}\hlopt{==}\hlstr{"na"}\hlstd{]}\hlkwb{<-}\hlnum{NA}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp}\hlkwb{<-}\hlkwd{as.numeric}\hlstd{(}\hlkwd{as.character}\hlstd{(}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp))}
\hlkwd{plot}\hlstd{(tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0,}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp,}
\hlkwc{xlab}\hlstd{=}\hlstr{"Omega Young-primate"}\hlstd{,}
\hlkwc{ylab}\hlstd{=}\hlstr{"DGINN-full's"}\hlstd{,}
\hlkwc{cex}\hlstd{=}\hlnum{0.3}\hlstd{)}
\hlkwd{abline}\hlstd{(}\hlnum{0}\hlstd{,}\hlnum{1}\hlstd{)}
\hlkwd{abline}\hlstd{(}\hlkwd{lm}\hlstd{(tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp}\hlopt{~}\hlstd{tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0),}
\hlkwc{col}\hlstd{=}\hlstr{"red"}\hlstd{)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0}\hlopt{<}\hlnum{0.4} \hlopt{&}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp}\hlopt{>}\hlnum{0.5}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0,}
\hlkwc{y}\hlstd{=outlier}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp,}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0}\hlopt{<}\hlnum{0.1} \hlopt{&}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp}\hlopt{>}\hlnum{0.3}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0,}
\hlkwc{y}\hlstd{=outlier}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp,}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0}\hlopt{>}\hlnum{0.33} \hlopt{&}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp}\hlopt{<}\hlnum{0.2}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0,}
\hlkwc{y}\hlstd{=outlier}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp,}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0}\hlopt{>}\hlnum{0.6} \hlopt{&}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp}\hlopt{<}\hlnum{0.6}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0,}
\hlkwc{y}\hlstd{=outlier}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp,}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/omegaM7M8_1-1}
\end{knitrout}
\subsection{Janet Young's results (Young-primate) VS Cooper's result}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "cooper.primates.Average\_dNdS".
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{tab}\hlopt{$}\hlstd{cooper.primates.Average_dNdS}\hlkwb{<-}\hlkwd{as.numeric}\hlstd{(}\hlkwd{as.character}\hlstd{(}
\hlstd{tab}\hlopt{$}\hlstd{cooper.primates.Average_dNdS))}
\hlkwd{plot}\hlstd{(tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0,}
\hlstd{tab}\hlopt{$}\hlstd{cooper.primates.Average_dNdS,}
\hlkwc{xlab}\hlstd{=}\hlstr{"Omega Young-primate"}\hlstd{,}
\hlkwc{ylab}\hlstd{=}\hlstr{"Omega Cooper-primate"}\hlstd{,}
\hlkwc{cex}\hlstd{=}\hlnum{0.3}\hlstd{)}
\hlkwd{abline}\hlstd{(}\hlnum{0}\hlstd{,}\hlnum{1}\hlstd{)}
\hlkwd{abline}\hlstd{(}\hlkwd{lm}\hlstd{(tab}\hlopt{$}\hlstd{cooper.primates.Average_dNdS}\hlopt{~}\hlstd{tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0),}
\hlkwc{col}\hlstd{=}\hlstr{"red"}\hlstd{)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0}\hlopt{<}\hlnum{0.15} \hlopt{&}
\hlstd{tab}\hlopt{$}\hlstd{cooper.primates.Average_dNdS}\hlopt{>}\hlnum{0.4}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0,}
\hlkwc{y}\hlstd{=outlier}\hlopt{$}\hlstd{cooper.primates.Average_dNdS,}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name,} \hlkwc{cex}\hlstd{=}\hlnum{0.5}\hlstd{)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0}\hlopt{<}\hlnum{0.3} \hlopt{&}
\hlstd{tab}\hlopt{$}\hlstd{cooper.primates.Average_dNdS}\hlopt{>}\hlnum{0.5}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0,}
\hlkwc{y}\hlstd{=outlier}\hlopt{$}\hlstd{cooper.primates.Average_dNdS,}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name,} \hlkwc{cex}\hlstd{=}\hlnum{0.5}\hlstd{)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0}\hlopt{>}\hlnum{0.3} \hlopt{&}
\hlstd{tab}\hlopt{$}\hlstd{cooper.primates.Average_dNdS}\hlopt{<}\hlnum{0.1}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{whole.gene.dN.dS.model.0,}
\hlkwc{y}\hlstd{=outlier}\hlopt{$}\hlstd{cooper.primates.Average_dNdS,}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name,} \hlkwc{cex}\hlstd{=}\hlnum{0.5}\hlstd{)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/omegaM7M8_2-1}
\end{knitrout}
\subsection{Cooper's results (Cooper-primate) VS DGINN-full's results}
Comparaison des Omega: colonne "cooper.primates.Average\_dNdS" VS colonne "omega" dans la sortie de dginn.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{plot}\hlstd{(tab}\hlopt{$}\hlstd{cooper.primates.Average_dNd,}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp,}
\hlkwc{xlab}\hlstd{=}\hlstr{"Omega Cooper-primate"}\hlstd{,}
\hlkwc{ylab}\hlstd{=}\hlstr{"DGINN-full's"}\hlstd{,}
\hlkwc{cex}\hlstd{=}\hlnum{0.3}\hlstd{)}
\hlkwd{abline}\hlstd{(}\hlnum{0}\hlstd{,}\hlnum{1}\hlstd{)}
\hlkwd{abline}\hlstd{(}\hlkwd{lm}\hlstd{(tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp}\hlopt{~}\hlstd{tab}\hlopt{$}\hlstd{cooper.primates.Average_dNd),} \hlkwc{col}\hlstd{=}\hlstr{"red"}\hlstd{)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{cooper.primates.Average_dNd}\hlopt{<}\hlnum{0.4} \hlopt{&}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp}\hlopt{>}\hlnum{0.5}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{cooper.primates.Average_dNd,}
\hlkwc{y}\hlstd{=outlier}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp,}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name,} \hlkwc{cex}\hlstd{=}\hlnum{0.5}\hlstd{)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{cooper.primates.Average_dNd}\hlopt{<}\hlnum{0.1} \hlopt{&}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp}\hlopt{>}\hlnum{0.3}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{cooper.primates.Average_dNd,}
\hlkwc{y}\hlstd{=outlier}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp,}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name,} \hlkwc{cex}\hlstd{=}\hlnum{0.5}\hlstd{)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{cooper.primates.Average_dNd}\hlopt{>}\hlnum{0.7} \hlopt{&}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp}\hlopt{<}\hlnum{0.3}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{cooper.primates.Average_dNd,}
\hlkwc{y}\hlstd{=outlier}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp,}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name,} \hlkwc{cex}\hlstd{=}\hlnum{0.5}\hlstd{)}
\hlstd{outlier}\hlkwb{<-}\hlstd{tab[tab}\hlopt{$}\hlstd{cooper.primates.Average_dNd}\hlopt{>}\hlnum{0.45} \hlopt{&}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp}\hlopt{<}\hlnum{0.2}\hlstd{,]}
\hlkwd{text}\hlstd{(}\hlkwc{x}\hlstd{=outlier}\hlopt{$}\hlstd{cooper.primates.Average_dNd,}
\hlkwc{y}\hlstd{=outlier}\hlopt{$}\hlstd{dginn.primate_omegaM0Bpp,}
\hlstd{outlier}\hlopt{$}\hlstd{Gene.name,} \hlkwc{cex}\hlstd{=}\hlnum{0.5}\hlstd{)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/omegaM7M8_3-1}
\end{knitrout}
\section{Overlap}
\subsection{Mondrian}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{library}\hlstd{(Mondrian)}
\hlstd{monddata}\hlkwb{<-}\hlkwd{as.data.frame}\hlstd{(tab}\hlopt{$}\hlstd{Gene.name)}
\hlkwd{dim}\hlstd{(monddata)}
\end{alltt}
\begin{verbatim}
## [1] 332 1
\end{verbatim}
\begin{alltt}
\hlstd{dginnfulltmp}\hlkwb{<-}\hlkwd{rowSums}\hlstd{(}\hlkwd{cbind}\hlstd{(tab}\hlopt{$}\hlstd{dginn.primate_BUSTED}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_BppM1M2}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_BppM7M8}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_codemlM1M2}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_codemlM7M8}\hlopt{==}\hlstr{"Y"}\hlstd{))}
\hlstd{monddata}\hlopt{$}\hlstd{primates_young}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(}
\hlstd{tab}\hlopt{$}\hlstd{pVal.M8vsM7}\hlopt{<}\hlnum{0.05}\hlstd{,} \hlnum{1}\hlstd{,} \hlnum{0}\hlstd{)}
\hlstd{monddata}\hlopt{$}\hlstd{primate_cooper}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(}
\hlstd{tab}\hlopt{$}\hlstd{cooper.primates.M7.M8_p_value}\hlopt{<}\hlnum{0.05}\hlstd{,} \hlnum{1}\hlstd{,} \hlnum{0}\hlstd{)}
\hlstd{monddata}\hlopt{$}\hlstd{primates_dginn_full}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(}
\hlstd{dginnfulltmp}\hlopt{>=}\hlnum{3}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlkwd{mondrian}\hlstd{(}\hlkwd{na.omit}\hlstd{(monddata[,}\hlnum{2}\hlopt{:}\hlnum{4}\hlstd{]),}
\hlkwc{labels}\hlstd{=}\hlkwd{c}\hlstd{(}\hlstr{"Young"}\hlstd{,} \hlstr{"Cooper"}\hlstd{,} \hlstr{"DGINN-full >=3"} \hlstd{))}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/mondrianprimates-1}
\begin{kframe}\begin{alltt}
\hlstd{monddata}\hlopt{$}\hlstd{primates_dginn_full}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(}
\hlstd{dginnfulltmp}\hlopt{>=}\hlnum{4}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlkwd{mondrian}\hlstd{(}\hlkwd{na.omit}\hlstd{(monddata[,}\hlnum{2}\hlopt{:}\hlnum{4}\hlstd{]),}
\hlkwc{labels}\hlstd{=}\hlkwd{c}\hlstd{(}\hlstr{"Young"}\hlstd{,} \hlstr{"Cooper"}\hlstd{,} \hlstr{"DGINN-full >=4"}\hlstd{))}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/mondrianprimates-2}
\end{knitrout}
\subsection{subsetR}
Just another representation of the same result.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{library}\hlstd{(UpSetR)}
\hlstd{upsetdata}\hlkwb{<-}\hlkwd{as.data.frame}\hlstd{(tab}\hlopt{$}\hlstd{Gene.name)}
\hlstd{upsetdata}\hlopt{$}\hlstd{primates_young}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(tab}\hlopt{$}\hlstd{pVal.M8vsM7}\hlopt{<}\hlnum{0.05}\hlstd{,} \hlnum{1}\hlstd{,} \hlnum{0}\hlstd{)}
\hlstd{upsetdata}\hlopt{$}\hlstd{primate_cooper}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(}
\hlstd{tab}\hlopt{$}\hlstd{cooper.primates.M7.M8_p_value}\hlopt{<}\hlnum{0.05}\hlstd{,} \hlnum{1}\hlstd{,} \hlnum{0}\hlstd{)}
\hlstd{upsetdata}\hlopt{$}\hlstd{primates_dginn_full}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginnfulltmp}\hlopt{>=}\hlnum{3}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlkwd{upset}\hlstd{(}\hlkwd{na.omit}\hlstd{(upsetdata),} \hlkwc{nsets} \hlstd{=} \hlnum{3}\hlstd{,} \hlkwc{matrix.color} \hlstd{=} \hlstr{"#DC267F"}\hlstd{,}
\hlkwc{main.bar.color} \hlstd{=} \hlstr{"#648FFF"}\hlstd{,} \hlkwc{sets.bar.color} \hlstd{=} \hlstr{"#FE6100"}\hlstd{)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/subsetprimates-1}
\begin{kframe}\begin{alltt}
\hlcom{###}
\hlstd{upsetdata}\hlopt{$}\hlstd{primates_dginn_full}\hlkwb{<-}\hlkwd{ifelse}\hlstd{(dginnfulltmp}\hlopt{>=}\hlnum{4}\hlstd{,} \hlnum{1}\hlstd{,}\hlnum{0}\hlstd{)}
\hlkwd{upset}\hlstd{(}\hlkwd{na.omit}\hlstd{(upsetdata),} \hlkwc{nsets} \hlstd{=} \hlnum{3}\hlstd{,} \hlkwc{matrix.color} \hlstd{=} \hlstr{"#DC267F"}\hlstd{,}
\hlkwc{main.bar.color} \hlstd{=} \hlstr{"#648FFF"}\hlstd{,} \hlkwc{sets.bar.color} \hlstd{=} \hlstr{"#FE6100"}\hlstd{)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/subsetprimates-2}
\end{knitrout}
\section{Gene List}
Genes under positive selection for at least 4 methods.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{dginnfulltmp}\hlkwb{<-}\hlkwd{rowSums}\hlstd{(}\hlkwd{cbind}\hlstd{(tab}\hlopt{$}\hlstd{dginn.primate_BUSTED}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_BppM1M2}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_BppM7M8}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_codemlM1M2}\hlopt{==}\hlstr{"Y"}\hlstd{,}
\hlstd{tab}\hlopt{$}\hlstd{dginn.primate_codemlM7M8}\hlopt{==}\hlstr{"Y"}\hlstd{))}
\hlstd{tab}\hlopt{$}\hlstd{Gene.name[dginnfulltmp}\hlopt{>=}\hlnum{4} \hlopt{&} \hlkwd{is.na}\hlstd{(dginnfulltmp)}\hlopt{==}\hlstd{F]}
\end{alltt}
\begin{verbatim}
## [1] "ACADM" "BCS1L" "BRD4" "CDK5RAP2" "CEP135"
## [6] "CEP68" "CLIP4" "DNMT1" "DPH5" "EMC1"
## [11] "ERO1LB" "FYCO1" "GCC2" "GGH" "GHITM"
## [16] "GIGYF2" "GLA" "GOLGA7" "HECTD1" "IDE"
## [21] "ITGB1" "LARP1" "LARP4B" "LMAN2" "MARK1"
## [26] "MIPOL1" "MPHOSPH10" "MYCBP2" "NDUFAF2" "NDUFB9"
## [31] "NUPL1" "PCNT" "POLA1" "PRIM2" "PRKAR2A"
## [36] "PVR" "REEP6" "RIPK1" "SAAL1" "SEPSECS"
## [41] "SIRT5" "SLC25A21" "SLC27A2" "TMEM39B" "TOR1AIP1"
## [46] "TUBGCP2" "UBAP2" "UGGT2" "VPS39" "ZNF318"
\end{verbatim}
\begin{alltt}
\hlstd{tab}\hlopt{$}\hlstd{Gene.name[dginnfulltmp}\hlopt{>=}\hlnum{3} \hlopt{&} \hlkwd{is.na}\hlstd{(dginnfulltmp)}\hlopt{==}\hlstd{F]}
\end{alltt}
\begin{verbatim}
## [1] "ACADM" "ADAM9" "AP2A2" "ATE1" "BCS1L"
## [6] "BRD4" "BZW2" "CDK5RAP2" "CEP135" "CEP68"
## [11] "CLIP4" "CNTRL" "DNMT1" "DPH5" "EDEM3"
## [16] "EIF4E2" "EMC1" "ERO1LB" "EXOSC2" "FYCO1"
## [21] "GCC2" "GGH" "GHITM" "GIGYF2" "GLA"
## [26] "GOLGA7" "GOLGB1" "GORASP1" "HDAC2" "HECTD1"
## [31] "HS6ST2" "IDE" "ITGB1" "LARP1" "LARP4B"
## [36] "LARP7" "LMAN2" "MARK1" "MDN1" "MIPOL1"
## [41] "MOV10" "MPHOSPH10" "MRPS5" "MYCBP2" "NAT14"
## [46] "NDUFAF2" "NDUFB9" "NGLY1" "NPC2" "NUPL1"
## [51] "PCNT" "PITRM1" "PLAT" "PLOD2" "PMPCB"
## [56] "POLA1" "POR" "PRIM2" "PRKAR2A" "PTBP2"
## [61] "PVR" "RAB14" "RAB1A" "RAB2A" "RAP1GDS1"
## [66] "RBX1" "REEP6" "RIPK1" "RPL36" "SAAL1"
## [71] "SCCPDH" "SEPSECS" "SIRT5" "SLC25A21" "SLC27A2"
## [76] "STOM" "TIMM8B" "TMEM39B" "TOR1AIP1" "TRIM59"
## [81] "TRMT1" "TUBGCP2" "UBAP2" "UGGT2" "USP54"
## [86] "VPS39" "ZNF318"
\end{verbatim}
\begin{alltt}
\hlstd{tmp}\hlkwb{<-}\hlstd{tab[dginnfulltmp}\hlopt{>=}\hlnum{4} \hlopt{&} \hlkwd{is.na}\hlstd{(dginnfulltmp)}\hlopt{==}\hlstd{F,}
\hlkwd{c}\hlstd{(}\hlstr{"Gene.name"}\hlstd{,}\hlstr{"dginn.primate_BUSTED"}\hlstd{,} \hlstr{"dginn.primate_BppM1M2"}\hlstd{,}
\hlstr{"dginn.primate_BppM7M8"}\hlstd{,}\hlstr{"dginn.primate_codemlM1M2"}\hlstd{,}\hlstr{"dginn.primate_codemlM7M8"}\hlstd{)]}
\hlkwd{write.table}\hlstd{(tmp,} \hlstr{"geneList_DGINN_full_primate_pos4.txt"}\hlstd{,} \hlkwc{row.names}\hlstd{=F,} \hlkwc{quote}\hlstd{=F)}
\end{alltt}
\end{kframe}
\end{knitrout}
\section{Shiny like}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{makeFig1} \hlkwb{<-} \hlkwa{function}\hlstd{(}\hlkwc{df}\hlstd{)\{}
\hlcom{# prepare data for colors etc}
\hlstd{colMethods} \hlkwb{<-} \hlkwd{c}\hlstd{(}\hlstr{"deepskyblue4"}\hlstd{,} \hlstr{"darkorange"} \hlstd{,} \hlstr{"deepskyblue3"} \hlstd{,} \hlstr{"mediumseagreen"} \hlstd{,} \hlstr{"yellow3"} \hlstd{,} \hlstr{"black"}\hlstd{)}
\hlstd{nameMethods} \hlkwb{<-} \hlkwd{c}\hlstd{(}\hlstr{"BUSTED"}\hlstd{,} \hlstr{"BppM1M2"}\hlstd{,} \hlstr{"BppM7M8"}\hlstd{,} \hlstr{"codemlM1M2"}\hlstd{,} \hlstr{"codemlM7M8"}\hlstd{,} \hlstr{"MEME"}\hlstd{)}
\hlstd{metColor} \hlkwb{<-} \hlkwd{data.frame}\hlstd{(}\hlkwc{Name} \hlstd{= nameMethods ,} \hlkwc{Col} \hlstd{= colMethods ,} \hlkwc{stringsAsFactors} \hlstd{=} \hlnum{FALSE}\hlstd{)}
\hlcom{# subset for this specific figure}
\hlcom{#df <- df[df$nbY >= 1, ] # to drop genes found by 0 methods (big datasets)}
\hlstd{xt} \hlkwb{<-} \hlstd{df[,} \hlkwd{c}\hlstd{(}\hlstr{"BUSTED"}\hlstd{,} \hlstr{"BppM1M2"}\hlstd{,} \hlstr{"BppM7M8"}\hlstd{,} \hlstr{"codemlM1M2"}\hlstd{,} \hlstr{"codemlM7M8"}\hlstd{)]}
\hlstd{xt}\hlopt{$}\hlstd{Gene} \hlkwb{<-} \hlstd{df}\hlopt{$}\hlstd{Gene}
\hlstd{nbrMeth} \hlkwb{<-} \hlnum{5}
\hlcom{# reverse order of dataframe so that genes with the most Y are at the bottom (to be on top of the barplot)}
\hlstd{xt[,}\hlnum{1}\hlopt{:}\hlnum{5}\hlstd{]} \hlkwb{<-} \hlkwd{ifelse}\hlstd{(xt[,}\hlnum{1}\hlopt{:}\hlnum{5}\hlstd{]} \hlopt{==} \hlstr{"Y"}\hlstd{,} \hlnum{1}\hlstd{,} \hlnum{0}\hlstd{)}
\hlcom{# sort and Filter the 0 lines}
\hlstd{xt}\hlkwb{<-}\hlstd{xt[}\hlkwd{order}\hlstd{(}\hlkwd{rowSums}\hlstd{(xt[,}\hlnum{1}\hlopt{:}\hlnum{5}\hlstd{])),]}
\hlstd{xt}\hlkwb{<-}\hlstd{xt[}\hlkwd{rowSums}\hlstd{(xt[,}\hlnum{1}\hlopt{:}\hlnum{5}\hlstd{])}\hlopt{>}\hlnum{2}\hlstd{,]}
\hlkwd{row.names}\hlstd{(xt)}\hlkwb{<-}\hlstd{xt}\hlopt{$}\hlstd{Gene}
\hlstd{xt}\hlkwb{<-}\hlstd{xt[,}\hlnum{1}\hlopt{:}\hlnum{5}\hlstd{]}
\hlstd{colFig1} \hlkwb{<-} \hlstd{metColor[}\hlkwd{which}\hlstd{(metColor}\hlopt{$}\hlstd{Name} \hlopt{%in%} \hlkwd{colnames}\hlstd{(xt)) , ]}
\hlcom{##### PART 1 : NUMBER OF METHODS}
\hlkwd{par}\hlstd{(}\hlkwc{xpd} \hlstd{=} \hlnum{NA} \hlstd{,} \hlkwc{mar}\hlstd{=}\hlkwd{c}\hlstd{(}\hlnum{2}\hlstd{,}\hlnum{7}\hlstd{,}\hlnum{4}\hlstd{,}\hlnum{0}\hlstd{) ,} \hlkwc{oma} \hlstd{=} \hlkwd{c}\hlstd{(}\hlnum{0}\hlstd{,}\hlnum{0}\hlstd{,}\hlnum{0}\hlstd{,}\hlnum{0}\hlstd{) ,} \hlkwc{mgp} \hlstd{=} \hlkwd{c}\hlstd{(}\hlnum{3}\hlstd{,}\hlnum{0.3}\hlstd{,}\hlnum{0}\hlstd{))}
\hlstd{h} \hlkwb{=} \hlkwd{barplot}\hlstd{(}
\hlkwd{t}\hlstd{(xt),}
\hlkwc{border} \hlstd{=} \hlnum{NA} \hlstd{,}
\hlkwc{axes} \hlstd{= F ,}
\hlkwc{col} \hlstd{=} \hlkwd{adjustcolor}\hlstd{(colFig1}\hlopt{$}\hlstd{Col,} \hlkwc{alpha.f} \hlstd{=} \hlnum{1}\hlstd{),}
\hlkwc{horiz} \hlstd{= T ,}
\hlkwc{las} \hlstd{=} \hlnum{2} \hlstd{,}
\hlkwc{main} \hlstd{=} \hlstr{"Methods detecting positive selection"} \hlstd{,}
\hlkwc{cex.main} \hlstd{=} \hlnum{0.85}\hlstd{,}
\hlkwc{cex.names} \hlstd{=} \hlkwd{min}\hlstd{(}\hlnum{50}\hlopt{/}\hlkwd{nrow}\hlstd{(xt),} \hlnum{1.5}\hlstd{)}
\hlstd{)}
\hlkwd{axis}\hlstd{(}\hlnum{3}\hlstd{,} \hlkwc{line} \hlstd{=} \hlnum{0}\hlstd{,} \hlkwc{at} \hlstd{=} \hlkwd{c}\hlstd{(}\hlnum{0}\hlopt{:}\hlstd{nbrMeth),} \hlkwc{label} \hlstd{=} \hlkwd{c}\hlstd{(}\hlstr{"0"}\hlstd{,} \hlkwd{rep}\hlstd{(}\hlstr{""}\hlstd{, nbrMeth} \hlopt{-}\hlnum{1}\hlstd{), nbrMeth),} \hlkwc{tck} \hlstd{=} \hlnum{0.02}\hlstd{)}
\hlkwd{legend}\hlstd{(}\hlstr{"bottomleft"}\hlstd{,}
\hlkwc{horiz} \hlstd{= T,}
\hlkwc{border} \hlstd{= colFig1}\hlopt{$}\hlstd{Col,}
\hlkwc{legend} \hlstd{= colFig1}\hlopt{$}\hlstd{Name,}
\hlkwc{fill} \hlstd{= colFig1}\hlopt{$}\hlstd{Col,}
\hlkwc{cex} \hlstd{=} \hlnum{0.8}\hlstd{,}
\hlkwc{bty} \hlstd{=} \hlstr{"n"}\hlstd{,}
\hlkwc{xpd} \hlstd{=} \hlnum{NA}
\hlstd{)}
\hlstd{\}}
\end{alltt}
\end{kframe}
\end{knitrout}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{source}\hlstd{(}\hlstr{"covid_comp_shiny.R"}\hlstd{)}
\hlstd{df}\hlkwb{<-}\hlkwd{read.delim}\hlstd{(}\hlkwd{paste0}\hlstd{(workdir,}
\hlstr{"/data/DGINN_202005281649summary_cleaned.csv"}\hlstd{),}
\hlkwc{fill}\hlstd{=T,} \hlkwc{h}\hlstd{=T,} \hlkwc{sep}\hlstd{=}\hlstr{","}\hlstd{)}
\hlkwd{names}\hlstd{(df)}
\end{alltt}
\begin{verbatim}
## [1] "File" "Name" "Gene"
## [4] "GeneSize" "NbSpecies" "omegaM0Bpp"
## [7] "omegaM0codeml" "BUSTED" "BUSTED.p.value"
## [10] "MEME.NbSites" "MEME.PSS" "BppM1M2"
## [13] "BppM1M2.p.value" "BppM1M2.NbSites" "BppM1M2.PSS"
## [16] "BppM7M8" "BppM7M8.p.value" "BppM7M8.NbSites"
## [19] "BppM7M8.PSS" "codemlM1M2" "codemlM1M2.p.value"
## [22] "codemlM1M2.NbSites" "codemlM1M2.PSS" "codemlM7M8"
## [25] "codemlM7M8.p.value" "codemlM7M8.NbSites" "codemlM7M8.PSS"
\end{verbatim}
\begin{alltt}
\hlstd{dftmp}\hlkwb{<-}\hlstd{tab[,}\hlkwd{c}\hlstd{(}\hlstr{"File"}\hlstd{,} \hlstr{"Name"}\hlstd{,} \hlstr{"Gene.name"}\hlstd{,}
\hlstr{"GeneSize"}\hlstd{,} \hlstr{"dginn.primate_NbSpecies"}\hlstd{,} \hlstr{"dginn.primate_omegaM0Bpp"}\hlstd{,}
\hlstr{"dginn.primate_omegaM0codeml"}\hlstd{,} \hlstr{"dginn.primate_BUSTED"}\hlstd{,} \hlstr{"dginn.primate_BUSTED.p.value"}\hlstd{,}
\hlstr{"dginn.primate_MEME.NbSites"}\hlstd{,} \hlstr{"dginn.primate_MEME.PSS"}\hlstd{,} \hlstr{"dginn.primate_BppM1M2"}\hlstd{,}
\hlstr{"dginn.primate_BppM1M2.p.value"}\hlstd{,} \hlstr{"dginn.primate_BppM1M2.NbSites"}\hlstd{,} \hlstr{"dginn.primate_BppM1M2.PSS"}\hlstd{,}
\hlstr{"dginn.primate_BppM7M8"}\hlstd{,} \hlstr{"dginn.primate_BppM7M8.p.value"}\hlstd{,} \hlstr{"dginn.primate_BppM7M8.NbSites"}\hlstd{,}
\hlstr{"dginn.primate_BppM7M8.PSS"}\hlstd{,} \hlstr{"dginn.primate_codemlM1M2"}\hlstd{,} \hlstr{"dginn.primate_codemlM1M2.p.value"}\hlstd{,}
\hlstr{"dginn.primate_codemlM1M2.NbSites"}\hlstd{,}\hlstr{"dginn.primate_codemlM1M2.PSS"}\hlstd{,} \hlstr{"dginn.primate_codemlM7M8"}\hlstd{,}
\hlstr{"dginn.primate_codemlM7M8.p.value"}\hlstd{,} \hlstr{"dginn.primate_codemlM7M8.NbSites"} \hlstd{,} \hlstr{"dginn.primate_codemlM7M8.PSS"}\hlstd{)]}
\hlkwd{names}\hlstd{(dftmp)}\hlkwb{<-}\hlkwd{names}\hlstd{(df)}
\hlkwd{makeFig1}\hlstd{(dftmp)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/unnamed-chunk-3-1}
\end{knitrout}
\end{document}
covid_comp_script0_table_backup.Rnw deleted 100644 → 0
View file @ 77f559a1
\documentclass[11pt, oneside]{article} % use "amsart" instead of "article" for AMSLaTeX format
%\usepackage{geometry} % See geometry.pdf to learn the layout options. There are lots.
%\geometry{letterpaper} % ... or a4paper or a5paper or ...
%\geometry{landscape} % Activate for for rotated page geometry
%\usepackage[parfill]{parskip} % Activate to begin paragraphs with an empty line rather than an indent
%\usepackage{graphicx} % Use pdf, png, jpg, or eps with pdflatex; use eps in DVI mode
% TeX will automatically convert eps --> pdf in pdflatex
%\usepackage{amssymb}
\usepackage[utf8]{inputenc}
%\usepackage[cyr]{aeguill}
%\usepackage[francais]{babel}
%\usepackage{hyperref}
\title{Positive selection on genes interacting with SARS-Cov2, comparison of different analysis}
\author{Marie Cariou}
\date{Janvier 2021} % Activate to display a given date or no date
\begin{document}
\maketitle
\tableofcontents
\newpage
\section{Files manipulations}
\subsection{Read Janet Young's table}
<<>>=
workdir<-"/home/adminmarie/Documents/CIRI_BIBS_projects/2020_05_Etienne_covid/"
tab<-read.delim(paste0(workdir,
"data/COVID_PAMLresults_332hits_plusBatScreens_2020_Apr14.csv"),
fill=T, h=T, dec=",")
dim(tab)
@
\subsection{Read DGINN Young table}
<<>>=
dginnY<-read.delim(paste0(workdir,
"data/summary_primate_young.res"),
fill=T, h=T)
dim(dginnY)
@
\subsection{Joining Young and DGINN Young table}
<<>>=
# correct gene names (MARC1)
val_remp=as.character(unique(dginnY$Gene)[(unique(dginnY$Gene) %in%
tab$Gene.name)==F])
tab$Gene.name<-as.character(tab$Gene.name)
tab$Gene.name[158]<-val_remp
sum(unique(dginnY$Gene) %in% unique(tab$Gene.name))
@
<<>>=
add_col<-function(method="PamlM1M2"){
tmp<-dginnY[dginnY$Method==method,
c("Gene", "Omega", "PosSel", "PValue", "NbSites", "PSS")]
names(tmp)<-c("Gene.name", paste0("Omega_", method),
paste0("PosSel_", method), paste0("PValue_", method),
paste0("NbSites_", method), paste0("PSS_", method))
tab<-merge(tab, tmp, by="Gene.name")
return(tab)
}
tab<-add_col("PamlM1M2")
tab<-add_col("PamlM7M8")
tab<-add_col("BppM1M2")
tab<-add_col("BppM7M8")
# Manip pour la colonne BUSTED
tmp<-dginnY[dginnY$Method=="BUSTED",c("Gene", "Omega", "PosSel", "PValue")]
names(tmp)<-c("Gene.name", "Omega_BUSTED", "PosSel_BUSTED", "PValue_BUSTED")
tab<-merge(tab, tmp, by="Gene.name")
tmp<-dginnY[dginnY$Method=="MEME",c("Gene", "NbSites", "PSS")]
names(tmp)<-c("Gene.name", "NbSites_MEME", "PSS_MEME")
tab<-merge(tab, tmp, by="Gene.name")
@
\subsection{Read DGINN Table}
<<>>=
dginnT<-read.delim(paste0(workdir,
"data/DGINN_202005281649summary_cleaned.csv"),
fill=T, h=T, sep=",")
dim(dginnT)
names(dginnT)
# Number of genes in dginn-primate output not present in the original table
dginnT[(dginnT$Gene %in% tab$Gene.name)==F,"Gene"]
# This includes paralogs, recombinations found by DGINN and additionnal genes
# included on purpose
# Number of genes from the original list not present in DGINN output
tab[(tab$Gene.name %in% dginnT$Gene)==F,"Gene.name"]
names(dginnT)<-c("File", "Name", "Gene.name", "GeneSize",
"dginn-primate_NbSpecies", "dginn-primate_omegaM0Bpp",
"dginn-primate_omegaM0codeml", "dginn-primate_BUSTED",
"dginn-primate_BUSTED.p.value", "dginn-primate_MEME.NbSites",
"dginn-primate_MEME.PSS", "dginn-primate_BppM1M2",
"dginn-primate_BppM1M2.p.value", "dginn-primate_BppM1M2.NbSites",
"dginn-primate_BppM1M2.PSS", "dginn-primate_BppM7M8",
"dginn-primate_BppM7M8.p.value", "dginn-primate_BppM7M8.NbSites",
"dginn-primate_BppM7M8.PSS", "dginn-primate_codemlM1M2",
"dginn-primate_codemlM1M2.p.value", "dginn-primate_codemlM1M2.NbSites",
"dginn-primate_codemlM1M2.PSS", "dginn-primate_codemlM7M8",
"dginn-primate_codemlM7M8.p.value", "dginn-primate_codemlM7M8.NbSites",
"dginn-primate_codemlM7M8.PSS")
@
<<eval=FALSE>>=
table(dginnT$`dginn-primate_BUSTED`)
table(dginnT$`dginn-primate_codemlM1M2`)
table(dginnT$`dginn-primate_codemlM7M8`)
table(dginnT$`dginn-primate_BppM1M2`)
table(dginnT$`dginn-primate_BppM7M8`)
table(dginnT$`dginn-primate_BUSTED`=="na",dginnT$`dginn-primate_codemlM1M2`=="na", dginnT$`dginn-primate_codemlM7M8`=="na",
dginnT$`dginn-primate_BppM1M2`=="na", dginnT$`dginn-primate_BppM7M8`=="na" )
@
\subsection{Join Table and DGINN table}
<<>>=
tab<-merge(tab,dginnT, by="Gene.name", all.x=T)
table(tab$`dginn-primate_BUSTED`)
table(tab$`dginn-primate_codemlM1M2`)
table(tab$`dginn-primate_codemlM7M8`)
table(tab$`dginn-primate_BppM1M2`)
table(tab$`dginn-primate_BppM7M8`)
table(tab$`dginn-primate_BUSTED`=="na" | tab$`dginn-primate_codemlM1M2`=="na" | tab$`dginn-primate_codemlM7M8`=="na" |
tab$`dginn-primate_BppM1M2`=="na"| tab$`dginn-primate_BppM7M8`=="na" )
@
\subsection{Add DGINN results on bat dataset}
DGINN results from different analysis.
<<>>=
# original table
dginnbats<-read.delim(paste0(workdir,
"data/DGINN_202005281339summary_cleaned.tab"),
fill=T, h=T)
# rerun on corrected alignment
dginnbatsnew1<-read.delim(paste0(workdir,
"data/DGINN_202011262248_summary.tab"),
fill=T, h=T)
dginnbatsnew2<-read.delim(paste0(workdir,
"data/DGINN_202012192053_summary.tab"),
fill=T, h=T)
# colomne choice, BUSTED and Bppml form first file, codeml from the other one
dginnbatsnew<-dginnbatsnew1
dginnbatsnew$omegaM0codeml<-dginnbatsnew2$omegaM0codeml
dginnbatsnew$codemlM1M2<-dginnbatsnew2$codemlM1M2
dginnbatsnew$codemlM1M2_p.value<-dginnbatsnew2$codemlM1M2_p.value
dginnbatsnew$codemlM1M2_NbSites<-dginnbatsnew2$codemlM1M2_NbSites
dginnbatsnew$codemlM1M2_PSS<-dginnbatsnew2$codemlM1M2_PSS
dginnbatsnew$codemlM7M8<-dginnbatsnew2$codemlM7M8
dginnbatsnew$codemlM7M8_p.value<-dginnbatsnew2$codemlM7M8_p.value
dginnbatsnew$codemlM7M8_NbSites<-dginnbatsnew2$codemlM7M8_NbSites
dginnbatsnew$codemlM7M8_PSS<-dginnbatsnew2$codemlM7M8_PSS
####
## RIPK1 is actually a primat results
## 1. Take it and put it at the right place
ripk1<-as.vector(dginnbatsnew[dginnbatsnew$Gene=="RIPK1",])
tab$`dginn-primate_omegaM0Bpp`<-as.numeric(as.character(tab$`dginn-primate_omegaM0Bpp`))
tab$`dginn-primate_BUSTED.p.value`<-as.numeric(as.character(tab$`dginn-primate_BUSTED.p.value`))
tab$`dginn-primate_BppM1M2.p.value`<-as.numeric(as.character(tab$`dginn-primate_BppM1M2.p.value`))
tab$`dginn-primate_BppM7M8.p.value`<-as.numeric(as.character(tab$`dginn-primate_BppM7M8.p.value`))
tab$`dginn-primate_BppM7M8.PSS`<-as.numeric(as.character(tab$`dginn-primate_BppM7M8.PSS`))
tab$`dginn-primate_codemlM1M2.p.value`<-as.numeric(as.character(tab$`dginn-primate_codemlM1M2.p.value`))
tab$`dginn-primate_codemlM1M2.PSS`<-as.numeric(as.character(tab$`dginn-primate_codemlM1M2.PSS`))
tab$`dginn-primate_codemlM7M8.p.value`<-as.numeric(as.character(tab$`dginn-primate_codemlM7M8.p.value`))
tab$`dginn-primate_codemlM7M8.PSS`<-as.numeric(as.character(tab$`dginn-primate_codemlM7M8.PSS`))
tab[tab$Gene.name=="RIPK1","GeneSize"]<-ripk1$GeneSize
tab[tab$Gene.name=="RIPK1","dginn-primate_NbSpecies"]<-ripk1$NbSpecies
tab[tab$Gene.name=="RIPK1","dginn-primate_omegaM0Bpp"]<-ripk1$omegaM0Bpp
tab[tab$Gene.name=="RIPK1","dginn-primate_omegaM0codeml"]<-ripk1$omegaM0codeml
tab[tab$Gene.name=="RIPK1","dginn-primate_BUSTED"]<-ripk1$BUSTED
tab[tab$Gene.name=="RIPK1","dginn-primate_BUSTED.p.value"]<-ripk1$BUSTED_p.value
tab[tab$Gene.name=="RIPK1","dginn-primate_MEME.NbSites"]<-ripk1$MEME_NbSites
tab[tab$Gene.name=="RIPK1","dginn-primate_MEME.PSS"]<-as.numeric(as.character(ripk1$MEME_PSS))
tab[tab$Gene.name=="RIPK1","dginn-primate_BppM1M2"]<-ripk1$BppM1M2
tab[tab$Gene.name=="RIPK1","dginn-primate_BppM1M2.p.value"]<-ripk1$BppM1M2_p.value
tab[tab$Gene.name=="RIPK1","dginn-primate_BppM1M2.NbSites"]<-ripk1$BppM1M2_NbSites
tab[tab$Gene.name=="RIPK1","dginn-primate_BppM1M2.PSS"]<-ripk1$BppM1M2_PSS
tab[tab$Gene.name=="RIPK1","dginn-primate_BppM7M8"]<-ripk1$BppM7M8
tab[tab$Gene.name=="RIPK1","dginn-primate_BppM7M8.p.value"]<-ripk1$BppM7M8_p.value
tab[tab$Gene.name=="RIPK1","dginn-primate_BppM7M8.NbSites"]<-ripk1$BppM7M8_NbSites
tab[tab$Gene.name=="RIPK1","dginn-primate_BppM7M8.PSS"]<-ripk1$BppM7M8_PSS
tab[tab$Gene.name=="RIPK1","dginn-primate_codemlM1M2"]<-ripk1$codemlM1M2
tab[tab$Gene.name=="RIPK1","dginn-primate_codemlM1M2.p.value"]<-ripk1$codemlM1M2_p.value
tab[tab$Gene.name=="RIPK1","dginn-primate_codemlM1M2.NbSites"]<-ripk1$codemlM1M2_NbSites
tab[tab$Gene.name=="RIPK1","dginn-primate_codemlM1M2.PSS"]<-ripk1$codemlM1M2_PSS
tab[tab$Gene.name=="RIPK1","dginn-primate_codemlM7M8"]<-ripk1$codemlM7M8
tab[tab$Gene.name=="RIPK1","dginn-primate_codemlM7M8.p.value"]<-ripk1$codemlM7M8_p.value
tab[tab$Gene.name=="RIPK1","dginn-primate_codemlM7M8.NbSites"]<-ripk1$codemlM7M8_NbSites
tab[tab$Gene.name=="RIPK1","dginn-primate_codemlM7M8.PSS"]<-ripk1$codemlM7M8_PSS
## 2. Remove it
dginnbatsnew<-dginnbatsnew[dginnbatsnew$Gene!="RIPK1",]
## suppress redundant lines
dginnbats<-dginnbats[(dginnbats$Gene %in% dginnbatsnew$Gene)==FALSE,]
names(dginnbatsnew)<-names(dginnbats)
##############"
dginnbatsnew[,4]<-as.numeric(dginnbatsnew[,4])
dginnbats[,6]<-as.numeric(as.character(dginnbats[,6]))
dginnbats[,8]<-as.character(dginnbats[,8])
dginnbats[,12]<-as.character(dginnbats[,12])
dginnbats[,13]<-as.numeric(as.character(dginnbats[,13]))
dginnbats[,16]<-as.character(dginnbats[,16])
dginnbats[,17]<-as.numeric(as.character(dginnbats[,17]))
## replace by new data
dginnbats<-rbind(dginnbats, dginnbatsnew)
names(dginnbats)<-c("File", "bats_Name", "cooper.batsGene", paste0("bats_",
names(dginnbats)[-(1:3)]))
names(dginnbats)
tab<-merge(tab,dginnbats, by="cooper.batsGene", all.x=T)
@
\subsection{Write the new table}
<<>>=
write.table(tab, "covid_comp_complete_old.txt", row.names=FALSE, quote=FALSE, sep="\t")
@
\section{Second Table}
Table containing the DGINN results for both Primates and bats. Conserve all genes.
\subsection{Primates}
<<>>=
dginnT<-read.delim(paste0(workdir,
"data/DGINN_202005281649summary_cleaned.csv"),
fill=T, h=T, sep=",")
dim(dginnT)
names(dginnT)
# Rename the columns to include primate
names(dginnT)<-c("File", "Name", "Gene.name", "GeneSize",
"dginn-primate_NbSpecies", "dginn-primate_omegaM0Bpp",
"dginn-primate_omegaM0codeml", "dginn-primate_BUSTED",
"dginn-primate_BUSTED.p.value", "dginn-primate_MEME.NbSites",
"dginn-primate_MEME.PSS", "dginn-primate_BppM1M2",
"dginn-primate_BppM1M2.p.value", "dginn-primate_BppM1M2.NbSites",
"dginn-primate_BppM1M2.PSS", "dginn-primate_BppM7M8",
"dginn-primate_BppM7M8.p.value", "dginn-primate_BppM7M8.NbSites",
"dginn-primate_BppM7M8.PSS", "dginn-primate_codemlM1M2",
"dginn-primate_codemlM1M2.p.value", "dginn-primate_codemlM1M2.NbSites",
"dginn-primate_codemlM1M2.PSS", "dginn-primate_codemlM7M8",
"dginn-primate_codemlM7M8.p.value", "dginn-primate_codemlM7M8.NbSites",
"dginn-primate_codemlM7M8.PSS")
@
\subsection{Bats}
<<>>=
# original table
dginnbats<-read.delim(paste0(workdir,
"data/DGINN_202005281339summary_cleaned-LE201108.txt"),
fill=T, h=T)
# rerun on corrected alignment
dginnbatsnew<-read.delim(paste0(workdir,
"data/DGINN_202011262248_hyphybpp-202012192053_codeml-summary.txt"),
fill=T, h=T)
@
<<>>=
# Add both columns
dginnbatsnew$Lucie.s.comments<-""
dginnbatsnew$Action.taken<-""
# Homogenize column names
dginnbats$BUSTED_p.value<-dginnbats$BUSTED.p.value
dginnbats$MEME_NbSites<-dginnbats$MEME.NbSites
dginnbats$MEME_PSS<-dginnbats$MEME.PSS
dginnbats$BppM1M2_p.value<-dginnbats$BppM1M2.p.value
dginnbats$BppM1M2_NbSites<-dginnbats$BppM1M2.NbSites
dginnbats$BppM1M2_PSS<-dginnbats$BppM1M2.PSS
dginnbats$BppM7M8_p.value<-dginnbats$BppM7M8.p.value
dginnbats$BppM7M8_NbSites<-dginnbats$BppM7M8.NbSites
dginnbats$BppM7M8_PSS<-dginnbats$BppM7M8.PSS
dginnbats$codemlM1M2_p.value<-dginnbats$codemlM1M2.p.value
dginnbats$codemlM1M2_NbSites<-dginnbats$codemlM1M2.NbSites
dginnbats$codemlM1M2_PSS<-dginnbats$codemlM1M2.PSS
dginnbats$codemlM7M8_p.value<-dginnbats$codemlM7M8.p.value
dginnbats$codemlM7M8_NbSites<-dginnbats$codemlM7M8.NbSites
dginnbats$codemlM7M8_PSS<-dginnbats$codemlM7M8.PSS
@
<<>>=
# Order columns in the same order in both tables
dginnbats<-dginnbats[,names(dginnbatsnew)]
names(dginnbatsnew) %in% names(dginnbats)
names(dginnbats)==names(dginnbatsnew)
# Put RIPK aside
ripk1<-dginnbatsnew[dginnbatsnew$Gene=="RIPK1",1:27]
# Add it to primate table
names(ripk1)<-names(dginnT)
ripk1$`dginn-primate_omegaM0Bpp`<-as.factor(ripk1$`dginn-primate_omegaM0Bpp`)
ripk1$`dginn-primate_BUSTED.p.value`<-as.factor(ripk1$`dginn-primate_BUSTED.p.value`)
ripk1$`dginn-primate_BppM1M2.p.value`<-as.factor(ripk1$`dginn-primate_BppM1M2.p.value`)
ripk1$`dginn-primate_BppM7M8.p.value`<-as.factor(ripk1$`dginn-primate_BppM7M8.p.value`)
dginnT<-rbind(dginnT, ripk1)
## Remove it Ripk1 from bats
dginnbatsnew<-dginnbatsnew[dginnbatsnew$Gene!="RIPK1",]
## suppress redundant lines
dginnbats<-dginnbats[(dginnbats$Gene %in% dginnbatsnew$Gene)==FALSE,]
names(dginnbatsnew)<-names(dginnbats)
## replace by new data
dginnbatsnew$omegaM0Bpp<-as.factor(dginnbatsnew$omegaM0Bpp)
dginnbatsnew$BppM1M2_p.value<-as.factor(dginnbatsnew$BppM1M2_p.value)
dginnbatsnew$BppM7M8_p.value<-as.factor(dginnbatsnew$BppM7M8_p.value)
dginnbats<-rbind(dginnbats, dginnbatsnew)
names(dginnbats)<-c("bats_File", "bats_Name", "Gene.name", paste0("bats_",
names(dginnbats)[-(1:3)]))
names(dginnbats)
@
\subsection{Merged table}
<<setup, include=FALSE, cache=FALSE, tidy=TRUE>>=
options(tidy=TRUE, width=70)
@
<<>>=
#tidy.opts = list(width.cutoff = 60)
dim(dginnT)
dginnT$Gene.name
dim(dginnbats)
dginnbats$Gene.name
@
Manual corrections:
TMPRSS2 in bats
<<>>=
dginnbats[dginnbats$Gene.name=="TMPRSS2",]
# keeping the uncut one
# renaming the other one TMPRSS2_cut
dginnbats$Gene.name<-as.character(dginnbats$Gene.name)
dginnbats[dginnbats$bats_File=="TMPRSS2_bat_select_cut_mafft_prank","Gene.name"]<-"TMPRSS2_cut"
@
RIPK1: ANcestral version kept, suppress it "RIPK1\_sequences\_filtered\_longestORFs\_mafft\_mincov\_prank"
<<>>=
dginnT<-dginnT[dginnT$File!="RIPK1_sequences_filtered_longestORFs_mafft_mincov_prank",]
@
REEP6 eA et B
<<>>=
dginnbats$Gene.name<-as.character(dginnbats$Gene.name)
dginnbats[dginnbats$bats_File=="REEP6_sequences_filtered_longestORFs_D210gp1_prank", "Gene.name"]<-"REEP6_old"
dginnbats[dginnbats$bats_File=="REEP6_LA_bat_select_mafft_prank", "Gene.name"]<-"REEP6"
dginnbats[dginnbats$bats_File=="REEP6_LB_bat_select_mafft_prank", "Gene.name"]<-"REEP6_like"
@
GNG5
<<>>=
dginnT$Gene.name<-as.character(dginnT$Gene.name)
dginnT[dginnT$File=="GNG5_sequences_filtered_longestORFs_D189gp2_prank", "Gene.name"]<-"GNG5_like"
@
<<>>=
dim(dginnbats)
dim(dginnT)
# genes in common
common<-dginnT$Gene.name[dginnT$Gene.name %in% dginnbats$Gene.name]
common
length(dginnT$Gene.name[dginnT$Gene.name %in% dginnbats$Gene.name])
# genes only in primates
onlyprimates<-dginnT$Gene.name[(dginnT$Gene.name %in% dginnbats$Gene.name)==FALSE]
onlyprimates
length(dginnT$Gene.name[(dginnT$Gene.name %in% dginnbats$Gene.name)==FALSE])
# genes only in bats
onlybats<-dginnbats$Gene.name[(dginnbats$Gene.name %in% dginnT$Gene.name)==FALSE]
onlybats
length(dginnbats$Gene.name[(dginnbats$Gene.name %in% dginnT$Gene.name)==FALSE])
@
<<>>=
tab<-merge(dginnT, dginnbats, by="Gene.name", all.x=T, all.y=T)
dim(tab)
# add column "shared"/"only bats"/"only primates"
tab$status<-""
tab$status[tab$Gene.name %in% common]<-"shared"
tab$status[tab$Gene.name %in% onlyprimates]<-"onlyprimates"
tab$status[tab$Gene.name %in% onlybats]<-"onlybats"
table(tab$status)
write.table(tab, "covid_comp_alldginn.txt", sep="\t")
@
\section{Complete data}
Merge the previous tab with J Young's original table. \textbf{Will replace the 1st part of this script}
<<>>=
young<-read.delim(paste0(workdir,
"data/COVID_PAMLresults_332hits_plusBatScreens_2020_Apr14.csv"),
fill=T, h=T, dec=",")
dim(young)
young$PreyGene<-as.character(young$PreyGene)
young$PreyGene[young$PreyGene=="MTARC1"]<-"MARC1"
@
How many genes in the Young table are not in the DGINN table. And who are they?
<<>>=
table(young$PreyGene %in% tab$Gene.name)
young[(young$PreyGene %in% tab$Gene.name)==FALSE, "PreyGene"]
tab[(tab$Gene.name %in% young$PreyGene)==FALSE, "Gene.name"]
@
Merge them and keep only the krogan genes
<<>>=
tablo<-merge(young, tab, by="Gene.name", all.x=TRUE)
write.table(tablo, "covid_comp_complete.txt", row.names=FALSE, quote=TRUE, sep="\t")
@
\end{document}
covid_comp_shiny.R deleted 100644 → 0
View file @ 77f559a1
makeFig1 <- function(df){
# prepare data for colors etc
colMethods <- c("deepskyblue4", "darkorange" , "deepskyblue3" , "mediumseagreen" , "yellow3" , "black")
nameMethods <- c("BUSTED", "BppM1M2", "BppM7M8", "codemlM1M2", "codemlM7M8", "MEME")
metColor <- data.frame(Name = nameMethods , Col = colMethods , stringsAsFactors = FALSE)
# subset for this specific figure
#df <- df[df$nbY >= 1, ] # to drop genes found by 0 methods (big datasets)
xt <- df[, c("BUSTED", "BppM1M2", "BppM7M8", "codemlM1M2", "codemlM7M8")]
xt$Gene <- df$Gene
nbrMeth <- 5
# reverse order of dataframe so that genes with the most Y are at the bottom (to be on top of the barplot)
xt[,1:5] <- ifelse(xt[,1:5] == "Y", 1, 0)
# sort and Filter the 0 lines
xt<-xt[order(rowSums(xt[,1:5])),]
xt<-na.omit(xt[rowSums(xt[,1:5])>2,])
row.names(xt)<-xt$Gene
xt<-xt[,1:5]
colFig1 <- metColor[which(metColor$Name %in% colnames(xt)) , ]
##### PART 1 : NUMBER OF METHODS
par(xpd = NA , mar=c(2,7,4,0) , oma = c(0,0,0,0) , mgp = c(3,0.3,0))
h = barplot(
t(xt),
border = NA ,
axes = F ,
col = adjustcolor(colFig1$Col, alpha.f = 1),
horiz = T ,
las = 2 ,
main = "Methods detecting positive selection" ,
cex.main = 0.85,
cex.names = min(50/nrow(xt), 1.5)
)
axis(3, line = 0, at = c(0:nbrMeth), label = c("0", rep("", nbrMeth -1), nbrMeth), tck = 0.02)
legend("bottomleft",
horiz = T,
border = colFig1$Col,
legend = colFig1$Name,
fill = colFig1$Col,
cex = 0.8,
bty = "n",
xpd = NA
)
}
df<-read.delim(paste0(workdir,
"/data/DGINN_202005281649summary_cleaned.csv"),
fill=T, h=T, sep=",")
data/COVID_PAMLresults_332hits_plusBatScreens_2020_Apr14.csv 0 → 100755
View file @ b9ee17c2
PreyGene PreyGene_JYname BaitShort Gene.name list description other.names top40_posSeln Num primate seqs Alignment length (nucleotides) Alignment length (codons) whole gene dN/dS model 0 total.tree.length total.dN.tree.length total.dS.tree.length p-value M8vsM8a (raw) p-value M8vsM8a (BH corrected) pVal.M8vsM7 pVal.M8vsM7.adj pVal.M2vsM1 pVal.M2vsM1.adj % codons under positive selection dN/dS of positively selected codons Number of codons with BEB >= 0.9 Codons under positive selection (BEB>=0.9) (alignment position) cooper batsGene cooper batsGene_Ensembl_ID cooper batsIsoform_Ensembl_ID cooper batsSpecies cooper batsReference_length.aa. cooper batsPercent_analyzed cooper batsAverage_dNdS cooper batsMaximum_dS cooper batsAverage_M7_tree cooper batsAverage_M8_tree cooper batsM7_log_likelihood cooper batsM8_log_likelihood cooper batsM7.M8_p_value cooper batsM8a_log_likelihood cooper batsM8.M8a_pvalue cooper batsBEB_hits.pp.0.95. cooper batsBEB_sites cooper primates Gene cooper primates Gene_Ensembl_ID cooper primates Isoform_Ensembl_ID cooper primates Species cooper primates Reference_length.aa. cooper primates Percent_analyzed cooper primates Average_dNdS cooper primates Maximum_dS cooper primates Average_M7_tree cooper primates Average_M8_tree cooper primates M7_log_likelihood cooper primates M8_log_likelihood cooper primates M7.M8_p_value cooper primates M8a_log_likelihood cooper primates M8.M8a_pvalue cooper primates BEB_hits.pp.0.95. cooper primates BEB_sites hawkins_Gene hawkins_Positive.Selection..M8vM8a.p.value hawkins_Positive.Selection..M8vM8a.FDR.corrected.p.value hawkins_Gene.Name.Alias hawkins_Connection.to.immunity.or.pathogens hawkins_Connection.to.reproduction hawkins_Connection.to.collagen hawkins_Connection.to.peroxisome hawkins_Gene.Description.for.Human.Ortholog..from.Genbank.GENE.database. CpGmask.numNT CpGmask.numAA CpGmask.overall.dN.dS CpGmask.total.tree.length CpGmask.total.dN.tree.length CpGmask.total.dS.tree.length CpGmask.pVal.M8vsM8a CpGmask.pVal.M8vsM8a.adj CpGmask.pVal.M8vsM7 CpGmask.pVal.M8vsM7.adj CpGmask.pVal.M2vsM1 CpGmask.pVal.M2vsM1.adj CpGmask.percent.sites.under.positive.selection CpGmask.dN.dS.of.selected.sites CpGmask.num.sites.with.BEB...0.9 CpGmask.which.sites.have.BEB...0.9
PCNT PCNT nsp13 PCNT list26_COV_list4dataset2nonOrf pericentrin KEN|MOPD2|PCN|PCNT2|PCNTB|PCTN2|SCKL4 yes 21 10074 3358 0,44695 1,04111 0,2557 0,5721 1,4068E-029 5,41618E-027 4,0744E-033 1,568644E-030 6,8784E-028 2,648184E-025 5,984 3,50299 36 77_D_0.956,109_V_0.951,113_N_0.905,122_M_0.986,129_P_0.941,133_H_0.929,138_V_0.958,155_P_0.974,161_M_0.972,164_V_0.976,165_S_0.931,172_P_0.921,178_M_0.991,181_I_0.918,185_Q_0.972,189_R_0.984,202_R_0.953,207_I_0.921,217_M_0.931,599_R_0.916,638_C_0.980,650_R_0.977,769_M_0.943,1016_W_0.904,1088_R_0.991,1825_R_0.961,1935_W_0.905,1967_R_0.923,1983_R_0.968,2039_V_0.964,2136_I_0.973,2140_I_0.992,2215_R_0.988,2298_A_0.907,2377_Q_0.986,2516_R_0.935 PCNT ENSG00000160299.16 ENST00000418394.1 18 228 96,92982456 0,251847598 0,2741 0,75182 0,75645 -1816,308178 -1815,640662 0,512981242 6678 2226 0,41957 0,53703 0,1291 0,3076 9,4062E-020 3,621387E-017 1,0178E-018 3,91853E-016 1,6815E-018 6,473775E-016 3,035 6,7467 23 3_V_0.960,38_T_0.987,71_Q_0.927,75_N_0.989,78_P_0.987,82_M_1.000,91_M_0.998,100_I_0.993,104_Q_0.997,107_M_0.983,112_I_0.991,115_I_0.993,119_P_0.900,123_M_0.994,438_S_0.959,1128_I_0.991,1463_S_0.956,1487_T_0.946,1542_P_0.960,1615_I_0.978,1728_T_0.940,1759_A_0.939,2216_T_0.954
PVR PVR orf8 PVR list23_COV_list1orf poliovirus receptor CD155|HVED|NECL5|Necl-5|PVS|TAGE4 yes 26 1263 421 0,55674 1,7972 0,5039 0,905 0,00000000000003286 0,00000000000632555 2,733E-016 0,00000000000005261025 0,00000000000006735 0,000000000012964875 10,922 3,37707 20 24_-_0.925,81_R_0.951,219_I_0.933,343_W_0.999,345_N_1.000,356_V_0.975,364_F_0.963,370_W_0.996,373_C_0.996,375_R_0.959,381_-_0.956,388_R_0.999,389_T_0.984,390_A_0.951,391_A_0.952,393_T_0.942,394_L_0.997,403_P_0.978,410_S_0.969,420_R_0.993 753 251 0,6007 1,13723 0,3275 0,5451 0,00000016829 0,00001295833 0,00000088661 0,00006826897 0,0000011781 0,0000907137 11,142 4,14108 16 113_I_0.971,201_P_0.972,209_V_0.994,211_V_0.912,213_T_0.948,215_L_0.909,229_-_0.976,230_A_0.984,231_V_0.919,236_P_0.997,238_A_0.924,239_-_0.926,243_S_0.954,247_E_0.926,249_T_0.955,250_R_0.999
POLA1 POLA1 nsp1 POLA1 list24_COV_list2nonOrf DNA polymerase alpha 1, catalytic subunit NSX|POLA|p180 yes 23 4407 1469 0,34634 0,34119 0,0746 0,2155 0,00000000015891 0,00000002039345 0,0000000011774 0,000000151099666666667 0,0000000012972 0,000000166474 0,426 17,73252 9 15_G_0.989,16_A_0.997,17_S_1.000,205_S_0.957,238_P_0.937,566_V_0.912,735_Q_0.954,1075_V_0.989,1099_N_0.925 POLA1 ENSMLUG00000004537.2 ENSMLUT00000004548.2 10 1350 93,85185185 0,188601927 0,2663 0,7851 0,80503 -9819,54088 -9806,381977 0,00000193 -9818,83609 0,000000601 3 P289, H243, V1073 POLA1 ENSG00000101868.10 ENST00000611764.1 15 1462 95,69083447 0,269632203 0,0987 0,25387 0,25688 -7874,115011 -7872,112305 0,134969561 POLA1 0,058773459 0,717665523 3831 1277 0,38372 0,22477 0,0529 0,1378 0,000000022632 0,00000290444 0,00000014029 0,0000135029125 0,00000011093 0,0000142360166666667 0,078 54,80272 4 6_S_1.000,85_G_0.923,624_T_0.904,956_N_0.936
FASTKD5 FASTKD5 M FASTKD5 list26_COV_list4dataset2nonOrf FAST kinase domains 5 dJ1187M17.5 yes 24 2298 766 0,67059 0,70536 0,2053 0,3062 0,000000027703 0,00000266641375 0,00000012968 0,00000998536 0,00000020219 0,0000194607875 3,702 5,3533 8 41_Q_0.959,75_H_0.998,115_M_0.997,180_V_0.943,253_Y_0.953,257_K_0.948,329_S_0.990,720_N_0.919 FASTKD5 ENSMLUG00000000343.2 ENSMLUT00000000340.2 14 767 97,52281617 0,23719054 0,2971 1,29358 1,29944 -7754,59961 -7752,020331 0,075828657 2013 671 0,64965 0,56536 0,1635 0,2517 0,0000034038 0,0002184105 0,000016149 0,0010362275 0,00002161 0,00138664166666667 2,928 6,1161 5 33_Q_0.973,152_V_0.963,224_Y_0.970,227_K_0.968,293_S_0.998
PRIM2 PRIM2 nsp1 PRIM2 list24_COV_list2nonOrf DNA primase subunit 2 PRIM2A|p58 yes 24 1530 510 0,32302 0,46099 0,0986 0,3053 0,000000071094 0,000005474238 0,00000033399 0,000021431025 0,00000049329 0,0000375824166666667 6 4,30163 12 5_G_0.933,6_K_0.908,7_L_0.944,8_R_0.999,9_R_0.923,10_M_0.979,11_Q_0.996,164_Q_0.956,173_L_0.975,176_V_0.998,179_-_0.965,180_S_0.986 1359 453 0,33447 0,35235 0,0764 0,2284 0,00010698 0,003432275 0,00025044 0,00803495 0,0005311 0,0185885 5,344 4,18248 8 5_G_0.954,6_K_0.938,7_L_0.959,8_R_0.948,142_Q_0.964,145_I_0.926,151_N_0.919,153_V_0.998
ITGB1 ITGB1 orf8 ITGB1 list25_COV_list3dataset2orf integrin subunit beta 1 CD29|FNRB|GPIIA|MDF2|MSK12|VLA-BETA|VLAB yes 24 2406 802 0,08863 0,38988 0,0372 0,4199 0,0000004944 0,000031724 0,0000010091 0,0000555005 0,0000011028 0,000060654 0,127 15,9223 5 15_V_0.914,387_G_0.957,580_E_0.924,752_K_0.936,778_Q_1.000 ITGB1 ENSMLUG00000017570.2 ENSMLUT00000017574.2 10 799 99,62453066 0,045400673 0,2938 0,5461 0,54338 -5214,22421 -5213,924729 0,741202805 ITGB1 ENSG00000150093.18 ENST00000302278.7 17 799 99,24906133 0,086420105 0,1678 0,33069 0,32447 -4625,018527 -4623,231946 0,167531983 ITGB1 1 1 2061 687 0,10232 0,27124 0,0308 0,3012 0,0014809 0,0221197307692308 0,0038315 0,0614636458333333 0,0057988 0,0970668695652174 4,481 2,56687 7 13_V_0.945,445_K_0.928,446_E_0.938,496_E_0.956,554_R_0.934,645_K_0.959,667_Q_0.999
CNTRL CNTRL nsp13 CNTRL list26_COV_list4dataset2nonOrf centriolin CEP1|CEP110|FAN|bA165P4.1 yes 24 6981 2327 0,40767 0,39419 0,092 0,2258 0,0000017117 0,0000941435 0,0000093186 0,000398629 0,000010608 0,00051051 0,933 7,94398 8 437_T_0.993,510_Q_0.935,853_R_0.978,1300_P_0.910,1376_C_0.975,1725_S_0.981,2217_F_0.912,2323_A_0.917 CNTRL ENSMLUG00000009719.2 ENSMLUT00000009796.2 10 2333 96,78525504 0,179657066 0,2905 0,74087 0,74929 -17641,08711 -17631,26535 0,0000543 -17636,52747 0,001178187 1 K252 CNTRL ENSG00000119397.16 ENST00000613863.4 18 483 96,6873706 0,203384647 0,1481 0,36514 0,36908 -2911,518698 -2910,971378 0,57849811 CNTRL 0,0000000166 0,00000699 centriolin This gene encodes a centrosomal protein required for the centrosome to function as a microtubule organizing center. The gene product is also associated with centrosome maturation. 6198 2066 0,39801 0,29068 0,0677 0,1701 0,046776 0,339787924528302 0,11823 0,734170161290323 0,12713 0,963556862745098 0,077 19,53224 1 1536_S_0.939
SIRT5 SIRT5 nsp14 SIRT5 list24_COV_list2nonOrf sirtuin 5 SIR2L5 yes 24 933 311 0,27368 0,6275 0,1152 0,421 0,0000029107 0,0001400774375 0,0000084157 0,000398629 0,000016309 0,000697662777777778 1,673 7,58579 4 22_P_0.989,26_R_0.992,46_F_0.978,303_C_1.000 SIRT5 ENSMLUG00000014620.2 ENSMLUT00000014625.2 10 311 99,03536977 0,237579341 0,3416 0,9244 0,92967 -2562,976409 -2561,669417 0,270632897 675 225 0,3326 0,34329 0,0735 0,221 0,0014798 0,0221197307692308 0,0060589 0,087456875 0,0067793 0,10701306 1,111 11,38468 2 17_P_0.990,33_F_0.987
CEP250 CEP250 nsp13 CEP250 list26_COV_list4dataset2nonOrf centrosomal protein 250 C-NAP1|CEP2|CNAP1 yes 24 7344 2448 0,37657 0,52603 0,1174 0,3117 0,0000039138 0,000167423666666667 0,000016167 0,00051869125 0,000026021 0,000910735 1,303 6,002 6 700_H_0.961,985_R_0.991,1168_T_0.957,1346_R_0.956,1816_A_0.961,2144_F_0.904 CEP250 ENSMLUG00000016216.2 ENSMLUT00000016246.2 9 2430 98,39506173 0,223396701 0,2874 0,94684 0,99501 -19511,34122 -19479,88355 0,0000000000000218 -19502,87264 0,000000000012 8 C2329, L2325, I2327, D2328, R2073, S2332, C2333, L2127 CEP250 ENSG00000126001.15 ENST00000621352.1 6 35 82,85714286 0,583356771 0,1803 0,37104 0,37247 -146,318139 -146,267744 0,950853763 CEP250 0,00000238 0,000511085 centrosomal protein 250 This gene encodes a core centrosomal protein required for centriole-centriole cohesion during interphase of the cell cycle. The encoded protein dissociates from the centrosomes when parental centrioles separate at the beginning of mitosis. 6276 2092 0,29525 0,37015 0,0731 0,2475 0,0066588 0,0754011176470588 0,020757 0,210301184210526 0,028514 0,332663333333333 0,494 9,3193 2 1567_A_0.958,1843_F_0.905
MRPS5 MRPS5 nsp8 MRPS5 list26_COV_list4dataset2nonOrf mitochondrial ribosomal protein S5 MRP-S5|S5mt yes 24 1293 431 0,51614 0,94585 0,2491 0,4827 0,0000051625 0,00019875625 0,000014829 0,00051869125 0,000024967 0,000910735 1,999 5,52626 4 17_-_0.930,48_C_0.978,136_H_0.974,351_H_1.000 MRPS5 ENSMLUG00000003183.2 ENSMLUT00000003187.2 9 432 88,19444444 0,186073572 0,4689 1,0865 1,10563 -3339,688105 -3336,94474 0,064353433 MRPS5 0,979691368 1 933 311 0,56675 0,66983 0,1855 0,3273 0,023793 0,20818875 0,067951 0,544313 0,071387 0,677712682926829 1,274 6,33775 3 23_C_0.984,68_S_0.901,180_I_0.956
CENPF CENPF nsp13 CENPF list26_COV_list4dataset2nonOrf centromere protein F CENF|CILD31|PRO1779|STROMS|hcp-1 yes 21 9351 3117 0,49349 0,50557 0,1309 0,2652 0,0000059335 0,0002076725 0,00002033 0,000602080769230769 0,000037031 0,00118807791666667 5,064 3,36994 7 918_D_0.918,1364_I_0.975,1705_N_0.930,1728_S_0.901,1976_T_0.971,3073_P_0.919,3107_S_0.902 CENPF ENSMLUG00000017038.2 ENSMLUT00000017046.2 8 3083 92,83165748 0,263203446 0,4624 1,16969 1,18996 -26308,11871 -26290,23285 0,0000000171 -26301,2531 0,00000267 4 S3001, T3002, S3004, C1367 CENPF ENSG00000117724.12 ENST00000614578.1 16 176 71,59090909 0,394049646 0,1534 0,47046 0,47825 -851,71129 -851,421955 0,748761328 CENPF 0,004868655 0,163786924 8244 2748 0,53617 0,38435 0,104 0,194 0,00066123 0,01212255 0,0027428 0,0502846666666667 0,0032003 0,061605775 1,472 6,00619 2 820_D_0.933,1740_T_0.968
TRMT1 TRMT1 nsp5_C145A TRMT1 list24_COV_list2nonOrf tRNA methyltransferase 1 TRM1 yes 23 1995 665 0,32049 1,20036 0,2581 0,8055 0,0000064958 0,000208406916666667 0,000000038427 0,00000369859875 0,0000005857 0,0000375824166666667 0,631 16,61671 3 2_S_0.997,3_H_0.995,5_R_0.976 TRMT1 ENSMLUG00000012809.2 ENSMLUT00000012821.2 7 672 93,00595238 0,200525465 0,3188 0,95425 0,96418 -4876,393271 -4874,829685 0,209383873 TRMT1 ENSG00000104907.12 ENST00000437766.5 4 660 96,21212121 0,244542375 0,2106 0,37666 0,38396 -3617,799544 -3616,444316 0,257888489 TRMT1 1 1 1263 421 0,36782 0,71903 0,1682 0,4573 0,00000000000070108 0,0000000001349579 0,00000000000053878 0,00000000010371515 0,0000000000048468 0,000000000933009 0,799 38,7804 3 2_S_1.000,3_H_1.000,4_R_0.999
SAAL1 SAAL1 M SAAL1 list26_COV_list4dataset2nonOrf serum amyloid A like 1 SPACIA1 yes 24 1431 477 0,28675 0,45035 0,0865 0,3016 0,0000092123 0,000272825807692308 0,000026072 0,00071698 0,000054487 0,00161365346153846 3,263 4,79472 6 14_C_0.998,15_D_0.996,17_Q_0.949,57_S_0.939,376_T_0.908,455_H_0.981 SAAL1 ENSMLUG00000008022.2 ENSMLUT00000008033.2 9 474 97,0464135 0,171414604 0,3324 0,7726 0,77317 -3561,95974 -3561,937062 0,977577213 SAAL1 ENSG00000166788.9 ENST00000530180.1 17 209 86,12440191 0,247077178 0,1884 0,41042 0,41683 -1145,791328 -1143,177301 0,073239016 SAAL1 1 1 1224 408 0,27904 0,26897 0,0511 0,1832 0,44278 1 0,66234 1 0,74608 1
CEP68 CEP68 nsp13 CEP68 list26_COV_list4dataset2nonOrf centrosomal protein 68 KIAA0582 yes 24 2277 759 0,59765 0,74742 0,2045 0,3421 0,000017751 0,0004881525 0,000092334 0,00209109352941176 0,000083935 0,0023082125 4,951 3,67498 5 44_R_0.927,190_A_0.949,220_G_0.949,414_R_0.999,425_R_0.985 CEP68 ENSMLUG00000002435.2 ENSMLUT00000002435.2 13 749 90,12016021 0,349914088 0,3466 1,64499 2,34495 -7894,797821 -7884,398068 0,0000304 -7893,86228 0,0000136 CEP68 ENSG00000011523.13 ENST00000537589.1 16 295 93,89830508 0,682457269 0,1598 0,61609 0,62168 -2118,847697 -2115,780209 0,046537911 -2118,560686 0,018365404 2 R25, R36 CEP68 0,946090868 1 1731 577 0,55976 0,41977 0,1124 0,2008 0,1004 0,544422535211268 0,24567 1 0,25935 1
NINL NINL nsp13 NINL list26_COV_list4dataset2nonOrf ninein like NLP yes 24 4224 1408 0,45757 1,05338 0,2658 0,581 0,000020207 0,000518646333333333 0,000015834 0,00051869125 0,00024343 0,00624803666666667 6,599 2,58396 12 703_D_0.929,708_R_0.943,802_G_0.942,829_R_0.939,848_R_0.902,871_P_0.938,875_G_0.920,903_A_0.949,938_L_0.942,981_R_0.908,1111_A_0.938,1185_S_0.953 NINL ENSG00000101004.14 ENST00000422516.5 13 1034 96,03481625 0,288848328 0,1913 0,51021 0,51842 -6660,290583 -6657,570979 0,065900846 NINL 0,613096766 1 2832 944 0,53526 0,53112 0,1438 0,2686 0,000000031176 0,00000300069 0,00000011293 0,0000135029125 0,00000021842 0,000021022925 12,273 3,41291 9 568_S_0.942,603_A_0.911,618_T_0.953,625_L_0.992,632_P_0.926,654_A_0.950,687_G_0.992,713_L_0.956,792_S_0.996
AKAP9 AKAP9 nsp13 AKAP9 list26_COV_list4dataset2nonOrf A-kinase anchoring protein 9 AKAP-9|AKAP350|AKAP450|CG-NAP|HYPERION|LQT11|MU-RMS-40.16A|PPP1R45|PRKA9|YOTIAO yes 22 11736 3912 0,35363 0,42129 0,0949 0,2682 0,000068311 0,0016437334375 0,000049129 0,0011821665625 0,000354 0,008518125 14,765 1,92832 2 329_A_0.913,1061_I_0.924 AKAP9 0,717684616 1 10671 3557 0,37099 0,33276 0,0769 0,2072 0,000085402 0,00298907 0,000070443 0,003013395 0,00044058 0,01696233 11,357 2,35214 3 981_I_0.976,1139_E_0.962,1231_T_0.905
NDUFAF2 NDUFAF2 nsp7 NDUFAF2 list24_COV_list2nonOrf NADH:ubiquinone oxidoreductase complex assembly factor 2 B17.2L|MMTN|NDUFA12L|mimitin yes 24 510 170 0,54117 0,76264 0,2107 0,3894 0,000087449 0,00198046264705882 0,00044116 0,00707694166666667 0,00045325 0,0102647794117647 4,784 6,9349 5 10_A_0.931,37_Q_0.983,94_-_0.973,98_-_0.998,106_-_0.967 NDUFAF2 0,165924363 1 435 145 0,56512 0,68089 0,1926 0,3408 0,00018537 0,005097675 0,00053012 0,0145783 0,00078464 0,0248221346153846 3,157 10,47691 4 47_I_0.913,77_-_0.988,81_-_0.999,87_-_0.970
GOLGB1 GOLGB1 nsp13 GOLGB1 list26_COV_list4dataset2nonOrf golgin B1 GCP|GCP372|GOLIM1 yes 24 9864 3288 0,43109 0,4171 0,1004 0,233 0,00010068 0,00207717631578947 0,00034794 0,00582421304347826 0,00054429 0,0110430157894737 4,229 3,42197 8 1135_A_0.961,1236_R_0.978,1268_R_0.945,1558_I_0.955,1851_V_0.923,2171_C_0.920,2893_V_0.923,3110_L_0.912 GOLGB1 ENSMLUG00000024533.1 ENSMLUT00000026933.1 5 3261 96,933456 0,221399945 0,2943 0,62767 0,63352 -21442,66525 -21438,93963 0,024098107 -21442,48654 0,007734994 1 S630 GOLGB1 0,511064965 1 9084 3028 0,42266 0,34099 0,0816 0,1932 0,17169 0,796393373493976 0,28226 1 0,36153 1
UGGT2 UGGT2 orf8 UGGT2 list25_COV_list3dataset2orf UDP-glucose glycoprotein glucosyltransferase 2 HUGT2|UGCGL2|UGT2 yes 23 4551 1517 0,35533 0,48303 0,1111 0,3127 0,00010251 0,00207717631578947 0,0001047 0,00223941666666667 0,00054498 0,0110430157894737 8,35 2,43106 3 640_M_0.913,665_R_0.967,1106_R_0.967 UGGT2 ENSMLUG00000013792.2 ENSMLUT00000013809.2 7 1468 97,68392371 0,218251257 0,4634 0,69338 0,70762 -9586,277286 -9585,18169 0,334340281 UGGT2 ENSG00000102595.19 ENST00000638479.1 16 280 98,57142857 0,198271962 0,1231 0,29704 0,30399 -1640,324902 -1637,365534 0,051851677 UGGT2 0,0000189 0,003004396 UDP-glucose glycoprotein glucosyltransferase 2 UDP-glucose:glycoprotein glucosyltransferase (UGT) is a soluble protein of the endoplasmic reticulum (ER) that selectively reglucosylates unfolded glycoproteins, thus providing quality control for protein transport out of the ER. 3948 1316 0,35856 0,34621 0,0812 0,2264 0,46686 1 0,6154 1 0,7956 1
SEPSECS SEPSECS nsp8 SEPSECS list24_COV_list2nonOrf Sep (O-phosphoserine) tRNA:Sec (selenocysteine) tRNA synthase LP|PCH2D|SLA|SLA/LP yes 24 1506 502 0,23138 0,39192 0,0699 0,3022 0,00014539 0,0027987575 0,00073465 0,0104755648148148 0,00073013 0,0140550025 5,009 3,57509 6 246_D_0.971,375_M_1.000,383_H_0.956,385_D_0.950,386_E_0.925,456_K_0.990 SEPSECS ENSMLUG00000005883.2 ENSMLUT00000005885.2 11 505 92,07920792 0,157833305 0,327 0,84904 0,84915 -3816,572052 -3816,423878 0,862281065 1296 432 0,24565 0,28571 0,0539 0,2196 0,0000083292 0,000458106 0,000048623 0,002339981875 0,000049379 0,002715845 1,667 8,61509 5 325_M_1.000,333_H_0.980,345_A_0.902,394_K_0.996,428_Y_0.910
ABCC1 ABCC1 orf9c ABCC1 list24_COV_list2nonOrf ATP binding cassette subfamily C member 1 ABC29|ABCC|GS-X|MRP|MRP1 yes 23 4596 1532 0,09711 0,7375 0,0836 0,8607 0,00015576 0,0028556 0,00017542 0,003376835 0,0018236 0,02808344 0,069 14,34062 2 927_H_0.937,1047_C_0.996 ABCC1 0,989782371 1 3117 1039 0,11016 0,38354 0,049 0,445 1 1 1 1 1 1
CDK5RAP2 CDK5RAP2 nsp13 CDK5RAP2 list26_COV_list4dataset2nonOrf CDK5 regulatory subunit associated protein 2 C48|Cep215|MCPH3 yes 25 5700 1900 0,58312 0,61288 0,1681 0,2882 0,00024834 0,00407217708333333 0,00060022 0,009243388 0,0011099 0,01942325 1,803 4,24322 2 508_R_0.994,613_S_0.957 CDK5RAP2 ENSMLUG00000004984.2 ENSMLUT00000026972.1 7 901 98,6681465 0,35046295 0,3523 1,11238 1,15965 -7755,145506 -7743,162832 0,00000625 -7754,009258 0,0000032 4 R684, V651, K660, H661 CDK5RAP2 0,039812298 0,586040651 4623 1541 0,56544 0,39808 0,1081 0,1911 0,24847 1 0,37909 1 0,51317 1
PDE4DIP PDE4DIP nsp13 PDE4DIP list26_COV_list4dataset2nonOrf phosphodiesterase 4D interacting protein CMYA2|MMGL yes 21 7119 2373 0,42718 0,57408 0,1357 0,3178 0,00025385 0,00407217708333333 0,0011533 0,015857875 0,001219 0,020405 17,329 1,85569 47 5_-_0.901,7_-_0.905,9_-_0.950,194_Q_0.912,207_T_0.957,214_M_0.938,333_S_0.902,419_R_0.953,421_R_0.977,571_H_0.979,664_E_0.971,690_L_0.914,694_G_0.953,706_T_0.954,736_V_0.952,925_S_0.945,976_V_0.951,1000_M_0.928,1095_A_0.951,1105_Q_0.945,1180_P_0.981,1193_V_0.941,1208_G_0.902,1212_A_0.976,1313_V_0.953,1324_R_0.950,1347_E_0.927,1358_L_0.907,1413_F_0.903,1637_H_0.945,1782_D_0.900,1783_S_0.904,1819_M_0.937,1936_T_0.974,2059_R_0.916,2097_V_0.957,2117_S_0.907,2147_T_0.976,2166_V_0.952,2170_P_0.900,2205_A_0.989,2211_V_0.954,2235_L_0.915,2241_H_0.949,2244_Q_0.946,2258_A_0.957,2366_F_0.961 6063 2021 0,4158 0,41454 0,0978 0,2353 0,0012121 0,0202895 0,0031858 0,0533275217391304 0,0052888 0,092554 18,141 1,90856 14 8_-_0.942,175_T_0.947,593_G_0.940,602_T_0.942,626_V_0.941,791_S_0.932,935_A_0.940,1011_P_0.983,1122_V_0.942,1405_H_0.926,1665_R_0.915,1853_V_0.938,1892_V_0.944,2015_F_0.952
ACADM ACADM M ACADM list26_COV_list4dataset2nonOrf acyl-CoA dehydrogenase medium chain ACAD1|MCAD|MCADH yes 24 1365 455 0,24077 0,47772 0,0949 0,3941 0,00028162 0,004336948 0,0012159 0,0161421206896552 0,0016625 0,0266692708333333 1,072 7,58852 4 6_G_0.924,89_T_0.994,264_I_0.907,316_V_0.995 ACADM ENSMLUG00000003015.2 ENSMLUT00000003015.2 4 447 99,32885906 0,179970289 0,1138 0,18347 0,232 -2134,919661 -2125,428875 0,0000755 -2134,103026 0,0000311 7 C97, T98, Q99, H100, R22, K88, E92 ACADM ENSG00000117054.13 ENST00000541113.5 17 386 88,86010363 0,440133164 0,1529 0,40184 0,43711 -2135,141647 -2128,596922 0,001437679 -2135,139747 0,000297567 2 T53, V247 ACADM 0,001706146 0,082051205 1200 400 0,29779 0,35519 0,0796 0,2672 0,39386 1 0,67628 1 0,69525 1
PRRC2B PRRC2B nsp12 PRRC2B list26_COV_list4dataset2nonOrf proline rich coiled-coil 2B BAT2L|BAT2L1|KIAA0515|LQFBS-1 yes 22 6741 2247 0,16522 0,44272 0,0595 0,3599 0,0003574 0,00529226923076923 0,000039029 0,00100174433333333 0,0021999 0,0313689444444444 0,607 4,55415 4 573_C_0.978,1184_T_0.941,1236_T_0.961,1281_A_0.921 PRRC2B ENSMLUG00000001237.2 ENSMLUT00000001244.2 4 2229 96,54553611 0,202135193 0,3733 0,84743 0,86752 -14962,60934 -14958,13038 0,011345206 -14958,87192 0,223291688 PRRC2B ENSG00000130723.18 ENST00000638390.1 18 326 100 0,103200802 0,0727 0,12189 0,12189 -1593,364246 -1593,364279 0,999967001 PRRC2B 0,129440486 1 5022 1674 0,22811 0,22154 0,0382 0,1673 0,39749 1 0,58988 1 0,69977 1
SLC25A21 SLC25A21 M SLC25A21 list26_COV_list4dataset2nonOrf solute carrier family 25 member 21 ODC|ODC1 yes 24 900 300 0,36182 0,33146 0,0714 0,1974 0,00044818 0,00639071481481482 0,0020029 0,0233671666666667 0,0021205 0,0313689444444444 2,169 6,95047 2 66_M_0.999,151_V_0.984 SLC25A21 ENSMLUG00000011418.2 ENSMLUT00000011420.2 8 241 96,68049793 0,18652784 0,2086 0,43093 0,43097 -1467,466963 -1467,465732 0,998769757 SLC25A21 ENSG00000183032.11 ENST00000331299.5 12 300 84 0,201973086 0,052 0,14097 0,14303 -1245,898917 -1244,559684 0,262046581 SLC25A21 0,023477256 0,440128319 807 269 0,31758 0,21859 0,0441 0,139 0,052178 0,35872375 0,10182 0,7000125 0,1512 1 0,699 7,1217 1 124_V_0.911
PUSL1 PUSL1 orf8 PUSL1 list23_COV_list1orf pseudouridylate synthase like 1 - yes 22 912 304 0,14275 0,85132 0,1262 0,8841 0,00048927 0,0067274625 0,00027699 0,00507815 0,0037234 0,0473736290322581 0,914 6,8565 2 141_S_0.923,303_E_0.987 PUSL1 ENSG00000169972.11 ENST00000467712.1 18 125 94,4 0,114100887 0,3613 0,93862 0,94765 -1034,121171 -1033,620377 0,606049266 PUSL1 0,365021161 1 453 151 0,20245 0,52978 0,0976 0,482 0,63273 1 0,8921 1 1 1
NDUFB9 NDUFB9 orf9c NDUFB9 list26_COV_list4dataset2nonOrf NADH:ubiquinone oxidoreductase subunit B9 B22|CI-B22|LYRM3|UQOR22 yes 24 540 180 0,29487 0,61954 0,1254 0,4252 0,0005814 0,00771858620689655 0,0024264 0,0253382027027027 0,0030169 0,041482375 5,154 4,34856 4 4_-_0.959,8_-_0.995,74_R_0.976,179_V_0.987 NDUFB9 ENSMLUG00000003765.2 ENSMLUT00000003764.2 10 180 96,66666667 0,108088134 0,4424 1,24592 1,24085 -1570,65544 -1567,125319 0,02930137 -1569,387372 0,033420532 1 I150 NDUFB9 0,0000399 0,005151697 NADH:ubiquinone oxidoreductase subunit B9 The protein encoded by this gene is a subunit of the mitochondrial oxidative phosphorylation complex I (nicotinamide adenine dinucleotide: ubiquinone oxidoreductase). Complex I is localized to the inner mitochondrial membrane and functions to dehydrogenate nicotinamide adenine dinucleotide and to shuttle electrons to coenzyme Q. Complex I deficiency is the most common defect found in oxidative phosphorylation disorders and results in a range of conditions, including lethal neonatal disease, hypertrophic cardiomyopathy, liver disease, and adult-onset neurodegenerative disorders. 420 140 0,27997 0,39454 0,0798 0,2849 0,068983 0,402400833333333 0,18325 0,979878472222222 0,19193 1 7,758 3,2472 3 70_L_0.964,72_N_0.951,139_V_0.942
TOR1AIP1 TOR1AIP1 nsp7 TOR1AIP1 list26_COV_list4dataset2nonOrf torsin 1A interacting protein 1 LAP1|LAP1B|LGMD2Y yes 24 1761 587 0,51702 0,62903 0,1643 0,3177 0,00082508 0,0105885266666667 0,0023145 0,0253382027027027 0,0037125 0,0473736290322581 3,658 4,14571 7 49_L_0.917,139_P_0.969,163_P_0.970,270_R_0.993,324_R_0.907,340_C_0.914,440_R_0.965 TOR1AIP1 ENSMLUG00000006221.2 ENSMLUT00000006225.2 3 598 93,47826087 0,359822155 0,3745 0,66845 0,70424 -3591,897486 -3589,380923 0,080736622 TOR1AIP1 ENSG00000143337.18 ENST00000447964.1 15 295 97,96610169 0,275693739 0,1852 0,49253 0,50872 -1980,718382 -1979,3108 0,244734336 1326 442 0,6327 0,46197 0,1321 0,2088 0,00033621 0,00761416764705882 0,00090021 0,021661303125 0,0016147 0,03885371875 18,37 2,87933 7 24_P_0.955,72_P_0.990,88_P_0.990,120_Y_0.945,149_T_0.942,239_I_0.943,349_F_0.950
MDN1 MDN1 orf7a MDN1 list23_COV_list1orf midasin AAA ATPase 1 Rea1 yes 24 16815 5605 0,3016 0,46605 0,0909 0,3014 0,00096101 0,0119351241935484 0,0016419 0,0203913387096774 0,0074824 0,0872946666666667 1,069 3,56119 13 2971_S_0.982,3062_N_0.945,3154_R_0.952,3572_R_0.954,4012_P_0.957,4048_G_0.986,4079_R_0.955,4362_V_0.948,4385_T_0.930,4401_V_0.948,5015_K_0.977,5020_R_0.954,5290_N_0.930 MDN1 ENSMLUG00000006776.2 ENSMLUT00000006889.2 9 5603 96,76958772 0,151440834 0,3057 0,88231 0,89377 -44274,64223 -44236,81952 3,75E-017 -44269,81604 4,53E-016 3 I1258, R1253, H1262 MDN1 ENSG00000112159.11 ENST00000629399.2 10 5597 98,80293014 0,194996798 0,1398 0,29001 0,29048 -31963,00569 -31961,67182 0,263456239 MDN1 0,630980112 1 14289 4763 0,31705 0,31911 0,065 0,2051 0,058877 0,372389672131148 0,13072 0,7863625 0,19823 1 0,728 3,79785 3 2650_N_0.905,3737_V_0.903,4278_K_0.939
GCC2 GCC2 nsp13 GCC2 list26_COV_list4dataset2nonOrf GRIP and coiled-coil domain containing 2 GCC185|RANBP2L4|REN53 yes 24 5070 1690 0,35761 0,53488 0,1192 0,3332 0,0010147 0,012208109375 0,0024219 0,0253382027027027 0,0038145 0,0473736290322581 0,603 8,0558 4 103_I_0.952,610_H_0.928,801_C_0.958,1534_A_0.955 GCC2 ENSG00000135968.20 ENST00000309863.10 16 1685 91,5727003 0,246708991 0,1682 0,49914 0,50374 -10661,09777 -10659,54568 0,211803992 GCC2 0,235479504 1 4518 1506 0,39793 0,42669 0,1005 0,2527 0,002449 0,0314288333333333 0,010155 0,118475 0,079076 0,7009975 0,394 9,99215 3 93_I_0.975,732_C_0.974,862_P_0.900
ERLEC1 ERLEC1 orf8 ERLEC1 list25_COV_list3dataset2orf endoplasmic reticulum lectin 1 C2orf30|CIM|CL24936|CL25084|HEL117|XTP3-B|XTP3TPB yes 24 1452 484 0,11441 0,31978 0,0361 0,3156 0,0019924 0,0229731764705882 0,0075726 0,0647878 0,0099282 0,10485527027027 0,912 6,7714 3 14_G_0.985,310_P_0.959,318_V_0.984 ERLEC1 ENSMLUG00000016924.2 ENSMLUT00000016927.2 10 496 87,90322581 0,133685784 0,1944 0,46346 0,46745 -2827,547059 -2827,123573 0,654760342 ERLEC1 ENSG00000068912.13 ENST00000378239.5 11 430 99,30232558 0,056894751 0,0834 0,11564 0,11758 -2022,182643 -2021,243196 0,390843912 ERLEC1 1 1 1254 418 0,11955 0,22632 0,0274 0,2296 0,038858 0,311673541666667 0,11694 0,734170161290323 0,1235 0,963556862745098 0,487 9,25976 1 273_V_0.976
FYCO1 FYCO1 nsp13 FYCO1 list26_COV_list4dataset2nonOrf FYVE and coiled-coil domain containing 1 CATC2|CTRCT18|RUFY3|ZFYVE7 yes 24 4437 1479 0,2811 0,60832 0,1223 0,435 0,0020288 0,0229731764705882 0,00034533 0,00582421304347826 0,008976 0,10164 9,973 1,79173 4 447_R_0.971,483_W_0.931,552_M_0.971,928_C_0.963 FYCO1 ENSMLUG00000006996.2 ENSMLUT00000007021.2 4 1482 99,1902834 0,213034363 0,3531 0,61438 0,62398 -8625,1382 -8624,09648 0,352847262 FYCO1 ENSG00000163820.14 ENST00000535325.5 18 1499 91,59439626 0,240037437 0,1681 0,56578 0,58664 -10005,83984 -9983,757336 0,000000000257 -10002,21112 0,00000000124 5 R800, M552, P759, S974, R447 FYCO1 1 1 3528 1176 0,23148 0,38064 0,0706 0,305 1 1 0,82377 1 1 1
GHITM GHITM orf9c GHITM list26_COV_list4dataset2nonOrf growth hormone inducible transmembrane protein DERP2|HSPC282|MICS1|My021|PTD010|TMBIM5 yes 24 1053 351 0,18728 0,54082 0,0787 0,4203 0,0021802 0,0239822 0,0046 0,0431951219512195 0,015535 0,13932625 0,476 7,71826 1 326_G_0.984 GHITM 0,094322531 0,926538721 918 306 0,20065 0,40374 0,0624 0,3108 0,00028814 0,00739559333333333 0,00031764 0,00940703076923077 0,0015619 0,03885371875 0,343 11,59591 1 283_G_0.985
GORASP1 GORASP1 nsp13 GORASP1 list26_COV_list4dataset2nonOrf golgi reassembly stacking protein 1 GOLPH5|GRASP65|P65 yes 24 1368 456 0,35746 0,53431 0,1154 0,3228 0,0022912 0,0245031111111111 0,0081688 0,0683693043478261 0,0092707 0,1019777 0,546 10,57822 1 433_A_0.992 GORASP1 ENSMLUG00000005614.2 ENSMLUT00000005615.2 3 431 90,9512761 0,323245913 0,1242 0,21174 0,37361 -1964,392034 -1961,6331 0,063359273 GORASP1 ENSG00000114745.13 ENST00000441081.1 16 88 100 0,012315818 0,2482 0,35432 0,35435 -461,526226 -461,526882 0,999344215 GORASP1 1 1 1074 358 0,46396 0,39956 0,0986 0,2124 0,00062917 0,01212255 0,0028925 0,05061875 0,0028065 0,0568685526315789 0,449 16,05395 2 186_H_0.921,338_A_0.998
USP54 USP54 nsp12 USP54 list24_COV_list2nonOrf ubiquitin specific peptidase 54 C10orf29|bA137L10.3|bA137L10.4 yes 22 5058 1686 0,42393 0,34784 0,0824 0,1943 0,0024524 0,0252063552631579 0,010016 0,0803366666666667 0,010077 0,10485527027027 0,23 10,3092 1 1491_M_0.992 USP54 ENSMLUG00000012728.2 ENSMLUT00000012728.2 12 1587 87,71266541 0,26879749 0,2314 0,96202 0,96413 -12307,94479 -12306,97869 0,38056511 USP54 ENSG00000166348.18 ENST00000422491.6 13 726 98,48484848 0,723739102 0,1146 0,36231 0,36547 -4514,93199 -4511,186064 0,023613753 -4514,93199 0,006197878 1 M531 USP54 0,970088998 1 4584 1528 0,41709 0,27595 0,0652 0,1563 0,35892 1 0,64101 1 0,65645 1
ATE1 ATE1 nsp8 ATE1 list24_COV_list2nonOrf arginyltransferase 1 - yes 24 1557 519 0,23406 0,42514 0,0736 0,3146 0,0024879 0,0252063552631579 0,0024351 0,0253382027027027 0,010541 0,106796973684211 1,571 4,4187 1 173_V_0.977 ATE1 1 1 1284 428 0,31286 0,26291 0,0555 0,1774 0,46648 1 0,65679 1 0,7674 1
MRPS27 MRPS27 nsp8 MRPS27 list24_COV_list2nonOrf mitochondrial ribosomal protein S27 MRP-S27|S27mt yes 24 1290 430 0,43502 0,57317 0,1371 0,3151 0,0040356 0,0361326976744186 0,014362 0,09966 0,015923 0,13932625 10,158 2,68743 1 423_K_0.978 MRPS27 ENSMLUG00000007355.2 ENSMLUT00000007349.2 10 327 94,18960245 0,219675263 0,5466 1,53511 1,53918 -3189,971077 -3188,848572 0,325463487 MRPS27 ENSG00000113048.16 ENST00000522095.1 17 95 96,84210526 0,505373385 0,1481 0,51248 0,52516 -650,283561 -649,189016 0,334691857 MRPS27 0,956319904 1 1086 362 0,4386 0,42959 0,1022 0,2331 0,0016255 0,02244275 0,0063605 0,087456875 0,0069489 0,10701306 4,544 4,59989 4 6_L_0.923,17_Y_0.927,211_C_0.900,356_K_0.990
VPS39 VPS39 orf3a VPS39 list25_COV_list3dataset2orf VPS39, HOPS complex subunit TLP|VAM6|hVam6p yes 24 2664 888 0,07692 0,33728 0,0244 0,3168 0,0053557 0,0458209888888889 0,0029118 0,0294919871794872 0,021125 0,180736111111111 0,32 6,7796 2 58_S_0.965,616_S_0.922 VPS39 ENSMLUG00000000645.2 ENSMLUT00000000653.2 11 888 93,01801802 0,049487979 0,3105 0,7106 0,71289 -6078,198643 -6078,048493 0,86057888 VPS39 ENSG00000166887.15 ENST00000318006.9 17 876 97,26027397 0,02655803 0,1281 0,27595 0,27723 -4843,388677 -4843,241397 0,863052289 VPS39 0,711489244 1 2259 753 0,08385 0,2275 0,0175 0,2092 0,011857 0,120130131578947 0,019509 0,202999054054054 0,042734 0,439436973684211 0,243 9,68284 1 42_S_0.966
CEP350 CEP350 nsp13 CEP350 list26_COV_list4dataset2nonOrf centrosomal protein 350 CAP350|GM133 yes 24 9372 3124 0,31075 0,43385 0,0913 0,2937 0,0057823 0,0483953369565217 0,0033197 0,0319521125 0,015818 0,13932625 1,591 3,26025 1 1315_A_0.962 CEP350 ENSMLUG00000006005.2 ENSMLUT00000006017.2 11 3123 94,01216779 0,1994026 0,3487 1,02212 1,02439 -25926,89224 -25922,08507 0,008170926 -25922,09229 0,90432485 CEP350 ENSG00000135837.15 ENST00000417046.1 17 582 99,48453608 0,214925457 0,1419 0,37468 0,37468 -3640,619381 -3640,619382 0,999999 CEP350 0,37658998 1 8271 2757 0,37414 0,32907 0,0763 0,204 0,022509 0,201534069767442 0,035304 0,32362 0,076923 0,7009975 3,482 2,74731 4 1026_Y_0.916,1078_A_0.917,1139_T_0.908,2164_S_0.920
ALG11 ALG11 nsp4 ALG11 list26_COV_list4dataset2nonOrf ALG11, alpha-1,2-mannosyltransferase CDG1P|GT8 no 24 1479 493 0,27763 0,40966 0,0809 0,2912 0,007819 0,0627148958333333 0,0090937 0,0744909468085106 0,029234 0,233168571428571 1,814 4,73998 2 6_R_0.921,9_C_0.932 ALG11 ENSMLUG00000028693.1 ENSMLUT00000026687.1 13 493 90,87221095 0,153683232 0,6891 1,58193 1,58193 -4768,261863 -4768,261909 0,999954001 ALG11 ENSG00000253710.2 ENST00000521508.1 18 493 94,92900609 0,167181829 0,1021 0,28339 0,2845 -2735,721105 -2734,190058 0,216309073 ALG11 1 1 1296 432 0,38277 0,28323 0,0665 0,1737 0,00016849 0,00498989615384615 0,00067321 0,0172790566666667 0,00083815 0,0248221346153846 2,029 8,5267 3 2_R_0.991,5_C_0.993,321_Y_0.921
MPHOSPH10 MPHOSPH10 nsp8 MPHOSPH10 list26_COV_list4dataset2nonOrf M-phase phosphoprotein 10 CT90|MPP10|MPP10P|PPP1R106 no 23 2073 691 0,36945 0,40726 0,0942 0,255 0,0081673 0,0641716428571429 0,023765 0,142961328125 0,032106 0,2472162 1,789 5,25551 1 330_G_0.983 MPHOSPH10 ENSG00000124383.8 ENST00000498451.2 16 463 91,57667387 0,455217257 0,1392 0,46529 0,47817 -2872,957619 -2869,397157 0,028425689 -2872,894086 0,008178987 1 N321 MPHOSPH10 1 1 1815 605 0,39528 0,28112 0,0673 0,1703 0,84373 1 0,96246 1 0,98084 1
SLC27A2 SLC27A2 nsp2 SLC27A2 list24_COV_list2nonOrf solute carrier family 27 member 2 ACSVL1|FACVL1|FATP2|HsT17226|VLACS|VLCS|hFACVL1 no 24 1863 621 0,30326 0,57184 0,1143 0,3768 0,0085308 0,06568716 0,013131 0,0936191666666667 0,02786 0,228214893617021 1,006 4,57613 2 415_V_0.954,471_H_0.985 SLC27A2 ENSMLUG00000005760.2 ENSMLUT00000005763.2 8 562 94,66192171 0,238231542 0,38 1,16873 1,19228 -4871,401646 -4860,608444 0,0000205 -4867,625233 0,000179575 4 M177, A66, S190, L191 SLC27A2 ENSG00000140284.10 ENST00000544960.1 11 386 99,22279793 0,273820253 0,1152 0,35661 0,35882 -2334,118749 -2333,392009 0,483482577 SLC27A2 0,975987433 1 1398 466 0,29917 0,41096 0,0826 0,276 1 1 1 1 1 1
ALG8 ALG8 orf9c ALG8 list26_COV_list4dataset2nonOrf ALG8, alpha-1,3-glucosyltransferase CDG1H|PCLD3 no 24 1584 528 0,23611 0,39771 0,0654 0,2771 0,01014 0,0751416346153846 0,021603 0,134147661290323 0,044483 0,311381 0,606 7,79934 1 115_T_0.949 ALG8 ENSMLUG00000014622.2 ENSMLUT00000014631.2 10 526 99,23954373 0,154665671 0,2658 0,71391 0,71672 -3972,78088 -3971,764719 0,361981925 ALG8 ENSG00000159063.12 ENST00000615266.4 16 468 97,86324786 0,244378089 0,1741 0,4149 0,4168 -2961,67908 -2957,461286 0,014731106 -2961,164866 0,00649648 3 R451, Q164, V461 ALG8 0,638317017 1 1398 466 0,30928 0,31283 0,0604 0,1953 0,0016322 0,02244275 0,0067773 0,0899745 0,0076207 0,112844980769231 0,739 12,10586 2 93_T_0.990,276_S_0.936
BCS1L BCS1L orf9c BCS1L list24_COV_list2nonOrf BCS1 homolog, ubiquinol-cytochrome c reductase complex chaperone BCS|BCS1|BJS|FLNMS|GRACILE|Hs.6719|MC3DN1|PTD|h-BCS|h-BCS1 no 22 1260 420 0,12122 0,32645 0,034 0,2808 0,010149 0,0751416346153846 0,03633 0,185225526315789 0,037167 0,27517875 1,669 4,98354 2 295_V_0.934,383_H_0.986 BCS1L ENSMLUG00000001520.2 ENSMLUT00000001514.2 14 418 97,6076555 0,122598675 0,3307 1,00355 1,00415 -3634,450422 -3634,398039 0,948965344 BCS1L ENSG00000074582.12 ENST00000436603.1 18 121 95,04132231 0,043897625 0,2035 0,32812 0,32686 -655,798553 -656,684909 0,412154912 1032 344 0,12904 0,19338 0,0216 0,1671 0,44991 1 1 1 1 1
STOM STOM M STOM list26_COV_list4dataset2nonOrf stomatin BND7|EPB7|EPB72 no 24 867 289 0,16126 0,34963 0,0454 0,2814 0,011191 0,0812931132075472 0,034571 0,179862635135135 0,036725 0,27517875 0,656 8,03423 2 8_A_0.993,268_V_0.930 STOM ENSMLUG00000000077.2 ENSMLUT00000000077.2 15 280 85 0,143064561 0,5232 1,77166 1,77816 -2767,552917 -2752,97994 0,000000469 -2759,929493 0,00019289 4 I19, S12, S166, D14 STOM 0,049087394 0,653548123 711 237 0,19334 0,203 0,0305 0,1579 0,28055 1 0,44112 1 0,55867 1
NUP210 NUP210 nsp4 NUP210 list26_COV_list4dataset2nonOrf nucleoporin 210 GP210|POM210 no 24 5664 1888 0,11937 0,66487 0,0766 0,642 0,011543 0,081942 0,014496 0,09966 1 1 0,115 6,99199 5 915_R_0.990,930_A_0.905,1445_V_0.951,1550_V_0.929,1755_A_0.900 NUP210 ENSMLUG00000008874.2 ENSMLUT00000008938.2 10 1835 98,03814714 0,163033289 0,4134 1,45189 1,46856 -17586,2163 -17576,58963 0,0000659 -17581,93687 0,001074556 NUP210 ENSG00000132182.11 ENST00000254508.6 17 1888 89,77754237 0,12209371 0,221 0,54216 0,54545 -11598,07978 -11595,38623 0,067640051 NUP210 0,735410013 1 4107 1369 0,11458 0,31591 0,036 0,3142 1 1 1 1 1 1
GOLGA2 GOLGA2 nsp13 GOLGA2 list26_COV_list4dataset2nonOrf golgin A2 GM130 no 24 3036 1012 0,18171 0,53387 0,079 0,435 0,011706 0,081942 0,041694 0,205252530864198 0,042572 0,307836018518519 0,12 11,31463 4 719_D_0.975,921_D_0.901,924_A_0.995,927_A_0.908 GOLGA2 1 1 2382 794 0,19743 0,31479 0,0481 0,2435 0,34568 1 0,61267 1 0,63953 1
EDEM3 EDEM3 orf8 EDEM3 list25_COV_list3dataset2orf ER degradation enhancing alpha-mannosidase like protein 3 C1orf22 no 24 2799 933 0,10859 0,28055 0,0285 0,2627 0,011922 0,08196375 0,011881 0,0914837 0,043177 0,307836018518519 0,692 4,73014 2 843_H_0.920,852_T_0.970 EDEM3 ENSMLUG00000013699.2 ENSMLUT00000013715.2 6 865 97,91907514 0,123634396 0,4858 0,76776 0,77242 -5826,650036 -5825,427114 0,294368763 EDEM3 ENSG00000116406.18 ENST00000367512.7 18 906 97,46136865 0,088261229 0,1148 0,24563 0,24588 -4889,688288 -4889,259337 0,651191837 EDEM3 0,993819675 1 2409 803 0,11726 0,19769 0,0218 0,1855 0,05753 0,372389672131148 0,10846 0,71995 0,16368 1 0,487 7,23505 1 725_H_0.919
DCTPP1 DCTPP1 orf9b DCTPP1 list25_COV_list3dataset2orf dCTP pyrophosphatase 1 CDA03|RS21C6|XTP3TPA no 23 513 171 0,29992 0,6262 0,1241 0,4138 0,014497 0,0962300862068965 0,043183 0,205252530864198 0,059901 0,397618706896552 1,589 6,25376 2 13_G_0.991,90_A_0.903 DCTPP1 ENSMLUG00000013774.2 ENSMLUT00000013776.2 13 171 88,88888889 0,228516312 0,5182 1,54428 1,5644 -1611,591443 -1610,897723 0,499713672 DCTPP1 ENSG00000179958.8 ENST00000565758.1 18 50 94 0,365164472 0,23 0,61043 0,87126 -347,668296 -346,081898 0,204661476 DCTPP1 0,936916944 1 351 117 0,36562 0,39354 0,0871 0,2383 0,29593 1 0,57449 1 0,57993 1
GIGYF2 GIGYF2 nsp2 GIGYF2 list26_COV_list4dataset2nonOrf GRB10 interacting GYF protein 2 GYF2|PARK11|PERQ2|PERQ3|TNRC15 no 23 4005 1335 0,10122 0,32394 0,031 0,3065 0,014778 0,0964327118644068 0,030211 0,165459097222222 0,052903 0,357327280701754 1,654 3,20825 3 427_T_0.966,535_A_0.907,1260_P_0.912 GIGYF2 ENSMLUG00000017382.2 ENSMLUT00000017388.2 6 1304 94,47852761 0,106498458 0,3672 0,48706 0,49193 -7612,081579 -7609,775133 0,099614654 GIGYF2 ENSG00000204120.14 ENST00000456491.5 19 68 94,11764706 0,136955684 0,4564 1,17354 1,30632 -492,991734 -490,162606 0,059064335 GIGYF2 1 1 3552 1184 0,10207 0,25472 0,0247 0,242 0,31045 1 0,42863 1 0,60436 1
UBAP2 UBAP2 nsp12 UBAP2 list24_COV_list2nonOrf ubiquitin associated protein 2 UBAP-2 no 24 3384 1128 0,37831 0,50461 0,1092 0,2886 0,015438 0,0990605 0,02833 0,165258333333333 0,061103 0,398722966101695 1,884 3,66493 2 25_M_0.958,469_T_0.905 UBAP2 ENSMLUG00000006809.2 ENSMLUT00000006834.2 8 1130 95,13274336 0,179086694 0,1872 0,49318 0,49317 -7073,846561 -7073,846562 0,999999 UBAP2 ENSG00000137073.21 ENST00000379239.8 17 359 98,05013928 0,238570131 0,1826 0,42619 0,42767 -2270,060113 -2269,804598 0,774517519 UBAP2 1 1 2850 950 0,48524 0,31756 0,0793 0,1634 1 1 0,99885 1 1 1
DDX21 DDX21 N DDX21 list26_COV_list4dataset2nonOrf DExD-box helicase 21 GUA|GURDB|RH-II/GU|RH-II/GuA no 24 2352 784 0,24908 0,39255 0,0714 0,2866 0,016519 0,104259262295082 0,036564 0,185225526315789 0,050843 0,349545625 0,546 8,52013 2 52_F_0.905,148_P_0.960 DDX21 0,039863803 0,586040651 2049 683 0,25239 0,2748 0,051 0,2022 0,011212 0,116665405405405 0,027686 0,26805625 0,035817 0,393987 0,302 21,05797 0 noneOver_0.9
RAB18 RAB18 nsp7 RAB18 list26_COV_list4dataset2nonOrf RAB18, member RAS oncogene family RAB18LI1|WARBM3 no 24 708 236 0,17802 0,30731 0,0468 0,2628 0,017166 0,106595322580645 0,030943 0,165459097222222 1 1 9,427 2,66212 2 67_Q_0.955,83_I_0.988 RAB18 ENSG00000099246.16 ENST00000611151.4 8 183 78,1420765 0,0772 50,06601 50,06601 -1098,885766 -1098,886007 0,999759029 RAB18 0,159860625 1 639 213 0,24266 0,26468 0,0494 0,2034 0,0083667 0,0920337 0,02785 0,26805625 0,0098141 0,136910125 1,061 24,99888 2 56_Q_1.000,72_I_0.999
MARK1 MARK1 orf9b MARK1 list25_COV_list3dataset2orf microtubule affinity regulating kinase 1 MARK|Par-1c|Par1c no 24 2391 797 0,08147 0,24303 0,0194 0,2383 0,018441 0,112695 0,062265 0,272409375 0,14046 0,694036538461539 1,104 3,80076 2 333_I_0.942,515_A_0.962 MARK1 ENSG00000116141.15 ENST00000402574.5 9 781 99,23175416 0,079775242 0,0454 0,08171 0,08187 -3545,187172 -3545,0095 0,837216986 MARK1 0,163968089 1 1980 660 0,10132 0,13152 0,0131 0,1292 0,29796 1 0,54656 1 0,58181 1
PRIM1 PRIM1 nsp1 PRIM1 list24_COV_list2nonOrf DNA primase subunit 1 p49 no 23 1263 421 0,25068 0,30154 0,0544 0,2169 0,025364 0,147956666666667 0,078595 0,305647222222222 0,089922 0,5392625 0,34 10,08588 1 258_G_0.978 PRIM1 ENSMLUG00000016776.2 ENSMLUT00000016785.2 3 423 98,58156028 0,206815922 0,205 0,2646 0,27313 -2146,060778 -2144,796155 0,28234572 PRIM1 1 1 1086 362 0,23839 0,20889 0,0369 0,1547 0,83862 1 0,87813 1 0,97947 1
TIMM29 C19orf52 nsp4 C19orf52 list24_COV_list2nonOrf translocase of inner mitochondrial membrane 29 C19orf52|TIM29 no 20 783 261 0,09802 0,57547 0,0767 0,7829 0,025969 0,1483075 0,048206 0,220944166666667 0,22976 0,972061538461538 1,492 5,48993 2 20_A_0.923,21_V_0.908 TIMM29 ENSMLUG00000011121.2 ENSMLUT00000011135.2 7 228 93,85964912 0,123761455 0,5815 1,05185 1,06124 -1672,344087 -1671,95654 0,678719734 TIMM29 ENSG00000142444.6 ENST00000588807.1 18 27 88,88888889 0,191128523 0,821 1,03241 1,03327 -179,186532 -179,186761 0,999771026 390 130 0,09538 0,45258 0,0517 0,5422 0,089563 0,499735579710145 0,23265 1 1 1 0,749 23,42706 1 8_V_0.916
ZNF318 ZNF318 nsp12 ZNF318 list24_COV_list2nonOrf zinc finger protein 318 HRIHFB2436|TZF|ZFP318 no 24 6855 2285 0,29861 0,4399 0,0859 0,2878 0,026812 0,1483075 0,038536 0,19268 0,091187 0,5392625 0,872 4,01109 2 1482_V_0.972,1911_P_0.901 ZNF318 ENSMLUG00000009010.2 ENSMLUT00000009012.2 8 2177 89,98621957 0,282912504 0,2315 0,69261 0,69462 -14715,78766 -14714,17339 0,199036312 ZNF318 ENSG00000171467.15 ENST00000361428.2 11 2280 94,60526316 0,268984219 0,0914 0,29951 0,38485 -12518,15852 -12328,25031 3,34E-083 -12516,52556 7,01E-084 32 R66, R67, S71, P73, R74, R55, R76, R77, V78, S81, R86, R87, S29, A36, R37, R38, S39, S40, P41, S46, S49, S50, R51, T52, P53, A54, V1481, R56, R58, S59, S61, H63 ZNF318 0,013754741 0,318245251 5829 1943 0,35013 0,33866 0,0731 0,2088 0,0047949 0,0559405 0,013839 0,152229 0,020265 0,251678225806452 0,378 7,65838 3 1214_V_0.988,1505_A_0.931,1609_P_0.941
ALG5 ALG5 orf3a ALG5 list25_COV_list3dataset2orf ALG5, dolichyl-phosphate beta-glucosyltransferase bA421P11.2 no 22 975 325 0,19352 0,43688 0,0648 0,3349 0,026835 0,1483075 0,042491 0,205252530864198 0,090111 0,5392625 0,916 6,16754 1 3_P_0.943 ALG5 ENSMLUG00000014698.2 ENSMLUT00000028515.1 4 329 87,23404255 0,22019498 1,6961 1,54406 12,28528 -2035,78195 -2008,520181 0,00000000000145 -2034,445957 0,000000000000599 11 N192, S195, S197, C198, S200, F201, F202, C203, I204, E205, Q207 ALG5 ENSG00000120697.8 ENST00000443765.5 18 295 98,98305085 0,174781993 0,1284 0,40079 0,41228 -1862,583277 -1860,189329 0,091268643 792 264 0,15885 0,2645 0,0353 0,2224 1 1 1 1 0,99997 1
EXOSC3 EXOSC3 nsp8 EXOSC3 list24_COV_list2nonOrf exosome component 3 CGI-102|PCH1B|RRP40|Rrp40p|bA3J10.7|hRrp-40|p10 no 24 834 278 0,20018 0,45795 0,0679 0,339 0,026965 0,1483075 0,064948 0,27478 0,086098 0,534640806451613 5,592 2,83124 4 4_G_0.946,5_V_0.983,60_C_0.964,97_G_0.943 EXOSC3 ENSG00000107371.12 ENST00000396521.3 19 165 92,72727273 0,218656939 0,1801 0,60416 0,62059 -1150,429321 -1148,780749 0,192324352 EXOSC3 1 1 636 212 0,1116 0,24064 0,0246 0,2204 0,1418 0,6824125 0,33955 1 0,33824 1
UBXN8 UBXN8 orf9c UBXN8 list24_COV_list2nonOrf UBX domain protein 8 D8S2298E|REP8|UBXD6 no 24 813 271 0,50084 0,84159 0,2194 0,438 0,028106 0,152405774647887 0,053229 0,23829261627907 0,092445 0,5392625 3,737 3,57202 1 186_-_0.936 UBXN8 ENSMLUG00000016846.2 ENSMLUT00000016850.2 6 276 97,46376812 0,274309794 0,2437 0,48252 0,48252 -1701,535277 -1701,535285 0,999992 UBXN8 ENSG00000104691.14 ENST00000518059.5 16 94 91,4893617 0,304674755 0,1752 0,52166 0,52167 -582,550308 -582,550748 0,999560097 663 221 0,62671 0,63926 0,1834 0,2926 0,0023872 0,0314288333333333 0,0071576 0,0918558666666667 0,0099571 0,136910125 2,326 6,34799 2 160_-_0.982,161_-_0.963
PABPC1 PABPC1 N PABPC1 list26_COV_list4dataset2nonOrf poly(A) binding protein cytoplasmic 1 PAB1|PABP|PABP1|PABPC2|PABPL1 no 22 1920 640 0,25446 0,48967 0,0895 0,3517 0,029875 0,157559931506849 0,060395 0,267265229885057 0,13375 0,686583333333333 0,624 7,12936 0 noneOver_0.9 1467 489 0,26057 0,3403 0,0636 0,2441 0,76986 1 0,93921 1 0,97767 1
LMAN2 LMAN2 nsp7 LMAN2 list26_COV_list4dataset2nonOrf lectin, mannose binding 2 C5orf8|GP36B|VIP36 no 24 1071 357 0,04915 0,43971 0,0317 0,6451 0,030744 0,159951891891892 0,015562 0,103684482758621 0,095872 0,550906268656716 1,959 3,00968 1 9_L_0.955 LMAN2 ENSMLUG00000009409.2 ENSMLUT00000009408.2 12 360 98,05555556 0,061497675 0,4011 1,01052 1,14983 -2800,905918 -2791,547389 0,0000862 -2798,830566 0,000135309 1 S147 LMAN2 ENSG00000169223.14 ENST00000515209.5 12 326 99,0797546 0,126566544 0,1801 0,33031 0,33033 -1883,954561 -1883,954464 0,999903005 LMAN2 0,011501474 0,286979492 711 237 0,09618 0,26238 0,0307 0,3192 0,32704 1 0,5888 1 0,61587 1
RIPK1 RIPK1 nsp12 RIPK1 list24_COV_list2nonOrf receptor interacting serine/threonine kinase 1 RIP|RIP-1|RIP1 no 24 2016 672 0,38791 0,79006 0,182 0,4692 0,031589 0,162156866666667 0,030341 0,165459097222222 0,10033 0,56012 17,918 1,58393 1 664_H_0.951 RIPK1 ENSMLUG00000016163.2 ENSMLUT00000016166.2 11 669 94,76831091 0,230770476 0,5873 1,61018 1,63436 -6898,757894 -6891,727408 0,000884502 -6897,079258 0,001069215 1 L551 RIPK1 ENSG00000137275.13 ENST00000380409.2 15 672 99,10714286 0,277738169 0,2016 0,62393 0,6255 -5051,481263 -5051,29152 0,82717169 RIPK1 0,805490382 1 1521 507 0,44562 0,52472 0,1298 0,2913 0,020117 0,184405833333333 0,04123 0,369152325581395 0,067245 0,663828846153846 8,033 2,75059 0 noneOver_0.9
JAKMIP1 JAKMIP1 nsp13 JAKMIP1 list26_COV_list4dataset2nonOrf janus kinase and microtubule interacting protein 1 Gababrbp|JAMIP1|MARLIN1 no 24 2496 832 0,01403 0,35654 0,0073 0,5234 0,032632 0,165306842105263 0,0018532 0,0222963125 0,10161 0,56012 0,129 5,15252 1 277_V_0.959 1845 615 0,02062 0,15767 0,0044 0,2154 1 1 1 1 0,99972 1
TARS2 TARS2 M TARS2 list26_COV_list4dataset2nonOrf threonyl-tRNA synthetase 2, mitochondrial COXPD21|TARSL1|thrRS no 24 2160 720 0,30484 0,52026 0,0988 0,3242 0,034329 0,171645 0,078453 0,305647222222222 0,13216 0,686583333333333 1,215 4,31287 2 173_I_0.958,638_R_0.901 TARS2 ENSMLUG00000013123.2 ENSMLUT00000013127.2 4 722 99,58448753 0,129341991 0,2712 0,32527 0,32527 -3826,701492 -3826,701494 0,999998 TARS2 1 1 1644 548 0,31786 0,29867 0,058 0,1825 0,8519 1 0,94738 1 0,98258 1
NDUFAF1 NDUFAF1 orf9c NDUFAF1 list24_COV_list2nonOrf NADH:ubiquinone oxidoreductase complex assembly factor 1 CGI-65|CGI65|CIA30 no 25 984 328 0,41096 0,68131 0,1695 0,4123 0,038447 0,187368291139241 0,10122 0,37114 0,11757 0,628672916666667 19,079 1,7727 6 74_S_0.952,91_I_0.910,178_T_0.906,179_R_0.900,198_M_0.919,275_P_0.934 NDUFAF1 ENSMLUG00000001623.1 ENSMLUT00000001622.1 14 328 96,34146341 0,234797941 0,2964 1,11821 1,11923 -3070,494797 -3070,184718 0,733389016 NDUFAF1 ENSG00000137806.8 ENST00000558719.1 18 191 100 0,673846993 0,1447 0,60117 0,61052 -1423,781138 -1420,81047 0,051269051 828 276 0,51821 0,49325 0,1357 0,2619 0,024753 0,211775666666667 0,078772 0,583215769230769 0,080114 0,7009975 17,973 2,23814 3 67_S_0.970,119_T_0.910,170_M_0.956
PCSK6 PCSK6 orf8 PCSK6 list25_COV_list3dataset2orf proprotein convertase subtilisin/kexin type 6 PACE4|SPC4 no 24 2940 980 0,08851 0,61083 0,0574 0,6487 0,044184 0,2126355 0,030184 0,165459097222222 0,30045 1 0,343 4,71921 3 22_-_0.979,30_-_0.925,717_N_0.941 PCSK6 1 1 1899 633 0,11842 0,25851 0,0308 0,26 0,40439 1 0,70637 1 0,86169 1
ANO6 ANO6 M ANO6 list24_COV_list2nonOrf anoctamin 6 BDPLT7|SCTS|TMEM16F no 24 2799 933 0,12803 0,3476 0,0389 0,3035 0,045415 0,215355853658537 0,11244 0,397397706422018 0,14061 0,694036538461539 0,79 4,00199 3 450_V_0.984,808_A_0.915,866_I_0.967 ANO6 ENSMLUG00000007076.2 ENSMLUT00000007096.2 8 841 97,97859691 0,09941969 0,4679 1,0014 1,00509 -6738,651994 -6737,916199 0,479124404 ANO6 ENSG00000177119.15 ENST00000441606.2 15 893 91,04143337 0,08014419 0,1228 0,25956 0,26133 -4546,756228 -4544,092163 0,069664459 ANO6 0,693976732 1 2394 798 0,11791 0,20692 0,0227 0,1926 0,89963 1 0,94901 1 0,9864 1
SNIP1 SNIP1 N SNIP1 list26_COV_list4dataset2nonOrf Smad nuclear interacting protein 1 PML1|PMRED no 24 1194 398 0,16858 0,42404 0,0573 0,3397 0,045868 0,215355853658537 0,089309 0,337097696078431 0,13727 0,694036538461539 3,401 3,00912 4 57_T_0.938,61_G_0.922,153_T_0.951,221_A_0.938 SNIP1 ENSG00000163877.10 ENST00000468040.2 8 69 84,05797101 0,432387306 1,736 57,02687 47,24559 -750,072181 -749,727467 0,708422933 849 283 0,13566 0,21434 0,0284 0,209 0,32741 1 0,56139 1 0,64496 1
DNMT1 DNMT1 orf8 DNMT1 list23_COV_list1orf DNA methyltransferase 1 ADCADN|AIM|CXXC9|DNMT|HSN1E|MCMT|m.HsaI no 24 4899 1633 0,07087 0,56215 0,0439 0,6195 0,049362 0,228968313253012 0,019073 0,122385083333333 0,022625 0,189361413043478 1,08 3,39424 6 15_V_0.928,121_G_0.900,151_R_0.934,154_P_0.921,162_G_0.937,1419_A_0.905 DNMT1 1 1 3432 1144 0,13202 0,24532 0,0322 0,2437 0,00003574 0,0013863465 0,000096131 0,0037010435 0,00020218 0,00864881111111111 0,765 12,91917 7 90_G_0.976,112_P_0.981,120_G_0.990,123_A_0.936,208_P_0.914,250_S_0.918,700_L_0.905
PRKAR2B PRKAR2B nsp13 PRKAR2B list26_COV_list4dataset2nonOrf protein kinase cAMP-dependent type II regulatory subunit beta PRKAR2|RII-BETA no 23 1266 422 0,12042 0,30311 0,0326 0,2704 0,053419 0,244624470588235 0,15359 0,484689754098361 0,19343 0,886554166666667 0,44 8,4592 1 61_A_0.983 PRKAR2B ENSMLUG00000012418.2 ENSMLUT00000012421.2 11 305 99,01639344 0,022501629 0,2602 0,46327 0,46328 -1820,904966 -1820,90569 0,999276262 972 324 0,08201 0,16457 0,014 0,1708 0,687 1 0,79637 1 0,92204 1
PLEKHA5 PLEKHA5 nsp12 PLEKHA5 list24_COV_list2nonOrf pleckstrin homology domain containing A5 PEPP-2|PEPP2 no 21 3861 1287 0,16454 0,35837 0,0506 0,3074 0,054008 0,244624470588235 0,07154 0,286905208333333 0,15651 0,76076 4,723 2,05716 3 365_A_0.900,1205_Y_0.918,1223_P_0.911 3297 1099 0,20673 0,25036 0,0417 0,2016 0,012349 0,121906794871795 0,043068 0,376845 0,043373 0,439436973684211 3,983 3,18246 6 316_A_0.963,344_N_0.944,763_I_0.950,1034_T_0.956,1035_Y_0.970,1049_P_0.967
FKBP15 FKBP15 nsp2 FKBP15 list24_COV_list2nonOrf FK506 binding protein 15 FKBP133|KIAA0674|PPP1R76 no 24 3669 1223 0,36084 0,47497 0,1016 0,2815 0,055782 0,24535 0,11876 0,411370796460177 0,15808 0,76076 11,532 1,87074 2 970_Q_0.929,1040_L_0.933 FKBP15 ENSMLUG00000015278.2 ENSMLUT00000015293.2 9 1211 93,22873658 0,283789285 0,2849 1,02448 1,02768 -9968,379835 -9967,309891 0,343027726 FKBP15 ENSG00000119321.8 ENST00000238256.7 18 1220 96,8852459 0,276092563 0,1507 0,4982 0,49595 -8411,546874 -8409,134261 0,089580914 FKBP15 0,822906741 1 3096 1032 0,44951 0,30322 0,0738 0,1642 1 1 1 1 1 1
POGLUT3 KDELC2 orf8 KDELC2 list23_COV_list1orf KDEL motif containing 2 - no 24 1524 508 0,19302 0,44994 0,0662 0,3428 0,057575 0,249060393258427 0,046571 0,216022108433735 0,16748 0,796046913580247 0,531 4,88598 0 noneOver_0.9 KDELC2 1 1 1173 391 0,17366 0,28161 0,0382 0,2198 1 1 1 1 0,99981 1
FBN1 FBN1 nsp9 FBN1 list24_COV_list2nonOrf fibrillin 1 ACMICD|ECTOL1|FBN|GPHYSD2|MASS|MFLS|MFS1|OCTD|SGS|SSKS|WMS|WMS2 no 24 8622 2874 0,03922 0,35339 0,0151 0,385 0,060761 0,259155769230769 0,0051117 0,04685725 0,19201 0,886554166666667 0,191 3,70837 2 10_L_0.982,125_M_0.908 FBN1 1 1 6828 2276 0,05084 0,19457 0,0115 0,227 0,90797 1 0,72665 1 0,99503 1
NUP88 NUP88 nsp9 NUP88 list24_COV_list2nonOrf nucleoporin 88 - no 24 2232 744 0,19123 0,48813 0,0732 0,3826 0,061255 0,259155769230769 0,052061 0,235805705882353 0,25163 1 0,57 4,54814 0 noneOver_0.9 NUP88 0,811881094 1 1824 608 0,1829 0,32802 0,0493 0,2694 1 1 1 1 0,99981 1
ATP13A3 ATP13A3 nsp6 ATP13A3 list24_COV_list2nonOrf ATPase 13A3 AFURS1 no 23 3681 1227 0,14578 0,37782 0,0489 0,3357 0,06395 0,267616847826087 0,17447 0,525615234375 0,211 0,93789540229885 0,081 16,27142 3 1050_Q_0.963,1116_I_0.936,1158_L_0.901 ATP13A3 ENSMLUG00000014164.2 ENSMLUT00000014172.2 11 1227 99,75550122 0,13286808 0,2049 0,59575 0,59732 -8654,442737 -8653,486754 0,38443406 ATP13A3 ENSG00000133657.14 ENST00000619199.4 13 702 97,57834758 0,089688782 0,1611 0,35125 0,35206 -4002,934816 -4001,403875 0,216332003 ATP13A3 0,649032564 1 3153 1051 0,19374 0,26814 0,0421 0,2172 0,43893 1 0,66813 1 0,74099 1
TRIM59 TRIM59 orf3a TRIM59 list23_COV_list1orf tripartite motif containing 59 IFT80L|MRF1|RNF104|TRIM57|TSBF1 no 22 1212 404 0,41501 0,51665 0,1273 0,3068 0,069322 0,286978172043011 0,13013 0,431896982758621 0,19086 0,886554166666667 9,802 2,27273 1 162_H_0.953 TRIM59 ENSMLUG00000026537.1 ENSMLUT00000029823.1 15 403 99,00744417 0,265264911 0,2441 1,15001 1,15339 -3946,982058 -3944,94036 0,129808109 1074 358 0,4239 0,38412 0,095 0,2242 0,75049 1 0,87123 1 0,95092 1
NPC2 NPC2 orf8 NPC2 list23_COV_list1orf NPC intracellular cholesterol transporter 2 EDDM1|HE1 no 24 465 155 0,28349 0,66431 0,128 0,4516 0,073109 0,29943579787234 0,12039 0,411370796460177 0,20056 0,908418823529412 16,901 1,89121 3 7_T_0.975,12_A_0.902,134_R_0.915 NPC2 ENSMLUG00000004558.2 ENSMLUT00000004553.2 8 188 80,31914894 0,409691319 0,3524 1,11719 1,11731 -1372,452736 -1372,452748 0,999988 NPC2 ENSG00000119655.10 ENST00000555592.1 19 122 100 0,109616622 0,2467 0,53262 0,57178 -845,865194 -844,02272 0,158424997 NPC2 1 1 363 121 0,33817 0,51879 0,1093 0,3233 0,00058009 0,01212255 0,002644 0,0502846666666667 0,0026863 0,0568685526315789 12,619 3,38544 4 3_T_0.996,56_V_0.961,103_R_0.971,117_Q_0.953
MEPCE MEPCE nsp8 MEPCE list24_COV_list2nonOrf methylphosphate capping enzyme BCDIN3 no 22 2079 693 0,15687 0,42088 0,0557 0,355 0,074341 0,301276684210526 0,14289 0,466208898305085 0,21194 0,93789540229885 0,779 5,11823 4 64_-_0.976,286_L_0.925,377_A_0.937,508_P_0.923 MEPCE ENSMLUG00000009678.2 ENSMLUT00000009684.2 8 681 92,80469897 0,157081921 0,2342 0,5599 0,57057 -4422,638039 -4418,472185 0,015516458 -4422,297898 0,005672746 MEPCE 0,000504613 0,034161031 methylphosphate capping enzyme 1518 506 0,12137 0,2415 0,027 0,2222 0,1999 0,894901162790698 0,38513 1 1 1
PLAT PLAT orf8 PLAT list25_COV_list3dataset2orf plasminogen activator, tissue type T-PA|TPA no 23 1692 564 0,16564 0,73992 0,1154 0,6966 0,075208 0,301615416666667 0,0249 0,147484615384615 0,28123 1 5,053 1,97012 1 21_S_0.930 PLAT ENSMLUG00000002013.2 ENSMLUT00000002012.2 11 565 98,9380531 0,230998728 0,4105 1,29381 1,29504 -5666,617304 -5666,585307 0,968509488 PLAT ENSG00000104368.17 ENST00000521694.1 19 157 98,72611465 0,161425106 0,3466 0,85212 0,86128 -1242,278572 -1241,57666 0,49563674 PLAT 0,037451878 0,564196083 1119 373 0,15523 0,33843 0,0533 0,3433 0,61478 1 0,60784 1 0,8822 1
EXOSC8 EXOSC8 nsp8 EXOSC8 list24_COV_list2nonOrf exosome component 8 CIP3|EAP2|OIP2|PCH1C|RRP43|Rrp43p|bA421P11.3|p9 no 23 831 277 0,16492 0,41639 0,061 0,37 0,08173 0,324392268041237 0,11356 0,39746 0,21814 0,9543625 7,555 2,27778 2 70_S_0.979,76_K_0.926 EXOSC8 0,763915432 1 732 244 0,1998 0,30907 0,0519 0,2596 0,0037775 0,0469141129032258 0,0099798 0,118475 0,014981 0,192256166666667 4,977 4,53395 6 57_S_0.996,58_A_0.929,59_P_0.902,60_D_0.911,61_K_0.972,62_G_0.924
CEP135 CEP135 nsp13 CEP135 list26_COV_list4dataset2nonOrf centrosomal protein 135 CEP4|KIAA0635|MCPH8 no 24 3423 1141 0,23978 0,39423 0,0725 0,3023 0,083372 0,327532857142857 0,15305 0,484689754098361 0,22336 0,966220224719101 10,668 1,77574 3 359_Q_0.922,485_I_0.928,599_S_0.949 CEP135 ENSMLUG00000015028.2 ENSMLUT00000015042.2 8 1140 99,38596491 0,16289216 0,2985 0,64712 0,64712 -7835,689032 -7835,689038 0,999994 CEP135 ENSG00000174799.10 ENST00000257287.4 17 1141 99,56178791 0,171373197 0,1243 0,32228 0,32336 -6728,250497 -6728,014371 0,789681168 CEP135 0,02680381 0,468831557 3054 1018 0,26589 0,30015 0,0586 0,2205 0,043436 0,3372446 0,07069 0,544313 0,13152 0,973753846153846 6,965 2,45299 4 431_I_0.956,535_S_0.968,609_Y_0.926,673_K_0.910
GCC1 GCC1 nsp13 GCC1 list26_COV_list4dataset2nonOrf GRIP and coiled-coil domain containing 1 GCC1P|GCC88 no 23 2328 776 0,13435 0,47829 0,0579 0,431 0,093842 0,36131865 0,067899 0,284142554347826 0,24919 1 3,021 2,19133 1 209_A_0.947 GCC1 ENSMLUG00000017118.2 ENSMLUT00000017123.2 13 776 98,58247423 0,123138883 0,3387 1,04423 1,05336 -6797,078765 -6795,84013 0,289779497 GCC1 ENSG00000179562.2 ENST00000321407.2 18 776 99,61340206 0,103231565 0,1821 0,43935 0,44071 -5024,601814 -5023,7418 0,423156158 GCC1 1 1 1902 634 0,15695 0,34656 0,0463 0,295 0,059002 0,372389672131148 0,091124 0,64968037037037 0,17135 1 0,651 5,63198 1 161_A_0.942
MIPOL1 MIPOL1 nsp13 MIPOL1 list26_COV_list4dataset2nonOrf mirror-image polydactyly 1 CCDC193 no 24 1329 443 0,44036 0,37986 0,0967 0,2195 0,093849 0,36131865 0,22859 0,6286225 0,24618 1 7,899 3,11818 0 noneOver_0.9 MIPOL1 ENSG00000151338.18 ENST00000545536.5 18 425 97,41176471 0,574467255 0,1035 0,39646 0,42094 -2643,795389 -2637,189776 0,001352754 -2643,657324 0,000322479 2 R9, C20 MIPOL1 0,371779525 1 1218 406 0,50665 0,30905 0,0832 0,1643 0,095599 0,5257945 0,23561 1 0,24936 1 8,327 3,60215 1 46_C_0.905
YIF1A YIF1A M YIF1A list26_COV_list4dataset2nonOrf Yip1 interacting factor homolog A, membrane trafficking protein 54TM|FinGER7|YIF1|YIF1P no 24 882 294 0,12933 0,36727 0,0442 0,3414 0,096034 0,366070198019802 0,2503 0,664589655172414 0,34712 1 0,448 10,38547 1 43_P_0.926 YIF1A ENSG00000174851.15 ENST00000528575.1 19 160 85 0,165053289 0,1698 0,2852 0,31847 -784,604746 -783,79045 0,442951055 YIF1A 1 1 690 230 0,14733 0,2433 0,0324 0,2198 0,60503 1 0,82915 1 0,89112 1
AASS AASS M AASS list26_COV_list4dataset2nonOrf aminoadipate-semialdehyde synthase LKR/SDH|LKRSDH|LORSDH no 23 2781 927 0,15824 0,42179 0,0559 0,3531 0,097344 0,367425882352941 0,0774 0,305647222222222 0,26252 1 2,274 2,39714 2 5_R_0.927,777_D_0.910 AASS ENSMLUG00000015957.2 ENSMLUT00000015979.2 9 819 100 0,141477444 0,3292 0,90534 0,91599 -6669,360229 -6664,828436 0,010761364 -6667,008435 0,036792348 AASS 0,075255753 0,819592694 2364 788 0,18941 0,27822 0,0426 0,2248 1 1 1 1 0,33201 1
GLA GLA nsp14 GLA list26_COV_list4dataset2nonOrf galactosidase alpha GALA no 23 1290 430 0,34628 0,41456 0,0914 0,2638 0,1031 0,385373786407767 0,2231 0,622416666666667 0,26738 1 GLA ENSMLUG00000004352.2 ENSMLUT00000004355.2 7 432 92,36111111 0,245535203 0,2592 0,65034 0,65384 -2848,543761 -2846,725247 0,1622667 GLA 0,000645969 0,039577345 galactosidase alpha This gene encodes a homodimeric glycoprotein that hydrolyses the terminal alpha-galactosyl moieties from glycolipids and glycoproteins. This enzyme predominantly hydrolyzes ceramide trihexoside, and it can catalyze the hydrolysis of melibiose into galactose and glucose. A variety of mutations in this gene affect the synthesis, processing, and stability of this enzyme, which causes Fabry disease, a rare lysosomal storage disorder that results from a failure to catabolize alpha-D-galactosyl glycolipid moieties. 1122 374 0,39143 0,30398 0,0721 0,1841 1 1 1 1 0,71453 1
FAM162A FAM162A nsp7 FAM162A list26_COV_list4dataset2nonOrf family with sequence similarity 162 member A C3orf28|E2IG5|HGTD-P no 24 468 156 0,78824 0,79174 0,2465 0,3127 0,10955 0,405545673076923 0,25724 0,669561824324324 0,28032 1 FAM162A ENSMLUG00000000941.2 ENSMLUT00000000941.2 12 156 75,64102564 0,444238485 0,4761 1,8729 1,88327 -1382,256543 -1381,452888 0,447689664 FAM162A ENSG00000114023.15 ENST00000469967.1 18 142 76,05633803 0,709364639 0,203 0,6908 0,69927 -839,142618 -836,007332 0,043487315 -839,131843 0,01242619 2 D65, F117 FAM162A 0,871744117 1 372 124 1,22917 0,56264 0,1969 0,1602 0,046286 0,339787924528302 0,072164 0,544767450980392 0,072172 0,677712682926829 60,853 2,14101 2 53_D_0.922,82_I_0.965
FBXL12 FBXL12 orf8 FBXL12 list23_COV_list1orf F-box and leucine rich repeat protein 12 Fbl12 no 24 981 327 0,06466 0,62178 0,05 0,7731 0,11156 0,409053333333333 0,030557 0,165459097222222 0,30813 1 1,298 2,70583 2 57_K_0.913,264_V_0.951 FBXL12 ENSG00000127452.8 ENST00000586073.1 16 135 91,85185185 0,574873298 0,1529 0,57689 0,58283 -925,793612 -925,653943 0,869646041 FBXL12 1 1 735 245 0,06852 0,3738 0,0315 0,4602 0,067353 0,398937 0,16258 0,910776086956522 0,21116 1 0,49 13,3344 1 35_K_0.953
EXOSC5 EXOSC5 nsp8 EXOSC5 list24_COV_list2nonOrf exosome component 5 RRP41B|RRP46|Rrp46p|hRrp46p|p12B no 24 708 236 0,14785 0,54807 0,0734 0,4966 0,11544 0,414623611111111 0,12856 0,430396521739131 0,30715 1 EXOSC5 ENSMLUG00000013104.2 ENSMLUT00000013103.2 11 236 96,61016949 0,119799705 0,3784 1,00915 1,01642 -1908,268348 -1908,145027 0,883979859 EXOSC5 ENSG00000077348.8 ENST00000593771.1 16 133 98,4962406 0,243215764 0,1784 0,66897 0,72757 -1008,164727 -1003,142205 0,006587891 -1007,649985 0,002676909 2 S91, W93 EXOSC5 1 1 474 158 0,14589 0,2939 0,0403 0,2765 0,03632 0,297514893617021 0,10835 0,71995 0,12433 0,963556862745098 1,425 7,51103 1 8_I_0.959
NGDN NGDN nsp8 NGDN list24_COV_list2nonOrf neuroguidin C14orf120|CANu1|LCP5|NGD|lpd-2 no 24 948 316 0,30905 0,40311 0,0812 0,2628 0,11631 0,414623611111111 0,24592 0,657494444444444 0,28287 1 NGDN ENSMLUG00000017232.2 ENSMLUT00000017233.2 11 320 89,375 0,152354448 0,3111 1,03219 1,03483 -2523,840779 -2523,396709 0,64142052 NGDN ENSG00000129460.15 ENST00000556699.1 16 75 96 0,340887187 0,1089 0,25673 0,29094 -404,053622 -401,716208 0,096577064 NGDN 1 1 795 265 0,39216 0,31231 0,0715 0,1822 0,32061 1 0,57456 1 0,61019 1
ATP6AP1 ATP6AP1 nsp6 ATP6AP1 list26_COV_list4dataset2nonOrf ATPase H+ transporting accessory protein 1 16A|ATP6IP1|ATP6S1|Ac45|CF2|VATPS1|XAP-3|XAP3 no 24 1443 481 0,13691 0,58173 0,0767 0,5605 0,12109 0,427385 0,28155 0,722645 1 1 ATP6AP1 ENSG00000071553.16 ENST00000619046.4 18 282 91,4893617 0,096121814 0,2011 0,41865 0,41897 -1572,757194 -1572,738926 0,981897848 ATP6AP1 0,421511211 1 975 325 0,16921 0,29404 0,0445 0,263 0,13705 0,676464743589744 0,29241 1 0,37443 1
LARP7 LARP7 nsp8 LARP7 list24_COV_list2nonOrf La ribonucleoprotein domain family member 7 ALAZS|HDCMA18P|PIP7S no 22 1755 585 0,16551 0,39095 0,0597 0,3609 0,12211 0,427385 0,17148 0,525615234375 1 1 LARP7 ENSMLUG00000027214.1 ENSMLUT00000026813.1 8 503 89,46322068 0,136163316 1,0096 2,02982 2,04874 -4890,2203 -4888,005039 0,109125029 1572 524 0,13884 0,31163 0,0435 0,3131 0,56334 1 0,43042 1 0,84622 1
SIL1 SIL1 orf8 SIL1 list23_COV_list1orf SIL1 nucleotide exchange factor BAP|MSS|ULG5 no 24 1386 462 0,15942 0,59934 0,0829 0,52 0,12745 0,434232300884956 1 1 1 1 SIL1 ENSMLUG00000005198.2 ENSMLUT00000005199.2 11 385 98,44155844 0,116118369 0,3877 1,00231 1,00231 -3151,512186 -3151,512306 0,999880007 SIL1 ENSG00000120725.12 ENST00000505353.1 19 94 90,42553191 0,221006576 0,2776 0,74645 0,74672 -692,585908 -692,585983 0,999925003 1059 353 0,20076 0,29004 0,047 0,2343 1 1 1 1 0,57402 1
NUP62 NUP62 nsp9 NUP62 list24_COV_list2nonOrf nucleoporin 62 IBSN|SNDI|p62 no 24 1599 533 0,18558 0,8424 0,1273 0,6861 0,12962 0,437751754385965 0,1473 0,4725875 0,52476 1 996 332 0,19473 0,37067 0,061 0,3134 0,36086 1 0,49174 1 0,6569 1
NDFIP2 NDFIP2 orf9c NDFIP2 list24_COV_list2nonOrf Nedd4 family interacting protein 2 N4WBP5A no 24 1017 339 0,26963 0,48829 0,0892 0,3307 0,1407 0,466978448275862 0,22078 0,620440145985401 0,32204 1 747 249 0,22175 0,23705 0,0397 0,179 1 1 1 1 0,69866 1
COLGALT1 COLGALT1 nsp1 COLGALT1 list26_COV_list4dataset2nonOrf collagen beta(1-O)galactosyltransferase 1 ColGalT 1|GLT25D1 no 24 1869 623 0,04239 0,70448 0,0442 1,0436 0,14548 0,478716239316239 0,017881 0,116681101694915 1 1 0,753 2,62207 1 547_H_0.957 COLGALT1 ENSMLUG00000004442.2 ENSMLUT00000004449.2 5 629 97,13831479 0,076213473 0,3899 0,71621 0,71708 -3712,931218 -3712,921308 0,990138942 COLGALT1 ENSG00000130309.10 ENST00000597147.1 17 217 100 0,024196018 0,3142 0,62531 0,62533 -1376,826978 -1376,827961 0,999017483 COLGALT1 1 1 1119 373 0,04579 0,38495 0,0277 0,6059 0,18517 0,848695833333333 0,30292 1 0,40115 1
ECSIT ECSIT orf9c ECSIT list24_COV_list2nonOrf ECSIT signalling integrator SITPEC no 24 1296 432 0,31623 0,90186 0,2021 0,6392 0,15025 0,488205882352941 0,24592 0,657494444444444 0,37676 1 ECSIT ENSMLUG00000016345.2 ENSMLUT00000016348.2 15 435 85,74712644 0,221887414 0,4374 1,97602 2,01283 -4572,631364 -4571,373419 0,284237535 ECSIT ENSG00000130159.13 ENST00000586149.1 17 24 91,66666667 0,383453897 0,1253 0,56044 0,59422 -148,405029 -148,085679 0,726621187 ECSIT 0,235323181 1 885 295 0,30601 0,50394 0,1098 0,3589 0,76327 1 0,94186 1 1 1
EMC1 EMC1 orf8 EMC1 list25_COV_list3dataset2orf ER membrane protein complex subunit 1 CAVIPMR|KIAA0090 no 22 2991 997 0,11747 0,43283 0,046 0,3913 0,1509 0,488205882352941 0,21129 0,600741544117647 0,38274 1 EMC1 ENSMLUG00000001556.2 ENSMLUT00000001561.2 11 998 96,89378758 0,082617467 0,3467 0,90202 0,91226 -7919,562518 -7916,895299 0,069445084 EMC1 0,810838121 1 2442 814 0,0991 0,26514 0,025 0,2525 0,44106 1 0,73813 1 0,74199 1
AP3B1 AP3B1 E AP3B1 list24_COV_list2nonOrf adaptor related protein complex 3 subunit beta 1 ADTB3|ADTB3A|HPS|HPS2|PE no 23 3285 1095 0,15982 0,32386 0,0465 0,2908 0,15456 0,491781818181818 0,3584 0,812938823529412 0,56919 1 AP3B1 ENSG00000132842.13 ENST00000522901.1 18 154 93,50649351 0,248401132 0,1666 0,51825 0,70943 -967,479082 -941,687639 0,00000000000629 -966,682479 0,00000000000155 3 S144, L145, L148 2955 985 0,18044 0,26164 0,0413 0,2289 0,78874 1 0,87325 1 0,96811 1
POLA2 POLA2 nsp1 POLA2 list26_COV_list4dataset2nonOrf DNA polymerase alpha 2, accessory subunit - no 24 1797 599 0,15279 0,51575 0,0645 0,4225 0,16456 0,519308196721311 0,15917 0,498215040650407 0,3952 1 POLA2 ENSMLUG00000003879.2 ENSMLUT00000003883.2 9 600 98,33333333 0,17624986 0,3733 0,96837 0,97292 -4965,384841 -4964,261652 0,325240946 POLA2 1 1 1413 471 0,20687 0,29671 0,0467 0,2257 1 1 1 1 1 1
CLIP4 CLIP4 nsp13 CLIP4 list26_COV_list4dataset2nonOrf CAP-Gly domain containing linker protein family member 4 RSNL2 no 24 2118 706 0,09451 0,43 0,0397 0,4196 0,16867 0,5227068 0,13376 0,440150427350427 0,47151 1 CLIP4 ENSMLUG00000010758.2 ENSMLUT00000010784.2 10 703 91,89189189 0,097768764 0,7931 1,28069 1,28272 -5809,284419 -5808,85544 0,651173604 CLIP4 ENSG00000115295.19 ENST00000438819.1 18 93 95,69892473 0,049852989 0,1251 0,21689 0,21685 -452,01225 -451,992607 0,980548667 CLIP4 0,219143969 1 1710 570 0,10121 0,24545 0,0254 0,2506 0,5335 1 0,54715 1 0,82901 1
SLU7 SLU7 nsp12 SLU7 list24_COV_list2nonOrf SLU7 homolog, splicing factor 9G8|hSlu7 no 24 1770 590 0,06299 0,26477 0,0183 0,2913 0,16871 0,5227068 0,14566 0,471252941176471 0,42115 1 SLU7 ENSMLUG00000003091.2 ENSMLUT00000003095.2 8 587 93,6967632 0,052285107 0,3882 0,72181 0,72909 -3777,489826 -3775,221268 0,103461264 SLU7 ENSG00000164609.9 ENST00000520664.1 19 101 100 0,026989183 0,148 0,28531 0,28252 -552,051744 -550,971531 0,3395232 SLU7 0,98511652 1 1587 529 0,08503 0,19183 0,0159 0,1867 0,054259 0,3601675 0,10063 0,7000125 0,15738 1 0,87 4,90547 1 498_I_0.966
CLCC1 CLCC1 orf3a CLCC1 list23_COV_list1orf chloride channel CLIC like 1 MCLC no 24 1662 554 0,37305 0,58272 0,1318 0,3532 0,18203 0,556202777777778 0,3418 0,804883333333333 0,41159 1 CLCC1 ENSMLUG00000010565.2 ENSMLUT00000010579.2 10 551 80,21778584 0,223061198 0,5464 1,66053 1,66452 -4798,81463 -4798,355971 0,632130765 CLCC1 ENSG00000121940.15 ENST00000356970.6 17 552 90,94202899 0,245120934 0,1799 0,48976 0,49191 -3497,202062 -3496,537675 0,514588874 CLCC1 0,533670304 1 1359 453 0,37748 0,37499 0,0864 0,2288 0,26949 1 0,49425 1 0,5431 1
ERO1B ERO1LB orf8 ERO1LB list23_COV_list1orf elongin B SIII|TCEB2 no 24 1404 468 0,15134 0,29031 0,0379 0,2503 0,18383 0,556234765625 0,35896 0,812938823529412 0,43446 1 ERO1B ENSG00000086619.13 ENST00000366589.1 16 71 100 0,049074312 0,1459 0,14276 0,14414 -332,486487 -332,12016 0,693276063 ERO1LB 0,963362268 1 1188 396 0,15311 0,16919 0,0223 0,146 1 1 1 1 1 1
ATP5MG ATP5L nsp6 ATP5L list26_COV_list4dataset2nonOrf ATP synthase membrane subunit g ATP5JG|ATP5L no 24 312 104 0,26952 0,24062 0,0461 0,171 0,18493 0,556234765625 0,37335 0,840583333333333 0,41482 1 270 90 0,2522 0,11696 0,0215 0,0854 1 1 1 1 1 1
ERMP1 ERMP1 orf9c ERMP1 list24_COV_list2nonOrf endoplasmic reticulum metallopeptidase 1 FXNA|KIAA1815|bA207C16.3 no 24 2727 909 0,16093 0,4408 0,0587 0,3649 0,18911 0,560856153846154 0,21221 0,600741544117647 0,22666 0,969601111111111 ERMP1 ENSMLUG00000002419.2 ENSMLUT00000002424.2 10 794 99,37027708 0,116046819 0,3082 0,87456 0,87518 -6355,754476 -6355,716447 0,962685023 ERMP1 ENSG00000099219.13 ENST00000487088.5 19 352 98,86363636 0,138268187 0,1396 0,3606 0,36081 -2174,679364 -2174,677461 0,99809881 ERMP1 0,858106691 1 2217 739 0,15328 0,26405 0,0345 0,2251 0,89125 1 0,8537 1 0,99145 1
HS6ST2 HS6ST2 orf8 HS6ST2 list25_COV_list3dataset2orf heparan sulfate 6-O-sulfotransferase 2 - no 23 1968 656 0,2307 0,37771 0,0714 0,3093 0,19239 0,561057894736842 0,18112 0,540551937984496 0,43537 1 HS6ST2 ENSG00000171004.18 ENST00000640529.1 16 277 97,47292419 0,116525036 0,1978 0,3608 0,36564 -1627,124599 -1626,17383 0,386443734 1383 461 0,19763 0,22521 0,0384 0,1943 1 1 1 1 1 1
NUP58 NUPL1 nsp9 NUPL1 list24_COV_list2nonOrf spartin SPG20|TAHCCP1 no 24 1803 601 0,11458 0,28658 0,03 0,2614 0,19242 0,561057894736842 0,34495 0,804883333333333 0,41534 1 NUPL1 1 1 1566 522 0,11964 0,18355 0,0208 0,1736 1 1 1 1 0,99977 1
EIF4H EIF4H nsp9 EIF4H list26_COV_list4dataset2nonOrf eukaryotic translation initiation factor 4H WBSCR1|WSCR1|eIF-4H no 23 750 250 0,20373 0,49924 0,0805 0,3949 0,19382 0,561057894736842 0,33611 0,803741304347826 1 1 EIF4H ENSMLUG00000009053.2 ENSMLUT00000009050.2 9 233 79,39914163 0,067710536 0,247 0,48058 0,47892 -1156,799444 -1155,092524 0,181423718 EIF4H ENSG00000106682.14 ENST00000353999.6 8 229 93,44978166 0,65726891 0,2049 0,43151 0,43484 -1388,238382 -1388,067762 0,843141907 EIF4H 0,975645425 1 579 193 0,1625 0,31982 0,0469 0,2883 1 1 1 1 1 1
ZYG11B ZYG11B orf10 ZYG11B list25_COV_list3dataset2orf zyg-11 family member B, cell cycle regulator ZYG11 no 24 2238 746 0,03689 0,26145 0,0101 0,2741 0,19666 0,565030597014925 0,012781 0,0936191666666667 0,4345 1 2,423 1,81431 0 noneOver_0.9 ZYG11B ENSMLUG00000024400.1 ENSMLUT00000027975.1 9 384 94,27083333 0,0353737 0,2124 0,36098 0,36142 -2070,412641 -2069,648561 0,465762235 2007 669 0,05058 0,19347 0,0099 0,195 0,043798 0,3372446 0,0076933 0,0955458225806452 0,12764 0,963556862745098 2,124 2,7985 0 noneOver_0.9
EXOSC2 EXOSC2 nsp8 EXOSC2 list24_COV_list2nonOrf exosome component 2 RRP4|Rrp4p|SHRF|hRrp4p|p7 no 24 882 294 0,13906 0,58075 0,072 0,518 0,20088 0,57288 0,16395 0,509038306451613 0,43972 1 EXOSC2 ENSMLUG00000008776.2 ENSMLUT00000008779.2 8 296 98,98648649 0,024424073 0,4064 0,69537 0,69892 -2040,595915 -2039,325761 0,280788377 EXOSC2 0,535813676 1 717 239 0,15613 0,4294 0,0586 0,3751 0,28082 1 0,44689 1 0,5577 1
COQ8B ADCK4 M ADCK4 list26_COV_list4dataset2nonOrf coenzyme Q8B ADCK4|NPHS9 no 24 1641 547 0,18011 0,65301 0,1015 0,5636 0,20476 0,577443795620438 0,3057 0,754451923076923 0,65529 1 COQ8B ENSMLUG00000000210.2 ENSMLUT00000000210.2 11 533 99,43714822 0,15920295 0,3561 1,15164 1,16695 -4815,306353 -4812,452234 0,05760655 1197 399 0,2012 0,4348 0,0751 0,3734 1 1 1 1 1 1
WFS1 WFS1 orf9c WFS1 list26_COV_list4dataset2nonOrf wolframin ER transmembrane glycoprotein CTRCT41|WFRS|WFS|WFSL no 24 2676 892 0,05634 0,83163 0,0831 1,4742 0,20548 0,577443795620438 0,020523 0,129530409836066 0,56458 1 1,077 1,68628 3 57_A_0.966,116_N_0.914,117_T_0.922 WFS1 ENSG00000109501.13 ENST00000506362.1 15 251 97,21115538 0,114656782 0,2466 0,63994 0,64766 -1677,785695 -1675,78902 0,135786022 1647 549 0,0819 0,34708 0,0459 0,5602 0,34967 1 0,4132 1 0,65158 1
TAPT1 TAPT1 orf9c TAPT1 list24_COV_list2nonOrf transmembrane anterior posterior transformation 1 CMVFR|OCLSBG no 24 1704 568 0,1727 0,3442 0,048 0,278 0,21717 0,601569424460432 0,35473 0,812922916666667 0,46634 1 TAPT1 ENSMLUG00000009395.2 ENSMLUT00000009399.2 10 501 98,20359281 0,039818834 0,5346 0,97227 0,97318 -3846,27272 -3845,726577 0,579179403 TAPT1 ENSG00000169762.16 ENST00000513833.1 19 51 94,11764706 0,050566703 0,0519 0,12196 0,20008 -225,402719 -221,92609 0,030911438 -225,388139 0,008504187 TAPT1 1 1 1419 473 0,11404 0,22831 0,0235 0,2063 1 1 1 1 1 1
SPART SPG20 nsp9 SPG20 list24_COV_list2nonOrf spartin SPG20|TAHCCP1 no 24 2001 667 0,10045 0,46066 0,0459 0,4567 0,21719 0,601569424460432 0,23827 0,650595390070922 0,56517 1 SPART ENSMLUG00000012067.2 ENSMLUT00000012066.2 10 625 95,04 0,141161772 1,9595 1,80094 1,81677 -5615,301761 -5613,401915 0,149591655 SPG20 1 1 1722 574 0,14587 0,31945 0,0431 0,2957 0,10234 0,547234722222222 0,16323 0,910776086956522 0,25922 1
NGLY1 NGLY1 orf8 NGLY1 list23_COV_list1orf N-glycanase 1 CDDG|CDG1V|PNG1|PNGase no 23 1968 656 0,28738 0,44287 0,0887 0,3087 0,22347 0,613433333333333 0,39263 0,873771965317919 0,49853 1 NGLY1 ENSMLUG00000001704.2 ENSMLUT00000001705.2 9 615 98,69918699 0,258973023 0,2775 0,7174 0,72344 -4501,119256 -4499,937154 0,306633517 NGLY1 ENSG00000151092.16 ENST00000428257.5 11 637 85,87127159 0,310338885 0,0885 0,23751 0,27515 -3044,408442 -3022,246211 0,000000000237 -3034,104948 0,00000112 6 S32, L34, L355, L36, Y38, P138 1716 572 0,31837 0,33199 0,0713 0,2238 0,48412 1 0,76538 1 0,79131 1
FAR2 FAR2 orf9c FAR2 list24_COV_list2nonOrf fatty acyl-CoA reductase 2 HEL-S-81|MLSTD1|SDR10E2 no 23 1551 517 0,27553 0,53622 0,1029 0,3735 0,22466 0,613433333333333 0,25739 0,669561824324324 0,47255 1 FAR2 ENSMLUG00000014544.2 ENSMLUT00000014548.2 6 516 99,41860465 0,123976275 0,4296 0,91673 0,92826 -3793,745323 -3792,274584 0,229755633 FAR2 1 1 1302 434 0,32665 0,38533 0,0828 0,2534 0,010923 0,116665405405405 0,018428 0,197077222222222 0,039065 0,417778472222222 19 1,78557 5 241_S_0.979,246_L_0.978,252_H_0.931,281_S_0.920,301_F_0.936
CHMP2A CHMP2A orf9b CHMP2A list25_COV_list3dataset2orf charged multivesicular body protein 2A BC-2|BC2|CHMP2|VPS2|VPS2A no 24 669 223 0,00793 0,38776 0,004 0,5084 0,23208 0,629230985915493 0,029512 0,165459097222222 0,48971 1 0,484 3,36041 0 noneOver_0.9 CHMP2A ENSMLUG00000014318.2 ENSMLUT00000014320.2 11 225 93,33333333 0,009865607 0,6108 51,30209 51,30799 -2251,87779 -2250,702219 0,308642694 CHMP2A 0,255998936 1 519 173 0,0001 0,26378 0 0,3752 0,99483 1 1 1 0,9991 1
NLRX1 NLRX1 orf9c NLRX1 list24_COV_list2nonOrf NLR family member X1 CLR11.3|DLNB26|NOD26|NOD5|NOD9 no 24 2955 985 0,17885 0,73839 0,1083 0,6055 0,23405 0,630134615384615 0,046322 0,216022108433735 0,50568 1 8,98 1,3302 1 143_Q_0.900 NLRX1 ENSMLUG00000000471.2 ENSMLUT00000000472.2 10 976 88,7295082 0,119503218 0,5895 1,51145 1,51495 -8200,057676 -8199,671454 0,679619633 NLRX1 ENSG00000160703.15 ENST00000409265.8 17 976 95,79918033 0,175988852 0,2133 0,5628 0,58202 -6658,028812 -6655,525082 0,081779392 NLRX1 1 1 2133 711 0,16764 0,40048 0,0569 0,3397 0,24233 0,992521808510638 0,22746 1 0,50582 1
NUP214 NUP214 nsp9 NUP214 list24_COV_list2nonOrf nucleoporin 214 CAIN|CAN no 24 6297 2099 0,28183 0,55213 0,1001 0,355 0,24047 0,641787248322148 0,29089 0,7281 0,6197 1 NUP214 ENSMLUG00000004875.2 ENSMLUT00000004890.2 9 2067 91,43686502 0,183714759 0,4956 1,60598 1,85946 -19714,55251 -19660,54198 3,5E-024 -19710,52044 1,56E-023 9 Q1665, G1682, Q1683, Q1684, L1685, T1281, V1675, S1678, A1679 NUP214 ENSG00000126883.16 ENST00000451030.5 15 1520 92,96052632 0,285515801 0,1529 0,50526 0,50819 -9870,347356 -9867,143151 0,040591159 -9870,284533 0,01219178 NUP214 1 1 5196 1732 0,35056 0,35951 0,076 0,2168 0,84904 1 0,86724 1 0,98284 1
PIGO PIGO orf9c PIGO list24_COV_list2nonOrf phosphatidylinositol glycan anchor biosynthesis class O HPMRS2 no 24 3276 1092 0,27487 0,43671 0,0783 0,2848 0,24174 0,641787248322148 0,38737 0,867078197674419 0,5056 1 PIGO ENSMLUG00000006159.2 ENSMLUT00000006157.2 15 1097 96,9006381 0,18586015 0,2616 1,19819 1,19982 -10775,27331 -10774,63539 0,52838822 PIGO ENSG00000165282.13 ENST00000361778.6 19 673 98,36552749 0,188942514 0,1108 0,32465 0,32617 -4024,759763 -4024,37837 0,682909453 PIGO 1 1 2766 922 0,28573 0,30907 0,0574 0,2009 0,63643 1 0,80664 1 0,89465 1
PITRM1 PITRM1 M PITRM1 list24_COV_list2nonOrf pitrilysin metallopeptidase 1 MP1|PreP no 24 3117 1039 0,16967 0,92013 0,1271 0,7489 0,24587 0,641787248322148 0,12074 0,411370796460177 1 1 PITRM1 ENSMLUG00000001788.2 ENSMLUT00000001807.2 4 1025 93,07317073 0,225980009 0,3712 0,48315 0,89431 -5525,955326 -5511,092897 0,000000351 -5525,683766 0,0000000659 5 S104, Q171, S172, L173, G174 PITRM1 ENSG00000107959.15 ENST00000380989.6 8 1039 95,76515881 0,107752447 0,1232 0,2203 0,22042 -5308,90701 -5308,827802 0,923847744 PITRM1 1 1 2160 720 0,18605 0,48445 0,0722 0,3881 1 1 1 1 1 1
ATP1B1 ATP1B1 M ATP1B1 list26_COV_list4dataset2nonOrf ATPase Na+/K+ transporting subunit beta 1 ATP1B no 24 912 304 0,11853 0,30643 0,0338 0,2853 0,24838 0,641787248322148 0,3232 0,787544303797468 0,51221 1 ATP1B1 ENSMLUG00000010015.2 ENSMLUT00000010005.2 6 304 88,48684211 0,157161129 0,7416 1,55528 1,55531 -2399,530233 -2399,530829 0,999404178 ATP1B1 ENSG00000143153.12 ENST00000494797.1 4 130 100 0,142624521 0,1377 0,19814 0,19814 -648,606372 -648,606401 0,999971 ATP1B1 1 1 744 248 0,18405 0,20344 0,0316 0,1718 0,062328 0,374941875 0,15669 0,900382835820896 0,17402 1 0,945 11,02287 2 163_V_0.972,188_S_0.962
PPT1 PPT1 orf10 PPT1 list25_COV_list3dataset2orf palmitoyl-protein thioesterase 1 CLN1|INCL|PPT no 24 921 307 0,13334 0,44277 0,0502 0,3763 0,26734 0,68064477124183 0,5405 1 0,77288 1 PPT1 ENSMLUG00000011644.2 ENSMLUT00000011641.2 9 267 96,25468165 0,257815896 0,2072 0,66153 0,66126 -1903,829352 -1903,71181 0,889103169 750 250 0,16672 0,30025 0,0383 0,2296 0,40213 1 0,64047 1 0,70249 1
HYOU1 HYOU1 orf8 HYOU1 list23_COV_list1orf hypoxia up-regulated 1 GRP-170|Grp170|HSP12A|ORP-150|ORP150 no 22 3006 1002 0,1235 0,40065 0,0432 0,3499 0,26918 0,68064477124183 0,25408 0,669561824324324 0,5498 1 HYOU1 ENSMLUG00000004772.2 ENSMLUT00000004783.2 3 998 99,3987976 0,110361074 0,0477 0,05613 0,05613 -4245,112732 -4245,112758 0,999974 HYOU1 1 1 2415 805 0,16411 0,24702 0,0334 0,2035 0,99126 1 1 1 1 1
TUBGCP2 TUBGCP2 M TUBGCP2 list26_COV_list4dataset2nonOrf tubulin gamma complex associated protein 2 ALP4|GCP-2|GCP2|Grip103|SPBC97|SPC97|Spc97p|h103p|hGCP2|hSpc97 no 24 2796 932 0,04985 0,71657 0,049 0,9833 0,27049 0,68064477124183 0,45191 0,961245027624309 1 1 TUBGCP2 ENSMLUG00000005342.2 ENSMLUT00000005349.2 8 909 92,29922992 0,075141747 0,4609 0,97117 0,97118 -6124,705355 -6124,707071 0,998285471 TUBGCP2 ENSG00000130640.13 ENST00000368563.6 14 903 97,89590255 0,084994374 0,2728 0,63111 0,63111 -6250,382718 -6250,382727 0,999991 TUBGCP2 0,982777377 1 1845 615 0,04666 0,29126 0,02 0,4283 1 1 1 1 1 1
POGLUT2 KDELC1 orf8 KDELC1 list23_COV_list1orf KDEL motif containing 1 EP58|ERp58|KDEL1 no 24 1509 503 0,16399 0,49371 0,0666 0,4064 0,27646 0,687759032258065 0,55105 1 0,73389 1 KDELC1 1 1 1233 411 0,22186 0,29863 0,0488 0,2201 0,96996 1 0,99702 1 1 1
NSD2 WHSC1 nsp8 WHSC1 list26_COV_list4dataset2nonOrf nuclear receptor binding SET domain protein 2 KMT3F|KMT3G|MMSET|REIIBP|TRX5|WHS|WHSC1 no 23 4113 1371 0,09001 0,36185 0,0315 0,3496 0,27689 0,687759032258065 0,27841 0,719381543624161 0,55646 1 NSD2 ENSG00000109685.17 ENST00000436793.5 18 274 92,33576642 0,070500844 0,1484 0,33157 0,33157 -1552,440796 -1552,44109 0,999706043 WHSC1 0,129384661 1 3228 1076 0,09064 0,17965 0,0171 0,1889 0,25303 1 0,34824 1 0,52368 1
GDF15 GDF15 orf8 GDF15 list23_COV_list1orf growth differentiation factor 15 GDF-15|MIC-1|MIC1|NAG-1|PDF|PLAB|PTGFB no 24 927 309 0,3142 0,9345 0,2073 0,6598 0,28141 0,693194936708861 0,18872 0,554079166666667 0,53676 1 GDF15 ENSMLUG00000002100.2 ENSMLUT00000002100.2 10 317 76,34069401 0,431348518 0,3741 1,80936 1,913 -2834,255536 -2828,759219 0,004101851 -2833,271339 0,002664227 1 R184 GDF15 ENSG00000130513.6 ENST00000252809.3 16 309 99,02912621 0,472097597 0,1642 0,69182 0,71146 -2397,029019 -2396,63771 0,676171187 450 150 0,37175 0,45028 0,1036 0,2787 0,62699 1 0,88607 1 0,88849 1
NARS2 NARS2 nsp8 NARS2 list24_COV_list2nonOrf asparaginyl-tRNA synthetase 2, mitochondrial DFNB94|SLM5|asnRS no 24 1434 478 0,39823 0,55805 0,1275 0,3202 0,28308 0,693194936708861 0,53869 1 0,56167 1 NARS2 ENSMLUG00000030064.1 ENSMLUT00000023526.1 8 479 97,07724426 0,217431908 0,2764 0,77397 0,78946 -3577,837957 -3571,569958 0,001896019 -3577,271359 0,000733334 3 R200, G204, A286 NARS2 0,100191532 0,958813731 1203 401 0,41312 0,45039 0,1051 0,2543 0,12046 0,6023 0,23605 1 0,29853 1
AKAP8 AKAP8 nsp12 AKAP8 list26_COV_list4dataset2nonOrf A-kinase anchoring protein 8 AKAP 95|AKAP-8|AKAP-95|AKAP95 no 24 2079 693 0,2358 0,77107 0,1348 0,5717 0,28448 0,693194936708861 0,086684 0,330429108910891 0,5762 1 10,642 1,34413 0 noneOver_0.9 AKAP8 ENSG00000105127.8 ENST00000599883.1 17 134 89,55223881 1,423143788 0,1175 0,6778 0,83102 -975,587369 -957,555979 0,0000000148 -975,425749 0,00000000226 6 R48, A114, Q83, A101, P120, R122 1323 441 0,25814 0,36183 0,0711 0,2754 0,0011182 0,0195685 0,0026395 0,0502846666666667 0,0050815 0,092554 2,027 6,19713 4 369_G_0.920,406_P_0.945,437_I_0.977,438_P_0.901
TBK1 TBK1 nsp13 TBK1 list26_COV_list4dataset2nonOrf TANK binding kinase 1 FTDALS4|IIAE8|NAK|T2K no 24 2190 730 0,09206 0,30576 0,0308 0,3344 0,2918 0,706559748427673 0,45169 0,961245027624309 0,68512 1 TBK1 ENSMLUG00000009717.2 ENSMLUT00000009747.2 11 730 99,31506849 0,132077656 0,2028 0,56124 0,56429 -4901,954784 -4900,946517 0,364850718 TBK1 ENSG00000183735.9 ENST00000539810.1 18 48 100 0,198 0,1811 0,1811 -221,309111 -221,309323 0,999788022 TBK1 1 1 1941 647 0,09834 0,20818 0,0224 0,2276 0,11095 0,562049342105263 0,21216 1 0,3253 1
BRD4 BRD4 E BRD4 list24_COV_list2nonOrf bromodomain containing 4 CAP|HUNK1|HUNKI|MCAP no 21 4113 1371 0,06065 0,41153 0,0251 0,4132 0,29969 0,7211290625 0,042907 0,205252530864198 0,57425 1 4,429 1,50441 1 583_A_0.960 BRD4 ENSG00000141867.17 ENST00000630599.2 13 147 95,23809524 0,011362622 0,0798 0,08819 0,08798 -625,441726 -625,428566 0,986926214 BRD4 0,99680847 1 3135 1045 0,09949 0,21838 0,021 0,2106 0,00033419 0,00761416764705882 0,001193 0,0270179411764706 0,0016057 0,03885371875 4,659 2,97219 3 756_Q_0.960,780_Q_0.960,781_Q_0.959
GRIPAP1 GRIPAP1 nsp13 GRIPAP1 list26_COV_list4dataset2nonOrf GRIP1 associated protein 1 GRASP-1 no 23 2526 842 0,1096 0,26692 0,0276 0,2519 0,31279 0,747976086956522 0,60659 1 0,6663 1 GRIPAP1 ENSMLUG00000009232.2 ENSMLUT00000009258.2 12 842 93,46793349 0,107890943 0,3829 1,18978 1,19909 -7203,55603 -7202,940685 0,540454406 GRIPAP1 ENSG00000068400.13 ENST00000611705.4 19 23 100 0,0779 0,04538 0,04538 -87,183363 -87,183391 0,999972 GRIPAP1 0,128417261 1 2139 713 0,09263 0,19315 0,017 0,1832 0,37347 1 0,6956 1 0,72285 1
PRKAR2A PRKAR2A nsp13 PRKAR2A list26_COV_list4dataset2nonOrf protein kinase cAMP-dependent type II regulatory subunit alpha PKR2|PRKAR2 no 24 1215 405 0,292 0,40393 0,0797 0,273 0,31871 0,757428086419753 0,49167 1 0,61064 1 PRKAR2A ENSMLUG00000016288.2 ENSMLUT00000016294.2 11 306 86,92810458 0,096603538 0,5384 0,97041 0,97603 -2217,430293 -2214,792451 0,071515433 933 311 0,38004 0,22242 0,0506 0,1331 1 1 1 1 1 1
MOGS MOGS nsp7 MOGS list26_COV_list4dataset2nonOrf mannosyl-oligosaccharide glucosidase CDG2B|CWH41|DER7|GCS1 no 22 2514 838 0,26344 0,42818 0,0758 0,2877 0,32782 0,76957743902439 0,52304 1 0,61797 1 MOGS ENSMLUG00000002652.1 ENSMLUT00000002648.1 10 837 95,69892473 0,168764689 0,2855 0,92877 0,93093 -6836,753533 -6835,811684 0,389906232 MOGS ENSG00000115275.11 ENST00000452063.6 18 732 98,63387978 0,209879997 0,134 0,39272 0,39394 -4654,436421 -4653,417236 0,360888945 MOGS 0,003943108 0,140860154 1992 664 0,30752 0,32142 0,0623 0,2025 0,060067 0,372996693548387 0,17039 0,934465492957747 0,17011 1 4,824 3,21378 2 94_R_0.910,243_I_0.959
SCAP SCAP orf9c SCAP list24_COV_list2nonOrf SREBF chaperone - no 22 3840 1280 0,07872 0,4931 0,0419 0,5329 0,33908 0,791186666666667 0,06311 0,27300393258427 0,69263 1 1,209 1,9665 2 600_H_0.920,904_T_0.911 SCAP ENSMLUG00000004086.2 ENSMLUT00000004092.2 9 1281 94,84777518 0,108448467 0,3923 1,23585 1,26031 -10360,59232 -10358,03438 0,07746423 SCAP ENSG00000114650.18 ENST00000428413.5 4 353 98,01699717 0,435790934 0,0736 0,18447 0,18947 -1736,169015 -1735,761127 0,665053361 SCAP 1 1 2823 941 0,08702 0,22967 0,022 0,2531 0,99471 1 1 1 0,99999 1
AATF AATF nsp8 AATF list24_COV_list2nonOrf apoptosis antagonizing transcription factor BFR2|CHE-1|CHE1|DED no 24 1707 569 0,32081 0,5717 0,1173 0,3657 0,34348 0,796625301204819 0,41047 0,908223850574713 0,70523 1 AATF ENSMLUG00000014754.2 ENSMLUT00000023634.1 11 509 88,60510806 0,127438902 0,3589 0,88821 0,89052 -3789,10586 -3788,867496 0,787915838 AATF 0,111335036 1 1350 450 0,32933 0,3587 0,0753 0,2285 1 1 1 1 1 1
BRD2 BRD2 E BRD2 list24_COV_list2nonOrf bromodomain containing 2 BRD2-IT1|D6S113E|FSH|FSRG1|NAT|O27.1.1|RING3|RNF3 no 22 2409 803 0,08383 0,40346 0,0305 0,3644 0,3639 0,838931137724551 0,6622 1 0,94347 1 2064 688 0,08073 0,26902 0,0195 0,2413 0,61428 1 0,66143 1 0,88048 1
FBN2 FBN2 nsp9 FBN2 list24_COV_list2nonOrf fibrillin 2 CCA|DA9|EOMD no 24 8745 2915 0,06871 0,37544 0,0263 0,3835 0,37008 0,84406124260355 0,17475 0,525615234375 0,65545 1 FBN2 ENSMLUG00000008374.2 ENSMLUT00000008504.2 8 2914 94,47494852 0,016657993 0,3658 0,61556 0,61598 -17859,44955 -17857,26975 0,113063915 FBN2 1 1 7026 2342 0,08992 0,21166 0,0192 0,2131 0,48817 1 0,72441 1 0,76977 1
AKAP8L AKAP8L M AKAP8L list26_COV_list4dataset2nonOrf A-kinase anchoring protein 8 like HA95|HAP95|NAKAP|NAKAP95 no 24 1953 651 0,08781 0,41593 0,0403 0,4594 0,37051 0,84406124260355 0,34764 0,806273493975904 1 1 AKAP8L ENSMLUG00000005469.2 ENSMLUT00000005479.2 10 645 92,55813953 0,104953817 0,2979 0,82808 0,83009 -4639,745585 -4638,897914 0,428411541 AKAP8L ENSG00000011243.17 ENST00000595067.1 18 161 85,0931677 0,276677834 0,1594 0,49321 0,60807 -927,328511 -908,930064 0,0000000102 -927,327428 0,00000000131 4 V33, V37, S38, G135 1389 463 0,06953 0,23605 0,0192 0,276 1 1 1 1 1 1
OS9 OS9 orf8 OS9 list23_COV_list1orf OS9, endoplasmic reticulum lectin ERLEC2|OS-9 no 24 2016 672 0,17831 0,39211 0,0555 0,3112 0,376 0,851529411764706 0,528 1 0,72735 1 OS9 ENSMLUG00000005905.2 ENSMLUT00000005907.2 12 668 95,20958084 0,19047821 0,2765 0,91422 0,92454 -5483,73567 -5480,367642 0,034457521 -5483,01591 0,021367878 2 L18, S348 OS9 ENSG00000135506.15 ENST00000552285.5 19 613 93,31158238 0,129807199 0,1537 0,40194 0,40256 -3659,214703 -3658,398313 0,442024486 OS9 0,011396312 0,286659061 1626 542 0,21531 0,25539 0,0419 0,1948 1 1 1 1 1 1
SIGMAR1 SIGMAR1 nsp6 SIGMAR1 list26_COV_list4dataset2nonOrf sigma non-opioid intracellular receptor 1 ALS16|DSMA2|OPRS1|SIG-1R|SR-BP|SR-BP1|SRBP|hSigmaR1|sigma1R no 24 672 224 0,05548 0,42023 0,0297 0,5354 0,38838 0,874422807017544 0,596 1 0,67815 1 SIGMAR1 ENSMLUG00000003061.1 ENSMLUT00000003059.1 9 224 98,66071429 0,085786955 0,2738 0,68602 0,68531 -1513,500222 -1513,071044 0,651044033 SIGMAR1 ENSG00000147955.16 ENST00000477726.1 16 193 87,56476684 0,045269245 0,2398 0,42429 0,42002 -1002,547639 -1002,375629 0,841970753 429 143 0,0585 0,29391 0,0203 0,3471 1 1 1 1 1 1
MTCH1 MTCH1 orf6 MTCH1 list25_COV_list3dataset2orf mitochondrial carrier 1 CGI-64|PIG60|PSAP|SLC25A49 no 24 1170 390 0,01463 0,31726 0,0058 0,3982 0,40247 0,90087761627907 0,19495 0,560117537313433 0,70179 1 MTCH1 1 1 828 276 0,01403 0,1865 0,0033 0,2357 1 1 1 1 1 1
LARP4B LARP4B nsp12 LARP4B list24_COV_list2nonOrf La ribonucleoprotein domain family member 4B KIAA0217|LARP5 no 24 2223 741 0,19866 0,42868 0,0622 0,3133 0,41368 0,917716091954023 0,4579 0,963341530054645 0,71903 1 LARP4B ENSG00000107929.14 ENST00000612396.4 9 739 98,91745602 0,093925415 0,0827 0,12243 0,12243 -3514,543999 -3514,571678 0,972700554 LARP4B 1 1 1740 580 0,23756 0,1925 0,0324 0,1363 1 1 1 1 1 1
RETREG3 FAM134C orf9c FAM134C list24_COV_list2nonOrf reticulophagy regulator family member 3 - no 24 1401 467 0,18477 0,3476 0,0493 0,2669 0,41476 0,917716091954023 0,5193 1 0,71832 1 RETREG3 ENSMLUG00000001002.2 ENSMLUT00000001011.2 13 467 81,37044968 0,187634062 0,2437 0,83137 0,84208 -3170,863884 -3168,40824 0,085807917 RETREG3 ENSG00000141699.10 ENST00000590541.1 19 82 95,12195122 0,197804078 0,2055 0,45293 0,45293 -499,872999 -499,873027 0,999972 FAM134C 0,383568347 1 1164 388 0,17208 0,21804 0,0299 0,1739 1 1 1 1 0,99988 1
ARL6IP6 ARL6IP6 orf3a ARL6IP6 list25_COV_list3dataset2orf ADP ribosylation factor like GTPase 6 interacting protein 6 AIP-6|AIP6|PFAAP1 no 23 681 227 0,32853 0,56407 0,1176 0,358 0,42489 0,928785310734463 0,57486 1 0,7298 1 ARL6IP6 ENSMLUG00000023268.1 ENSMLUT00000029257.1 9 227 93,39207048 0,231315309 0,3793 1,0818 1,0818 -1885,798414 -1885,79842 0,999994 ARL6IP6 ENSG00000177917.10 ENST00000425034.5 13 38 92,10526316 0,190198357 0,1732 0,26435 0,26406 -188,146347 -188,139579 0,993254851 ARL6IP6 1 1 447 149 0,4519 0,35247 0,0871 0,1927 0,40967 1 0,6991 1 0,71817 1
RBM28 RBM28 N RBM28 list26_COV_list4dataset2nonOrf RNA binding motif protein 28 ANES no 22 2307 769 0,34258 0,47328 0,1046 0,3052 0,42504 0,928785310734463 0,54944 1 0,72893 1 RBM28 ENSMLUG00000002186.2 ENSMLUT00000002190.2 9 763 91,08781127 0,235335938 0,2569 0,78998 0,79964 -5494,801233 -5488,975374 0,002950269 -5494,088549 0,001384484 4 G9, T538, G91, L618 RBM28 ENSG00000106344.8 ENST00000459726.1 8 204 79,90196078 0,18206032 0,1638 0,29879 0,29879 -920,743821 -920,743821 1 RBM28 0,722677185 1 1950 650 0,39408 0,34188 0,0821 0,2084 0,22476 0,961473333333333 0,41473 1 0,47929 1
MRPS2 MRPS2 nsp8 MRPS2 list26_COV_list4dataset2nonOrf mitochondrial ribosomal protein S2 CGI-91|COXPD36|MRP-S2|S2mt no 24 897 299 0,15502 1,17978 0,1996 1,2876 0,427 0,928785310734463 0,12306 0,415597368421053 1 1 MRPS2 ENSMLUG00000013877.2 ENSMLUT00000013879.2 13 244 93,03278689 0,170502026 0,4602 1,8283 1,84919 -2336,049023 -2335,381647 0,513053064 462 154 0,16389 0,5677 0,0967 0,5902 0,32466 1 0,26986 1 0,67062 1
CEP112 CEP112 nsp13 CEP112 list26_COV_list4dataset2nonOrf centrosomal protein 112 CCDC46|MACOCO no 23 2868 956 0,27672 0,36328 0,0696 0,2516 0,49425 1 0,78137 1 0,82637 1 CEP112 ENSMLUG00000013982.2 ENSMLUT00000013991.2 8 799 99,12390488 0,163141077 0,3459 0,77965 0,78737 -6120,488871 -6118,808518 0,186308198 CEP112 ENSG00000154240.16 ENST00000392769.6 14 956 98,9539749 0,120187334 0,1307 0,25774 0,26157 -5241,856728 -5241,04238 0,442928022 2517 839 0,32162 0,27798 0,0584 0,1817 0,14144 0,6824125 0,30824 1 0,34172 1
LARP1 LARP1 N LARP1 list26_COV_list4dataset2nonOrf La ribonucleoprotein domain family member 1 LARP no 24 3066 1022 0,09549 0,29482 0,0258 0,2698 0,49555 1 0,79066 1 0,78293 1 LARP1 ENSMLUG00000002608.2 ENSMLUT00000002615.2 7 954 93,08176101 0,103751344 0,2012 0,48234 0,52496 -5512,860831 -5488,781817 0,0000000000349 -5512,856226 0,00000000000395 9 C800, K786, S803, L805, V214, F795, P796, S797, M799 LARP1 1 1 2589 863 0,10596 0,20656 0,02 0,1892 0,47127 1 0,71476 1 0,77053 1
INHBE INHBE orf8 INHBE list23_COV_list1orf inhibin subunit beta E - no 24 1056 352 0,38677 0,55937 0,1249 0,323 0,49865 1 0,68668 1 0,79581 1 INHBE ENSMLUG00000001009.2 ENSMLUT00000001003.2 15 354 87,85310734 0,341318744 0,3176 1,66327 1,71573 -3702,997967 -3692,734981 0,0000349 -3701,695406 0,000023 3 V88, S89, G206 INHBE ENSG00000139269.2 ENST00000266646.2 19 351 98,29059829 0,291782947 0,1604 0,49118 0,49247 -2420,61857 -2420,509659 0,89681023 846 282 0,34811 0,36159 0,0781 0,2244 0,43355 1 0,65462 1 0,73741 1
AGPS AGPS nsp7 AGPS list26_COV_list4dataset2nonOrf alkylglycerone phosphate synthase ADAP-S|ADAS|ADHAPS|ADPS|ALDHPSY|RCDP3 no 23 2082 694 0,13686 0,29057 0,0366 0,2674 0,50248 1 0,33891 0,804883333333333 0,79861 1 AGPS ENSMLUG00000005113.2 ENSMLUT00000005117.2 3 631 99,52456418 0,137430765 0,0486 0,06106 0,09496 -2749,523522 -2741,01313 0,000201365 -2749,441895 0,0000403 1 C36 AGPS ENSG00000018510.13 ENST00000637633.1 15 543 97,053407 0,114573435 0,0962 0,2451 0,25094 -2828,579642 -2825,756491 0,05941842 AGPS 0,00000168 0,000384393 alkylglycerone phosphate synthase Yes This gene is a member of the FAD-binding oxidoreductase/transferase type 4 family. It encodes a protein that catalyzes the second step of ether lipid biosynthesis in which acyl-dihydroxyacetonephosphate (DHAP) is converted to alkyl-DHAP by the addition of a long chain alcohol and the removal of a long-chain acid anion. The protein is localized to the inner aspect of the peroxisomal membrane and requires FAD as a cofactor. Mutations in this gene have been associated with rhizomelic chondrodysplasia punctata, type 3 and Zellweger syndrome. 1707 569 0,11542 0,16958 0,0199 0,1725 0,39736 1 0,63927 1 0,69903 1
MRPS25 MRPS25 nsp8 MRPS25 list24_COV_list2nonOrf mitochondrial ribosomal protein S25 MRP-S25|RPMS25 no 24 522 174 0,09937 0,4868 0,0562 0,5651 0,52124 1 0,45691 0,963341530054645 0,81267 1 MRPS25 ENSG00000131368.7 ENST00000447299.1 3 111 97,2972973 0,043297522 0,0602 0,07156 0,07156 -453,886885 -453,886924 0,999961001 426 142 0,09407 0,31907 0,0356 0,3785 0,77024 1 0,76365 1 0,95724 1
MTARC1 01/03/2020 nsp7 01/03/2020 list24_COV_list2nonOrf mitochondrial amidoxime reducing component 1 MOSC1 no 24 1023 341 0,30561 0,87314 0,1812 0,5928 0,52595 1 0,18997 0,554079166666667 0,82394 1 645 215 0,36972 0,31499 0,0713 0,1929 0,25792 1 0,46322 1 0,52227 1
F2RL1 F2RL1 orf9c F2RL1 list24_COV_list2nonOrf F2R like trypsin receptor 1 GPR11|PAR2 no 24 1194 398 0,21906 0,49746 0,0834 0,3809 0,54297 1 0,75811 1 0,83158 1 F2RL1 ENSG00000164251.4 ENST00000296677.4 19 398 89,94974874 0,217466052 0,2177 0,64829 0,70554 -2682,34684 -2676,838331 0,004052145 -2681,73712 0,001747419 3 K41, S21, S37 1005 335 0,22368 0,35318 0,0598 0,2675 1 1 0,98522 1 1 1
MFGE8 MFGE8 orf8 MFGE8 list25_COV_list3dataset2orf milk fat globule-EGF factor 8 protein BA46|EDIL1|HMFG|HsT19888|MFG-E8|MFGM|OAcGD3S|SED1|SPAG10|hP47 no 24 1164 388 0,18196 0,78587 0,1345 0,739 0,54812 1 0,32569 0,788620440251572 0,95525 1 771 257 0,11983 0,38704 0,0527 0,4399 1 1 1 1 1 1
SLC9A3R1 SLC9A3R1 orf9b SLC9A3R1 list25_COV_list3dataset2orf SLC9A3 regulator 1 EBP50|NHERF|NHERF-1|NHERF1|NPHLOP2 no 23 1077 359 0,09675 0,48167 0,0594 0,6134 0,55212 1 0,24235 0,657075704225352 0,8662 1 SLC9A3R1 1 1 675 225 0,09107 0,23244 0,0252 0,2771 0,95163 1 1 1 1 1
CWC27 CWC27 E CWC27 list26_COV_list4dataset2nonOrf CWC27 spliceosome associated protein homolog NY-CO-10|RPSKA|SDCCAG-10|SDCCAG10 no 22 1422 474 0,14589 0,321 0,0455 0,3121 0,55657 1 0,60269 1 0,84731 1 CWC27 ENSMLUG00000003562.2 ENSMLUT00000003564.2 4 313 94,88817891 0,096007456 0,3081 0,38211 0,38211 -1632,491527 -1632,491553 0,999974 CWC27 ENSG00000153015.15 ENST00000508024.1 6 382 97,90575916 0,223945479 0,0634 0,11018 0,12637 -1747,965293 -1745,841439 0,119569917 CWC27 1 1 1227 409 0,21492 0,20943 0,0378 0,1759 1 1 1 1 1 1
CYB5B CYB5B nsp7 CYB5B list26_COV_list4dataset2nonOrf cytochrome b5 type B CYB5-M|CYPB5M|OMB5 no 24 453 151 0,22144 0,36415 0,0587 0,2649 0,55997 1 0,65223 1 0,84198 1 CYB5B ENSMLUG00000006608.2 ENSMLUT00000006598.2 4 148 78,37837838 0,557940745 0,3324 0,9831 1,15315 -783,618443 -781,550673 0,12646749 CYB5B ENSG00000103018.16 ENST00000307892.12 4 151 98,01324503 0,12987815 0,1274 0,16408 0,16408 -724,577464 -724,577492 0,999972 CYB5B 0,685166434 1 351 117 0,3512 0,22811 0,0476 0,1355 1 1 1 1 0,99999 1
MAP7D1 MAP7D1 orf10 MAP7D1 list25_COV_list3dataset2orf MAP7 domain containing 1 PARCC1|RPRC1 no 24 2538 846 0,19093 0,42876 0,0659 0,345 0,56264 1 0,67164 1 0,84817 1 MAP7D1 ENSMLUG00000012362.2 ENSMLUT00000012367.2 13 793 86,25472888 0,154264292 0,2775 0,91788 0,92033 -5765,175095 -5764,40945 0,465033887 MAP7D1 ENSG00000116871.15 ENST00000530975.4 16 247 90,28340081 0,139878192 0,2025 0,39017 0,39018 -1366,41079 -1366,410994 0,999796021 MAP7D1 1 1 1788 596 0,24239 0,27063 0,048 0,1982 0,16076 0,764106172839506 0,34789 1 0,37994 1
IDE IDE nsp4 IDE list24_COV_list2nonOrf insulin degrading enzyme INSULYSIN no 24 3060 1020 0,11029 0,28343 0,0298 0,2705 0,59121 1 0,62883 1 0,86566 1 IDE ENSMLUG00000009510.2 ENSMLUT00000009538.2 11 990 99,6969697 0,040802575 0,228 0,48692 0,48692 -6281,329159 -6281,329227 0,999932002 IDE ENSG00000119912.15 ENST00000371581.9 19 465 98,70967742 0,062270064 0,1261 0,29865 0,29466 -2629,337266 -2626,242243 0,045273971 -2629,027972 0,018255554 IDE 1 1 2760 920 0,12071 0,21574 0,0245 0,2033 0,16439 0,771831097560976 0,29376 1 0,38037 1
PMPCA PMPCA M PMPCA list24_COV_list2nonOrf peptidase, mitochondrial processing alpha subunit Alpha-MPP|CLA1|CPD3|INPP5E|P-55|SCAR2 no 24 1578 526 0,09776 0,72842 0,069 0,7056 0,61863 1 0,28881 0,7281 0,84442 1 PMPCA ENSG00000165688.11 ENST00000444897.2 8 272 93,01470588 0,24738248 0,1426 0,41602 0,44285 -1552,358247 -1551,276085 0,338862113 1047 349 0,06969 0,4089 0,0306 0,4398 1 1 1 1 1 1
GGH GGH orf8 GGH list25_COV_list3dataset2orf gamma-glutamyl hydrolase GH no 23 957 319 0,35952 0,59257 0,1323 0,368 0,67163 1 0,71223 1 0,91407 1 GGH ENSMLUG00000007310.2 ENSMLUT00000007310.2 5 318 94,33962264 0,387323224 0,3468 1,06811 1,10986 -2443,726561 -2440,324553 0,033306324 -2443,440181 0,012551468 1 K255 GGH ENSG00000137563.11 ENST00000260118.6 16 319 95,61128527 0,240154466 0,1593 0,40196 0,41017 -1914,496732 -1913,879312 0,539334126 GGH 0,000916132 0,052384308 780 260 0,35526 0,42582 0,0961 0,2706 0,64927 1 0,77867 1 0,90158 1
QSOX2 QSOX2 nsp7 QSOX2 list26_COV_list4dataset2nonOrf quiescin sulfhydryl oxidase 2 QSCN6L1|SOXN no 24 2097 699 0,20663 0,88694 0,1456 0,7046 0,67587 1 0,6123 1 1 1 QSOX2 ENSG00000165661.16 ENST00000455222.1 17 283 95,75971731 0,177627444 0,3308 0,91996 0,91996 -2333,494261 -2333,494261 1 1260 420 0,2796 0,48719 0,0958 0,3426 0,053471 0,3601675 0,11783 0,734170161290323 0,16321 1 0,944 6,84638 4 63_N_0.901,200_P_0.924,245_S_0.919,383_D_0.965
GNG5 GNG5 nsp7 GNG5 list26_COV_list4dataset2nonOrf G protein subunit gamma 5 - no 24 207 69 0,33712 0,49735 0,1 0,2966 0,68217 1 0,8982 1 0,91911 1 GNG5 ENSG00000174021.10 ENST00000370641.3 19 69 81,15942029 0,540859677 0,1553 0,85314 0,89071 -462,876283 -457,698307 0,005639409 -462,703522 0,001556562 2 R25, R18 171 57 0,40192 0,27133 0,0592 0,1472 0,10788 0,561267567567568 0,26941 1 0,27406 1
PKP2 PKP2 nsp1 PKP2 list24_COV_list2nonOrf plakophilin 2 ARVD9 no 24 2655 885 0,15795 0,58899 0,0811 0,5133 0,71917 1 1 1 1 1 PKP2 0,91996417 1 1896 632 0,17784 0,33125 0,0514 0,2891 0,70539 1 0,85457 1 0,93165 1
PMPCB PMPCB M PMPCB list24_COV_list2nonOrf peptidase, mitochondrial processing beta subunit Beta-MPP|MPP11|MPPB|MPPP52|P-52 no 23 1473 491 0,17734 0,40849 0,0615 0,3469 0,73014 1 0,93474 1 0,99203 1 PMPCB ENSG00000105819.13 ENST00000453466.1 18 120 85 0,213684658 0,3629 0,76883 0,80597 -813,913803 -808,924074 0,006807509 -813,840738 0,00171378 1 A110 1266 422 0,19537 0,29376 0,0481 0,2462 0,4863 1 0,77581 1 0,8086 1
RRP9 RRP9 N RRP9 list26_COV_list4dataset2nonOrf ribosomal RNA processing 9, U3 small nucleolar RNA binding protein RNU3IP2|U3-55K no 24 1431 477 0,11615 0,64947 0,0732 0,6301 0,73716 1 0,9087 1 1 1 RRP9 ENSG00000114767.6 ENST00000232888.6 15 476 98,1092437 0,092429478 0,2269 0,48016 0,48015 -3052,516617 -3052,516695 0,999922003 RRP9 1 1 984 328 0,109 0,36934 0,0421 0,3859 1 1 1 1 1 1
NIN NIN nsp13 NIN list26_COV_list4dataset2nonOrf ninein SCKL7 no 24 6408 2136 0,30293 0,48783 0,0957 0,3158 0,76324 1 0,94107 1 0,95656 1 NIN ENSG00000100503.23 ENST00000245441.9 18 2134 98,87535145 0,202455774 0,1696 0,44163 0,44404 -14232,14747 -14231,53816 0,543722648 NIN 0,00019421 0,016491925 ninein This gene encodes one of the proteins important for centrosomal function. This protein is important for positioning and anchoring the microtubules minus-ends in epithelial cells. Localization of this protein to the centrosome requires three leucine zippers in the central coiled-coil domain. 5415 1805 0,30366 0,31705 0,0633 0,2084 0,36495 1 0,66019 1 0,70868 1
CUL2 CUL2 orf10 CUL2 list25_COV_list3dataset2orf cullin 2 - no 19 2295 765 0,0304 0,23 0,0077 0,2534 0,76743 1 0,31529 0,773163375796178 0,81961 1 2064 688 0,03811 0,14669 0,0063 0,166 1 1 1 1 0,99983 1
ADAM9 ADAM9 orf8 ADAM9 list25_COV_list3dataset2orf ADAM metallopeptidase domain 9 CORD9|MCMP|MDC9|Mltng no 24 2460 820 0,23426 0,32078 0,0577 0,2461 0,78746 1 0,91009 1 0,96496 1 2169 723 0,23717 0,23665 0,0439 0,1852 1 1 1 1 1 1
TMEM39B TMEM39B orf9c TMEM39B list24_COV_list2nonOrf transmembrane protein 39B - no 24 1479 493 0,02611 0,35618 0,0127 0,4876 0,79256 1 0,01562 0,103684482758621 0,54675 1 2,802 1,11455 1 27_S_0.931 TMEM39B ENSMLUG00000001098.2 ENSMLUT00000001096.2 14 493 97,76876268 0,045827183 0,2491 0,73561 0,75068 -3638,443569 -3637,831116 0,542019663 TMEM39B ENSG00000121775.17 ENST00000441402.5 15 194 97,42268041 0,284776497 0,1299 0,34659 0,35253 -1148,165807 -1146,539157 0,196587039 TMEM39B 0,801578622 1 1119 373 0,04573 0,15903 0,0097 0,2125 0,050521 0,353647 0,013311 0,1507275 0,033776 0,382463529411765 1,547 3,9348 1 16_S_0.950
RAB8A RAB8A nsp7 RAB8A list26_COV_list4dataset2nonOrf RAB8A, member RAS oncogene family MEL|RAB8 no 24 624 208 0,01061 0,28587 0,0043 0,4035 0,79362 1 0,79117 1 0,96637 1 RAB8A ENSMLUG00000006982.2 ENSMLUT00000006977.2 3 208 98,55769231 0,167179383 3,5313 1,98028 2,21968 -1315,811747 -1313,992403 0,162132075 459 153 0,01589 0,13078 0,0029 0,1843 0,96303 1 0,98434 1 0,99892 1
CYB5R3 CYB5R3 nsp7 CYB5R3 list24_COV_list2nonOrf cytochrome b5 reductase 3 B5R|DIA1 no 24 1005 335 0,07081 0,65259 0,0578 0,8156 0,80021 1 0,44949 0,961245027624309 0,97702 1 CYB5R3 ENSG00000100243.20 ENST00000361740.8 10 335 77,91044776 0,129886326 0,2462 0,47319 0,47859 -1581,374141 -1581,50339 0,878755128 CYB5R3 1 1 687 229 0,06525 0,32689 0,0299 0,4578 0,30143 1 0,46291 1 0,57986 1
GPAA1 GPAA1 orf9c GPAA1 list26_COV_list4dataset2nonOrf glycosylphosphatidylinositol anchor attachment 1 GAA1|GPIBD15|hGAA1 no 23 1866 622 0,07715 0,59115 0,048 0,6227 0,81654 1 0,29339 0,728742903225806 0,97893 1 GPAA1 0,943023482 1 1269 423 0,09819 0,34051 0,0325 0,3305 0,014814 0,14258475 0,051345 0,439285 0,11556 0,946608510638298 0,999 4,72883 1 256_A_0.961
TM2D3 TM2D3 orf8 TM2D3 list25_COV_list3dataset2orf TM2 domain containing 3 BLP2 no 24 744 248 0,15463 0,57108 0,0762 0,493 0,81998 1 0,86452 1 0,97514 1 TM2D3 ENSMLUG00000012867.2 ENSMLUT00000012868.2 7 168 77,97619048 0,125987331 1,0628 1,78587 1,78588 -1241,214775 -1241,214837 0,999938002 TM2D3 ENSG00000184277.12 ENST00000558677.5 6 21 90,47619048 0,392413606 0,0737 0,16384 0,16383 -93,941226 -93,941223 0,999997 TM2D3 0,987384863 1 570 190 0,15692 0,3176 0,0442 0,2817 0,19703 0,89243 0,32898 1 0,43578 1
COMT COMT nsp7 COMT list26_COV_list4dataset2nonOrf catechol-O-methyltransferase HEL-S-98n no 24 819 273 0,18931 1,04616 0,1861 0,9829 0,83136 1 0,5052 1 1 1 COMT ENSMLUG00000009290.2 ENSMLUT00000009278.2 12 269 76,57992565 0,238498277 0,5648 2,23819 2,36405 -2574,274077 -2564,587824 0,0000621 -2570,040918 0,000958436 2 L163, L31 COMT ENSG00000093010.13 ENST00000428707.1 14 152 89,47368421 0,318784335 0,1652 0,7361 0,75512 -1091,441756 -1088,752312 0,067918692 495 165 0,20246 0,49391 0,0907 0,4478 0,72222 1 0,80254 1 0,94991 1
SLC30A9 SLC30A9 M SLC30A9 list26_COV_list4dataset2nonOrf solute carrier family 30 member 9 BILAPES|C4orf1|GAC63|HUEL|ZNT9 no 24 1710 570 0,18206 0,30255 0,045 0,2471 0,83684 1 0,93433 1 0,97891 1 SLC30A9 ENSMLUG00000002605.2 ENSMLUT00000002607.2 5 568 95,77464789 0,206334706 0,1818 0,40627 0,41181 -3264,022594 -3262,675495 0,259993409 SLC30A9 ENSG00000014824.13 ENST00000513699.5 18 120 83,33333333 0,558165283 0,1582 0,60259 0,60259 -761,801825 -761,801825 1 SLC30A9 0,036645318 0,559873801 1488 496 0,20868 0,2108 0,0352 0,1685 0,94112 1 0,98931 1 0,99741 1
PLD3 PLD3 orf8 PLD3 list25_COV_list3dataset2orf phospholipase D family member 3 AD19|HU-K4|HUK4|SCA46 no 20 1473 491 0,10911 0,30464 0,0355 0,3251 0,85342 1 0,84194 1 1 1 PLD3 ENSMLUG00000007885.2 ENSMLUT00000007904.2 9 491 97,14867617 0,12395641 0,3311 0,89258 0,9003 -3676,996033 -3676,655612 0,711470731 PLD3 ENSG00000105223.19 ENST00000486134.1 17 71 95,77464789 0,119009659 0,3026 0,62383 0,65407 -454,915213 -454,783906 0,87694851 PLD3 0,988548595 1 1107 369 0,06302 0,11948 0,0088 0,1399 0,3198 1 0,2859 1 0,60966 1
CHPF CHPF orf8 CHPF list23_COV_list1orf chondroitin polymerizing factor CHSY2|CSS2 no 22 2331 777 0,05393 0,4563 0,0271 0,5028 0,85614 1 0,75958 1 0,98819 1 CHPF ENSMLUG00000010340.2 ENSMLUT00000010332.2 7 773 98,70633894 0,075455074 0,3363 0,7372 0,7372 -5413,519552 -5413,519654 0,999898005 CHPF ENSG00000123989.13 ENST00000373891.2 15 299 97,65886288 0,038916877 0,1553 0,31839 0,3229 -1631,411117 -1631,246076 0,847858941 CHPF 1 1 1518 506 0,0538 0,2465 0,0143 0,2652 1 1 1 1 0,99978 1
SRP19 SRP19 nsp8 SRP19 list24_COV_list2nonOrf signal recognition particle 19 - no 24 435 145 0,07161 0,21106 0,018 0,251 0,85759 1 0,97637 1 0,98377 1 SRP19 1 1 381 127 0,14093 0,09824 0,0137 0,0975 1 1 1 1 0,99998 1
FKBP7 FKBP7 orf8 FKBP7 list23_COV_list1orf FK506 binding protein 7 FKBP23|PPIase no 23 669 223 0,18199 0,44825 0,0677 0,3723 0,87213 1 0,96773 1 1 1 FKBP7 ENSMLUG00000005816.2 ENSMLUT00000005820.2 13 223 90,13452915 0,132702183 0,3392 0,90653 0,90653 -1686,218543 -1686,218553 0,99999 FKBP7 ENSG00000079150.17 ENST00000412612.1 19 67 92,53731343 0,0657558 0,2131 0,32603 0,32604 -349,163973 -349,164088 0,999885007 546 182 0,17325 0,33256 0,0498 0,2874 1 1 1 1 1 1
DDX10 DDX10 nsp8 DDX10 list24_COV_list2nonOrf DEAD-box helicase 10 Dbp4|HRH-J8 no 24 2664 888 0,20192 0,51551 0,0826 0,409 0,87746 1 0,98818 1 1 1 DDX10 ENSMLUG00000001645.2 ENSMLUT00000001650.2 7 668 93,41317365 0,151130157 0,2951 0,74067 0,74294 -4657,636879 -4656,832291 0,447272165 DDX10 1 1 2199 733 0,23126 0,37751 0,0655 0,2831 0,91769 1 1 1 1 1
ATP6V1A ATP6V1A M ATP6V1A list26_COV_list4dataset2nonOrf ATPase H+ transporting V1 subunit A ARCL2D|ATP6A1|ATP6V1A1|HO68|IECEE3|VA68|VPP2|Vma1 no 24 1854 618 0,03667 0,3035 0,0122 0,3318 0,8936 1 0,98854 1 1 1 ATP6V1A ENSMLUG00000003808.2 ENSMLUT00000003810.2 10 618 99,51456311 0,036816946 0,2405 0,47687 0,47455 -3838,66641 -3838,735925 0,932846141 ATP6V1A 1 1 1536 512 0,05058 0,18094 0,0094 0,1854 0,47978 1 0,29455 1 0,77909 1
AAR2 AAR2 M AAR2 list26_COV_list4dataset2nonOrf AAR2 splicing factor homolog C20orf4|CGI-23 no 24 1155 385 0,10697 0,43296 0,0468 0,4372 0,90865 1 0,993 1 1 1 AAR2 ENSMLUG00000011977.1 ENSMLUT00000011973.1 14 385 98,44155844 0,121314111 0,2847 1,07113 1,07113 -3321,28142 -3321,28143 0,99999 AAR2 ENSG00000131043.11 ENST00000373932.3 17 385 99,22077922 0,098793106 0,1997 0,37743 0,37786 -2314,613474 -2314,58797 0,97481848 AAR2 0,29088992 1 957 319 0,13822 0,2348 0,031 0,2242 1 1 1 1 1 1
SELENOS VIMP nsp7 VIMP list26_COV_list4dataset2nonOrf selenoprotein S protein-coding AD-015|ADO15|SBBI8|SELS|SEPS1|VIMP no 24 567 189 0,21832 0,68129 0,1108 0,5076 0,93424 1 0,91359 1 0,99621 1 SELENOS ENSG00000131871.14 ENST00000528346.1 9 218 83,02752294 0,295972578 0,1231 0,34109 0,34109 -1105,646929 -1105,646929 1 408 136 0,1532 0,50802 0,0684 0,4462 0,65661 1 0,78342 1 0,90578 1
NAT14 NAT14 nsp7 NAT14 list26_COV_list4dataset2nonOrf N-acetyltransferase 14 (putative) KLP1 no 22 621 207 0,04689 0,58635 0,0362 0,773 0,97917 1 1 1 0,99958 1 NAT14 ENSG00000090971.4 ENST00000588985.1 18 173 92,48554913 0,485640999 0,2943 1,13529 1,55491 -1403,667641 -1368,700723 6,52E-016 -1402,496799 2,01E-016 9 S147, L149, S150, P152, Q153, A154, R156, A157, I158 411 137 0,05402 0,27709 0,0199 0,3693 1 1 0,39089 1 0,92468 1
MYCBP2 MYCBP2 nsp12 MYCBP2 list26_COV_list4dataset2nonOrf MYC binding protein 2, E3 ubiquitin protein ligase Myc-bp2|PAM|Phr no 22 14037 4679 0,0308 0,25436 0,0089 0,2894 0,98184 1 0,43704 0,961245027624309 1 1 MYCBP2 ENSMLUG00000006495.2 ENSMLUT00000006549.2 7 4440 97,7027027 0,025368008 0,3005 0,51156 0,51209 -26822,6496 -26820,87724 0,169931987 MYCBP2 ENSG00000005810.17 ENST00000612956.4 16 276 84,7826087 0,0556612 0,0881 0,1688 0,17528 -1164,045643 -1162,450136 0,20280568 MYCBP2 0,988058803 1 12465 4155 0,04038 0,16491 0,0075 0,1869 1 1 0,24673 1 1 1
ERC1 ERC1 nsp13 ERC1 list26_COV_list4dataset2nonOrf ELKS/RAB6-interacting/CAST family member 1 Cast2|ELKS|ERC-1|RAB6IP2 no 23 3372 1124 0,03114 0,36432 0,0126 0,4043 0,99056 1 1 1 1 1 ERC1 ENSMLUG00000008287.2 ENSMLUT00000008314.2 10 932 95,60085837 0,021071587 0,3414 0,49232 0,49314 -5619,619103 -5618,773256 0,429193677 ERC1 0,991406686 1 2814 938 0,041 0,24883 0,0108 0,2625 0,99661 1 0,57235 1 1 1
DCAF7 DCAF7 nsp9 DCAF7 list24_COV_list2nonOrf DDB1 and CUL4 associated factor 7 AN11|HAN11|SWAN-1|WDR68 no 24 1029 343 0,0001 0,23939 0 0,3147 0,99056 1 1 1 0,99836 1 DCAF7 ENSMLUG00000012623.2 ENSMLUT00000012630.2 12 344 98,25581395 0,006208383 0,2911 0,5473 0,5473 -2206,816373 -2206,818012 0,998362342 DCAF7 ENSG00000136485.14 ENST00000615512.1 18 206 100 0,0297002 0,1034 0,20993 0,20994 -1098,377869 -1098,378045 0,999824015 DCAF7 0,992953366 1 819 273 0,0001 0,09041 0 0,1256 1 1 1 1 0,99935 1
FBLN5 FBLN5 nsp9 FBLN5 list26_COV_list4dataset2nonOrf fibulin 5 ADCL2|ARCL1A|ARMD3|DANCE|EVEC|FIBL-5|HNARMD|UP50 no 24 1347 449 0,03177 0,34357 0,0124 0,3908 0,99314 1 0,60486 1 1 1 FBLN5 ENSMLUG00000011592.2 ENSMLUT00000011600.2 10 443 95,03386005 0,072138666 0,3908 1,02413 1,01319 -3407,255769 -3403,439564 0,022011175 -3404,234477 0,207350995 FBLN5 ENSG00000140092.14 ENST00000557570.1 14 36 91,66666667 0,162003072 0,1315 0,27035 0,39032 -175,924339 -169,671374 0,001924739 -175,883204 0,000423929 1 Q26 FBLN5 0,952555539 1 1005 335 0,03055 0,20975 0,008 0,2623 0,77156 1 0,45009 1 0,95874 1
CSDE1 CSDE1 orf9b CSDE1 list25_COV_list3dataset2orf cold shock domain containing E1 D1S155E|UNR no 22 2535 845 0,03244 0,18065 0,0066 0,2039 0,99342 1 0,97742 1 1 1 CSDE1 1 1 2220 740 0,03113 0,12333 0,0044 0,141 1 1 1 1 0,99985 1
ELOC TCEB1 orf10 TCEB1 list25_COV_list3dataset2orf elongin C SIII|TCEB1 no 22 342 114 0,0001 0,15603 0 0,1838 0,99483 1 1 1 0,99964 1 300 100 0,0001 0,09218 0 0,1258 1 1 1 1 0,99975 1
RAB14 RAB14 nsp7 RAB14 list26_COV_list4dataset2nonOrf RAB14, member RAS oncogene family FBP|RAB-14 no 24 648 216 0,0001 0,14521 0 0,1768 0,99535 1 1 1 0,99934 1 RAB14 ENSMLUG00000009818.2 ENSMLUT00000009814.2 6 216 98,61111111 0,1122 0,12105 0,12105 -969,858557 -969,859346 0,999211311 RAB14 ENSG00000119396.10 ENST00000451303.1 6 181 83,97790055 0,056879213 4,361 118,74761 118,74786 -2021,457931 -2021,458026 0,999905005 RAB14 1 1 579 193 0,0001 0,0741 0 0,1017 1 1 1 1 0,99961 1
BZW2 BZW2 M BZW2 list26_COV_list4dataset2nonOrf basic leucine zipper and W2 domains 2 HSPC028|MST017|MSTP017 no 23 1260 420 0,0001 0,2139 0 0,2168 0,99661 1 1 1 0,99859 1 BZW2 0,94738989 1 1074 358 0,0001 0,1059 0 0,1084 1 1 1 1 0,99933 1
VPS11 VPS11 orf3a VPS11 list23_COV_list1orf VPS11, CORVET/HOPS core subunit END1|HLD12|PEP5|RNF108|hVPS11 no 24 2826 942 0,04236 0,3332 0,0151 0,3575 0,99681 1 0,10871 0,391152803738318 1 1 VPS11 ENSMLUG00000004793.2 ENSMLUT00000004798.2 13 943 98,83351007 0,055926261 0,3084 0,82222 0,824 -7277,956573 -7277,724273 0,792708275 VPS11 0,402188916 1 2307 769 0,03257 0,20794 0,0079 0,2425 0,80883 1 0,15067 0,878908333333333 0,97115 1
CIT CIT nsp13 CIT list26_COV_list4dataset2nonOrf citron rho-interacting serine/threonine kinase CITK|CRIK|MCPH17|STK21 no 24 6210 2070 0,03889 0,33654 0,014 0,3603 0,99887 1 0,10317 0,374721226415094 1 1 CIT ENSMLUG00000015398.2 ENSMLUT00000015418.2 9 2053 99,75645397 0,049639278 0,2704 0,58266 0,58355 -13769,99377 -13769,98439 0,990667818 CIT 0,981289664 1 4941 1647 0,05523 0,16649 0,0092 0,1672 0,28546 1 0,23839 1 0,56482 1
SLC30A6 SLC30A6 orf9c SLC30A6 list26_COV_list4dataset2nonOrf solute carrier family 30 member 6 MST103|MSTP103|ZNT6 no 24 1506 502 0,10083 0,265 0,027 0,2674 0,99887 1 0,99051 1 1 1 SLC30A6 ENSG00000152683.14 ENST00000440718.5 17 178 100 0,193019452 0,1015 0,16291 0,17605 -894,734632 -894,148265 0,556344818 SLC30A6 1 1 1356 452 0,12835 0,21114 0,0261 0,203 1 1 0,99716 1 1 1
DPY19L1 DPY19L1 orf9c DPY19L1 list26_COV_list4dataset2nonOrf dpy-19 like C-mannosyltransferase 1 - no 24 2247 749 0,12771 0,623 0,07 0,5478 1 1 1 1 1 1 DPY19L1 ENSMLUG00000013912.2 ENSMLUT00000013920.2 10 654 95,10703364 0,066641354 0,8845 1,9287 3,7297 -6487,933389 -6444,860734 1,97E-019 -6478,465185 2,44E-016 2 R421, S422 DPY19L1 1 1 1863 621 0,13685 0,36378 0,0436 0,3185 1 1 1 1 1 1
RTN4 RTN4 M RTN4 list26_COV_list4dataset2nonOrf reticulon 4 ASY|NI220/250|NOGO|NSP|NSP-CL|Nbla00271|Nbla10545|RTN-X|RTN4-A|RTN4-B1|RTN4-B2|RTN4-C no 25 3588 1196 0,39144 0,49586 0,1138 0,2907 1 1 1 1 1 1 RTN4 ENSMLUG00000009620.2 ENSMLUT00000009615.2 3 1182 93,82402707 0,35406443 0,2724 0,57599 1,48161 -6918,969966 -6896,923627 0,000000000266 -6918,554509 0,0000000000479 3 Q169, R170, P167 RTN4 ENSG00000115310.17 ENST00000402434.6 11 346 94,79768786 0,191128322 0,2098 0,53405 0,53248 -2201,323838 -2201,097352 0,797330505 2940 980 0,48069 0,34429 0,0888 0,1848 1 1 1 1 1 1
AP2M1 AP2M1 nsp10 AP2M1 list24_COV_list2nonOrf adaptor related protein complex 2 subunit mu 1 AP50|CLAPM1|mu2 no 23 1308 436 0,00997 0,22811 0,0031 0,3064 1 1 1 1 0,99964 1 AP2M1 ENSMLUG00000013541.2 ENSMLUT00000013545.2 11 442 99,32126697 0,009907453 0,2441 0,42519 0,46649 -2586,621611 -2570,307583 0,0000000822 -2576,454425 0,000454494 1 P192 AP2M1 ENSG00000161203.13 ENST00000621863.4 3 435 98,3908046 0,098695777 0,155 0,17927 0,17927 -2042,490635 -2042,490702 0,999933002 AP2M1 0,973065434 1 1017 339 0,0001 0,12605 0 0,1923 1 1 1 1 0,99883 1
RNF41 RNF41 nsp15 RNF41 list26_COV_list4dataset2nonOrf ring finger protein 41 FLRF|NRDP1|SBBI03 no 24 954 318 0,00561 0,24274 0,0015 0,2718 1 1 0,99375 1 0,9998 1 RNF41 ENSMLUG00000006057.2 ENSMLUT00000006056.2 14 318 98,11320755 0,096264505 0,5611 2,00581 2,53274 -3299,790746 -3312,670484 0,00000255 -3299,446508 0,000000271 3 C120, I194, R178 RNF41 ENSG00000181852.17 ENST00000615206.4 17 318 99,05660377 0,1463 0,19289 0,1929 -1584,778166 -1584,779342 0,998824691 747 249 0,0001 0,1218 0 0,1501 1 1 1 1 1 1
TOR1A TOR1A orf8 TOR1A list25_COV_list3dataset2orf torsin family 1 member A DQ2|DYT1 no 24 999 333 0,07967 0,44641 0,0385 0,4838 1 1 1 1 1 1 TOR1A ENSMLUG00000006890.2 ENSMLUT00000006884.2 5 371 76,28032345 0,139303891 0,8003 1,48619 1,64303 -2351,61907 -2341,490297 0,0000399 -2344,046035 0,023768091 3 S65, S75, N83 TOR1A ENSG00000136827.11 ENST00000351698.4 14 333 91,89189189 0,121829084 0,2238 0,34598 0,34943 -1839,341021 -1835,394373 0,019319352 -1838,736927 0,009722163 2 S57, E59 TOR1A 1 1 759 253 0,12827 0,29153 0,0347 0,2703 0,21479 0,929147752808989 0,40056 1 0,46022 1
PSMD8 PSMD8 M PSMD8 list26_COV_list4dataset2nonOrf proteasome 26S subunit, non-ATPase 8 HEL-S-91n|HIP6|HYPF|Nin1p|Rpn12|S14|p31 no 24 1053 351 0,09383 0,50047 0,0581 0,6188 1 1 0,77716 1 1 1 PSMD8 ENSMLUG00000009902.2 ENSMLUT00000009898.2 7 341 88,85630499 0,126455804 0,3519 0,68178 0,77693 -2063,089941 -2054,398826 0,000168073 -2062,954793 0,0000352 5 E32, A34, L28, S109, V14 PSMD8 0,983727412 1 732 244 0,0832 0,29688 0,0318 0,3823 1 1 1 1 1 1
SLC44A2 SLC44A2 E SLC44A2 list24_COV_list2nonOrf solute carrier family 44 member 2 CTL2|PP1292 no 24 2127 709 0,05217 0,49832 0,0306 0,586 1 1 0,50379 1 1 1 SLC44A2 ENSMLUG00000006644.2 ENSMLUT00000006652.2 14 706 84,70254958 0,094531946 0,3716 1,00538 1,01985 -5241,309467 -5233,432311 0,00037931 -5239,946596 0,000306775 1 G126 SLC44A2 ENSG00000129353.14 ENST00000586549.1 18 148 97,97297297 0,020166154 0,1963 0,36902 0,37317 -858,571302 -858,061344 0,6005208 SLC44A2 0,001390998 0,070168603 1590 530 0,06982 0,29745 0,0231 0,3303 1 1 1 1 1 1
MOV10 MOV10 N MOV10 list26_COV_list4dataset2nonOrf Mov10 RISC complex RNA helicase fSAP113|gb110 no 24 3012 1004 0,0581 0,29374 0,0184 0,3167 1 1 1 1 1 1 MOV10 ENSMLUG00000009815.2 ENSMLUT00000009826.2 12 896 86,27232143 0,214017978 0,3471 1,48397 1,51014 -7834,294675 -7826,58329 0,000447701 -7830,832117 0,003556047 1 T202 MOV10 ENSG00000155363.18 ENST00000357443.2 17 1004 99,00398406 0,057554153 0,1226 0,26332 0,26367 -5540,969219 -5540,930533 0,962052746 MOV10 0,099789404 0,957471912 2391 797 0,05359 0,17042 0,01 0,1865 1 1 1 1 0,99967 1
RAB10 RAB10 nsp7 RAB10 list24_COV_list2nonOrf RAB10, member RAS oncogene family - no 24 603 201 0,01254 0,14137 0,0022 0,179 1 1 1 1 0,99981 1 RAB10 ENSMLUG00000009937.2 ENSMLUT00000009931.2 4 201 87,56218905 0,949840879 0,0764 3,28637 11,64957 -1449,293237 -1442,149102 0,000789481 -1446,23852 0,004238195 2 K136, S18 RAB10 0,991187256 1 504 168 0,03665 0,08073 0,0029 0,0801 1 1 0,96527 1 0,99998 1
NEU1 NEU1 orf8 NEU1 list23_COV_list1orf neuraminidase 1 NANH|NEU|SIAL1 no 23 1251 417 0,14615 0,50214 0,0634 0,4336 1 1 1 1 0,80574 1 NEU1 ENSMLUG00000004882.2 ENSMLUT00000004881.2 12 417 83,45323741 0,09628246 0,4298 1,22209 1,23249 -3224,358725 -3218,354531 0,002468378 -3221,193981 0,017170129 2 V115, G27 NEU1 0,093714705 0,922593508 873 291 0,11676 0,25249 0,0268 0,2295 1 1 1 1 0,99982 1
MIB1 MIB1 nsp9 MIB1 list24_COV_list2nonOrf mindbomb E3 ubiquitin protein ligase 1 DIP-1|DIP1|LVNC7|MIB|ZZANK2|ZZZ6 no 24 3021 1007 0,00559 0,26166 0,0018 0,3281 1 1 1 1 0,99846 1 MIB1 ENSMLUG00000004022.2 ENSMLUT00000004026.2 11 931 99,67776584 0,007742272 0,2488 0,47316 0,47675 -5717,506378 -5712,206308 0,004991245 -5713,054254 0,192826091 MIB1 ENSG00000101752.11 ENST00000261537.6 12 1007 92,85004965 0,026255584 0,123 0,18047 0,1825 -4704,826366 -4701,178491 0,026046419 -4701,735465 0,291225348 MIB1 0,88359204 1 2535 845 0,01116 0,14901 0,0022 0,1932 1 1 1 1 0,99945 1
TYSND1 TYSND1 nsp12 TYSND1 list24_COV_list2nonOrf trypsin domain containing 1 NET41 no 24 1701 567 0,19443 0,82089 0,1392 0,716 1 1 1 1 1 1 TYSND1 ENSMLUG00000024592.1 ENSMLUT00000029504.1 7 501 94,21157685 0,250696654 0,4319 1,40747 1,54659 -4264,492143 -4259,43093 0,006337867 -4263,3958 0,004862878 TYSND1 ENSG00000156521.13 ENST00000287078.6 15 567 95,23809524 0,264137739 0,2301 0,68808 0,68879 -4116,047002 -4116,042735 0,995742091 TYSND1 0,97075562 1 921 307 0,29466 0,41853 0,0862 0,2927 0,75011 1 0,94656 1 0,9368 1
USP13 USP13 nsp13 USP13 list26_COV_list4dataset2nonOrf ubiquitin specific peptidase 13 ISOT3|IsoT-3 no 24 2592 864 0,05031 0,54252 0,0277 0,5498 1 1 1 1 1 1 USP13 ENSMLUG00000013714.2 ENSMLUT00000013725.2 6 852 84,50704225 0,051623599 0,3758 0,57302 0,58116 -4583,477402 -4578,42332 0,006383224 -4581,966746 0,007765153 1 T131 USP13 1 1 1896 632 0,06944 0,30839 0,0215 0,31 1 1 1 1 1 1
ZC3H7A ZC3H7A nsp12 ZC3H7A list24_COV_list2nonOrf zinc finger CCCH-type containing 7A HSPC055|ZC3H7|ZC3HDC7 no 24 2919 973 0,0703 0,28797 0,0222 0,3162 1 1 1 1 1 1 ZC3H7A ENSMLUG00000016057.2 ENSMLUT00000016063.2 14 972 96,39917695 0,145913877 0,2854 0,92464 0,92703 -7996,266014 -7992,041236 0,014628582 -7992,243575 0,524683178 ZC3H7A ENSG00000122299.11 ENST00000574995.1 17 60 100 0,08310466 0,1663 0,20635 0,20635 -306,624008 -306,624037 0,999971 ZC3H7A 1 1 2544 848 0,07866 0,16398 0,0145 0,1849 1 1 1 1 1 1
IMPDH2 IMPDH2 nsp14 IMPDH2 list24_COV_list2nonOrf inosine monophosphate dehydrogenase 2 IMPD2|IMPDH-II no 24 1545 515 0,02179 0,27103 0,0065 0,2968 1 1 1 1 0,99936 1 IMPDH2 ENSMLUG00000013965.2 ENSMLUT00000013972.2 14 515 86,21359223 0,012416405 0,4874 50,93829 50,93616 -4963,051792 -4959,072943 0,018707159 -4958,897176 0,553246052 IMPDH2 ENSG00000178035.11 ENST00000442157.1 15 279 99,28315412 0,345543694 1,5521 3,80863 3,96597 -3207,035275 -3203,3997 0,026368768 -3203,996416 0,274638373 IMPDH2 0,993717516 1 1293 431 0,03891 0,1682 0,0067 0,1732 1 1 1 1 0,99982 1
DNAJC11 DNAJC11 nsp4 DNAJC11 list26_COV_list4dataset2nonOrf DnaJ heat shock protein family (Hsp40) member C11 dJ126A5.1 no 23 1680 560 0,02848 0,37563 0,0119 0,4193 1 1 1 1 0,99889 1 DNAJC11 ENSMLUG00000016244.2 ENSMLUT00000016245.2 11 538 88,66171004 0,041426347 0,5631 1,15902 1,16651 -4039,894021 -4035,943317 0,019241151 -4037,422546 0,085429377 DNAJC11 1 1 1275 425 0,02296 0,18685 0,0054 0,2346 1 1 1 1 0,99958 1
NEK9 NEK9 nsp9 NEK9 list24_COV_list2nonOrf NIMA related kinase 9 APUG|LCCS10|NC|NERCC|NERCC1 no 24 2955 985 0,10566 0,27705 0,0265 0,2509 1 1 1 1 1 1 NEK9 ENSMLUG00000010541.2 ENSMLUT00000010565.2 9 978 90,08179959 0,068599531 0,2558 0,49516 0,4948 -5775,797711 -5771,999836 0,02241836 -5774,769078 0,018602691 3 A34, G35, S28 NEK9 0,368858038 1 2502 834 0,11821 0,19585 0,0207 0,1755 1 1 1 1 1 1
PLEKHF2 PLEKHF2 orf8 PLEKHF2 list23_COV_list1orf pleckstrin homology and FYVE domain containing 2 EAPF|PHAFIN2|ZFYVE18 no 23 750 250 0,05521 0,27314 0,0177 0,3212 1 1 1 1 1 1 PLEKHF2 ENSMLUG00000012204.1 ENSMLUT00000012203.1 15 250 98,8 0,028280086 0,2888 0,70329 0,70206 -1812,683687 -1808,892994 0,022579949 -1810,294442 0,094094212 651 217 0,06581 0,21058 0,0162 0,2459 1 1 1 1 1 1
IL17RA IL17RA orf8 IL17RA list25_COV_list3dataset2orf interleukin 17 receptor A CANDF5|CD217|CDw217|IL-17RA|IL17R|IMD51|hIL-17R no 24 2604 868 0,17786 0,9775 0,1649 0,9269 1 1 0,071071 0,286905208333333 1 1 7,642 1 0 noneOver_0.9 IL17RA ENSMLUG00000003654.2 ENSMLUT00000003658.2 8 810 89,01234568 0,28793215 0,4154 1,05462 1,09076 -5965,067892 -5961,704246 0,034608845 -5963,535414 0,055655549 1575 525 0,16855 0,55537 0,0919 0,5452 0,20625 0,90234375 0,070016 0,544313 0,44919 1 1,58 2,25443 0 noneOver_0.9
GNB1 GNB1 nsp7 GNB1 list24_COV_list2nonOrf G protein subunit beta 1 MRD42 no 23 1023 341 0,00386 0,28741 0,0014 0,3533 1 1 1 1 0,99906 1 GNB1 ENSMLUG00000004007.2 ENSMLUT00000004007.2 7 341 89,44281525 0,019662276 1,9099 14,43006 50,40283 -2216,034824 -2213,051297 0,050614003 GNB1 ENSG00000078369.17 ENST00000615252.4 19 241 99,17012448 0,035713833 1,7366 20,03193 20,13779 -2588,528 -2588,529064 0,998936566 GNB1 0,993518026 1 717 239 0,0001 0,08605 0 0,1138 0,99578 1 1 1 0,99949 1
GRPEL1 GRPEL1 nsp10 GRPEL1 list24_COV_list2nonOrf GrpE like 1, mitochondrial GrpE|HMGE|mt-GrpE#1 no 22 654 218 0,08526 0,5483 0,0504 0,5915 1 1 1 1 1 1 GRPEL1 ENSMLUG00000008977.2 ENSMLUT00000008979.2 9 220 80 0,14925679 0,525 1,41536 1,54259 -1577,493821 -1574,733486 0,063270569 GRPEL1 ENSG00000109519.12 ENST00000264954.4 18 218 94,03669725 0,080541871 0,254 0,53586 0,53586 -1427,756159 -1427,756167 0,999992 GRPEL1 0,627530857 1 489 163 0,07023 0,3454 0,0281 0,3999 0,36343 1 0,5754 1 0,6579 1
ARF6 ARF6 nsp15 ARF6 list26_COV_list4dataset2nonOrf ADP ribosylation factor 6 - no 23 528 176 0,00829 0,16737 0,0024 0,2869 1 1 1 1 0,9997 1 ARF6 ENSMLUG00000010869.1 ENSMLUT00000010859.1 14 176 94,31818182 0,025085904 0,1572 0,29711 0,33603 -890,50429 -887,871119 0,071850263 ARF6 ENSG00000165527.6 ENST00000298316.6 18 176 98,29545455 0,00434569 0,1225 0,16533 0,16533 -826,751641 -826,751992 0,999649062 ARF6 0,085998489 0,881444321 372 124 0,02028 0,0951 0,0034 0,1664 1 1 1 1 0,99992 1
FKBP10 FKBP10 orf8 FKBP10 list25_COV_list3dataset2orf FK506 binding protein 10 BRKS1|FKBP65|OI11|OI6|PPIASE|hFKBP65 no 24 1749 583 0,05854 0,48767 0,036 0,6153 1 1 0,80844 1 1 1 FKBP10 ENSMLUG00000011865.2 ENSMLUT00000011863.2 12 583 91,42367067 0,09180607 0,3317 1,12346 1,12763 -4623,843793 -4621,404193 0,087195723 FKBP10 ENSG00000141756.18 ENST00000455106.1 10 386 98,1865285 0,303872044 0,2268 0,56093 0,63872 -2473,075379 -2461,017224 0,0000058 -2473,053949 0,000000927 7 R112, K149, A154, D155, S143, Q157, G141 FKBP10 0,690158345 1 1188 396 0,0486 0,24802 0,0155 0,3179 1 1 1 1 1 1
PABPC4 PABPC4 N PABPC4 list26_COV_list4dataset2nonOrf poly(A) binding protein cytoplasmic 4 APP-1|APP1|PABP4|iPABP no 24 1983 661 0,02748 0,22293 0,0065 0,2366 1 1 0,28405 0,724233443708609 1 1 PABPC4 ENSMLUG00000012068.2 ENSMLUT00000012071.2 4 616 88,7987013 0,152907163 4,0876 3,43137 3,54991 -5318,31677 -5316,135205 0,112864759 PABPC4 0,650870822 1 1620 540 0,03302 0,14794 0,0053 0,1593 0,98714 1 0,6042 1 0,99987 1
HMOX1 HMOX1 orf3a HMOX1 list23_COV_list1orf heme oxygenase 1 HMOX1D|HO-1|HSP32|bK286B10 no 24 867 289 0,17957 0,66242 0,1133 0,6311 1 1 0,84428 1 1 1 HMOX1 ENSMLUG00000008815.2 ENSMLUT00000008807.2 12 285 81,75438596 0,23859892 0,3776 1,60867 1,66099 -2454,648823 -2452,499544 0,116568173 HMOX1 ENSG00000100292.16 ENST00000216117.8 18 289 91,69550173 0,26105194 0,1867 0,70724 0,71221 -2045,708694 -2045,643886 0,937247398 630 210 0,22543 0,28407 0,0546 0,2423 0,57248 1 0,84928 1 0,85275 1
FOXRED2 FOXRED2 orf8 FOXRED2 list25_COV_list3dataset2orf FAD dependent oxidoreductase domain containing 2 ERFAD no 24 2055 685 0,14696 0,6933 0,1031 0,7014 1 1 0,94946 1 1 1 FOXRED2 ENSMLUG00000015845.2 ENSMLUT00000015850.2 11 687 94,46870451 0,153632497 0,3489 1,27604 1,28555 -6076,886155 -6075,060307 0,16108099 FOXRED2 ENSG00000100350.14 ENST00000423980.1 18 86 100 0,260056426 0,3509 0,76529 0,76529 -605,489707 -605,489713 0,999994 FOXRED2 1 1 1338 446 0,13198 0,34456 0,0479 0,3626 1 1 1 1 1 1
NOL10 NOL10 nsp8 NOL10 list24_COV_list2nonOrf nucleolar protein 10 PQBP5 no 24 2067 689 0,09925 0,40974 0,0388 0,3911 1 1 1 1 1 1 NOL10 ENSMLUG00000012309.2 ENSMLUT00000012324.2 7 689 91,5820029 0,04711 0,5473 0,67759 0,67845 -4316,213081 -4314,723413 0,225447492 NOL10 ENSG00000115761.15 ENST00000538384.5 16 663 90,79939668 0,079673673 0,1878 0,34335 0,3476 -3573,778847 -3573,123958 0,519499724 NOL10 0,945985014 1 1728 576 0,1001 0,26531 0,0261 0,2603 1 1 1 1 1 1
SRP72 SRP72 nsp8 SRP72 list24_COV_list2nonOrf signal recognition particle 72 BMFF|BMFS1|HEL103 no 24 2016 672 0,10809 0,29053 0,0293 0,2714 1 1 1 1 0,99947 1 SRP72 ENSMLUG00000007344.2 ENSMLUT00000007361.2 11 676 97,18934911 0,050821197 0,2715 0,50612 0,50653 -4309,948669 -4308,540031 0,244476033 SRP72 0,622236978 1 1767 589 0,11538 0,20629 0,0229 0,1988 1 1 1 1 0,99971 1
ZDHHC5 ZDHHC5 S ZDHHC5 list24_COV_list2nonOrf zinc finger DHHC-type containing 5 DHHC5|ZNF375 no 24 2148 716 0,12268 0,2455 0,0259 0,2108 1 1 1 1 1 1 ZDHHC5 ENSMLUG00000007907.2 ENSMLUT00000007912.2 13 716 97,48603352 0,064428918 0,2339 0,56354 0,56388 -4860,870289 -4859,602846 0,281550627 ZDHHC5 ENSG00000156599.10 ENST00000529447.1 14 300 99 0,080413163 0,1002 0,16659 0,16651 -1522,725007 -1522,654007 0,931461892 ZDHHC5 1 1 1848 616 0,12365 0,18502 0,0202 0,1636 0,99805 1 0,9804 1 1 1
PTGES2 PTGES2 nsp7 PTGES2 list26_COV_list4dataset2nonOrf prostaglandin E synthase 2 C9orf15|GBF-1|GBF1|PGES2|mPGES-2 no 24 1134 378 0,07702 0,72054 0,0775 1,0067 1 1 0,48597 1 1 1 PTGES2 ENSMLUG00000013576.2 ENSMLUT00000013581.2 8 375 80 0,203412788 0,4166 1,20399 1,24465 -2592,589381 -2591,41349 0,308543945 PTGES2 1 1 696 232 0,04953 0,29556 0,0245 0,4938 1 1 0,059658 0,49931152173913 0,99987 1 0,043 422,17375 0 noneOver_0.9
TMED5 TMED5 orf9c TMED5 list26_COV_list4dataset2nonOrf transmembrane p24 trafficking protein 5 CGI-100|p24g2|p28 no 24 690 230 0,20601 0,25994 0,0403 0,1956 1 1 1 1 1 1 TMED5 ENSMLUG00000005855.2 ENSMLUT00000005854.2 12 230 96,95652174 0,171524106 0,3374 0,7916 0,79264 -1789,058873 -1788,027614 0,356557772 TMED5 ENSG00000117500.12 ENST00000370280.1 18 163 98,1595092 0,121305926 0,1413 0,23614 0,23614 -862,837429 -862,837432 0,999997 TMED5 1 1 618 206 0,24636 0,23169 0,0404 0,1639 1 1 1 1 0,99995 1
HOOK1 HOOK1 nsp13 HOOK1 list26_COV_list4dataset2nonOrf hook microtubule tethering protein 1 HK1 no 24 2193 731 0,10862 0,31513 0,0322 0,2964 1 1 1 1 1 1 HOOK1 ENSMLUG00000015536.2 ENSMLUT00000015546.2 11 730 93,69863014 0,063317564 0,2519 0,58065 0,58158 -4668,362609 -4667,393128 0,379279833 HOOK1 ENSG00000134709.10 ENST00000371208.3 17 729 95,61042524 0,088870594 0,1255 0,25743 0,25743 -3874,124599 -3874,124612 0,999987 HOOK1 0,502954854 1 1944 648 0,15747 0,22947 0,0303 0,1925 0,86968 1 1 1 1 1
POFUT1 POFUT1 orf8 POFUT1 list25_COV_list3dataset2orf protein O-fucosyltransferase 1 DDD2|FUT12|O-FUT|O-Fuc-T|O-FucT-1|OFUCT1 no 24 1167 389 0,10747 0,48828 0,0508 0,4726 1 1 1 1 1 1 POFUT1 ENSMLUG00000008418.2 ENSMLUT00000008424.2 12 392 97,70408163 0,184066525 0,359 1,28852 1,29612 -3796,585477 -3795,631984 0,385392494 POFUT1 ENSG00000101346.13 ENST00000375730.3 17 195 88,71794872 0,106511159 0,2531 0,37121 0,36961 -1023,094752 -1022,994131 0,904275688 POFUT1 1 1 894 298 0,13903 0,24831 0,0312 0,2248 1 1 1 1 1 1
SUN2 SUN2 orf3a SUN2 list25_COV_list3dataset2orf Sad1 and UNC84 domain containing 2 UNC84B no 24 2241 747 0,13437 0,69299 0,0913 0,6793 1 1 1 1 1 1 SUN2 ENSMLUG00000003774.2 ENSMLUT00000003780.2 10 756 93,12169312 0,211219405 0,3524 1,34381 1,36231 -6792,477488 -6791,727373 0,472312234 SUN2 ENSG00000100242.15 ENST00000405510.5 17 718 95,68245125 0,13547888 0,2157 0,53883 0,53883 -4701,790563 -4701,790571 0,999992 SUN2 1 1 1560 520 0,15132 0,27627 0,0417 0,2753 0,94003 1 0,99049 1 1 1
ERP44 ERP44 orf8 ERP44 list23_COV_list1orf endoplasmic reticulum protein 44 PDIA10|TXNDC4 no 21 1221 407 0,0756 0,16966 0,0134 0,177 1 1 1 1 0,9998 1 ERP44 ENSMLUG00000011752.2 ENSMLUT00000011749.2 10 407 94,34889435 0,141535311 0,2403 0,49346 0,49686 -2435,949038 -2435,276063 0,510188507 ERP44 0,295409685 1 1107 369 0,10469 0,12605 0,0133 0,127 1 1 1 1 0,99989 1
PRKACA PRKACA nsp13 PRKACA list26_COV_list4dataset2nonOrf protein kinase cAMP-activated catalytic subunit alpha PKACA|PPNAD4 no 20 1056 352 0,01831 0,28052 0,0076 0,4162 1 1 1 1 1 1 PRKACA ENSMLUG00000008255.2 ENSMLUT00000008252.2 9 243 96,70781893 0,066924939 0,3759 0,73304 0,75682 -1703,989269 -1703,456416 0,586928073 PRKACA ENSG00000072062.13 ENST00000308677.8 15 352 93,18181818 0,588676251 0,5711 1,35481 1,37944 -2935,873854 -2935,292462 0,55911953 786 262 0,04962 0,13391 0,0098 0,1969 0,74157 1 0,69838 1 1 1
GOLGA3 GOLGA3 nsp13 GOLGA3 list26_COV_list4dataset2nonOrf golgin A3 GCP170|MEA-2 no 24 4542 1514 0,13848 0,7478 0,0997 0,7199 1 1 1 1 1 1 GOLGA3 ENSMLUG00000001618.2 ENSMLUT00000001625.2 11 1392 95,47413793 0,179682799 0,535 1,57737 1,58271 -13303,62565 -13303,236 0,677291177 GOLGA3 ENSG00000090615.14 ENST00000450791.6 11 1499 96,93128753 0,130669865 0,2672 0,47551 0,50725 -8871,254649 -8855,888968 0,000000212 -8869,318424 0,000000219 2 K42, V43 GOLGA3 0,093020573 0,918542878 3216 1072 0,15255 0,3635 0,0521 0,3413 0,73337 1 0,71943 1 0,94555 1
ADAMTS1 ADAMTS1 orf8 ADAMTS1 list25_COV_list3dataset2orf ADAM metallopeptidase with thrombospondin type 1 motif 1 C3-C5|METH1 no 24 2910 970 0,11735 0,56648 0,0621 0,5288 1 1 1 1 1 1 ADAMTS1 ENSMLUG00000011098.2 ENSMLUT00000011104.2 5 965 96,16580311 0,081350858 0,4665 0,59051 0,59114 -5846,284175 -5846,013643 0,762973484 ADAMTS1 ENSG00000154734.14 ENST00000517452.1 19 128 94,53125 0,068152955 0,1427 0,27036 0,27394 -690,162298 -690,011798 0,86027773 ADAMTS1 1 1 2049 683 0,08577 0,29926 0,0259 0,3019 1 1 1 1 1 1
CSNK2B CSNK2B N CSNK2B list26_COV_list4dataset2nonOrf casein kinase 2 beta CK2B|CK2N|CSK2B|Ckb1|Ckb2|G5A no 23 648 216 0,06127 0,27347 0,0173 0,2817 1 1 1 1 1 1 CSNK2B ENSMLUG00000009414.2 ENSMLUT00000009411.2 13 236 86,86440678 0,004570907 0,3122 0,61783 0,6146 -1327,632217 -1327,37153 0,770522055 CSNK2B 1 1 537 179 0,02126 0,18748 0,005 0,2342 1 1 1 1 0,99975 1
THTPA THTPA orf10 THTPA list25_COV_list3dataset2orf thiamine triphosphatase THTP|THTPASE no 24 693 231 0,40061 0,60108 0,1372 0,3424 1 1 1 1 1 1 THTPA ENSMLUG00000016741.2 ENSMLUT00000016739.2 15 231 98,7012987 0,307948843 0,3508 1,57567 1,57684 -2692,924316 -2692,665785 0,772185093 THTPA ENSG00000259431.5 ENST00000556545.1 19 60 95 0,332747784 0,2145 0,75211 0,74901 -446,76741 -446,742424 0,975323566 570 190 0,46354 0,38389 0,0937 0,202 0,37349 1 0,64589 1 0,68145 1
PIGS PIGS orf9c PIGS list26_COV_list4dataset2nonOrf phosphatidylinositol glycan anchor biosynthesis class S - no 24 1674 558 0,13699 0,43919 0,0527 0,3848 1 1 1 1 1 1 PIGS ENSMLUG00000012363.2 ENSMLUT00000012364.2 14 556 95,14388489 0,113059233 0,3105 1,08827 1,09231 -4851,689763 -4851,470833 0,803377953 PIGS ENSG00000087111.20 ENST00000268758.13 10 42 88,0952381 0,238660577 0,1342 0,33801 0,4123 -201,283434 -200,746994 0,584826533 PIGS 1 1 1353 451 0,11267 0,30383 0,0307 0,2727 0,061489 0,374941875 1 1 1 1 0,001 1 0 noneOver_0.9
RBM41 RBM41 nsp12 RBM41 list26_COV_list4dataset2nonOrf RNA binding motif protein 41 - no 23 1242 414 0,25091 0,211 0,0395 0,1574 1 1 1 1 1 1 RBM41 ENSMLUG00000005943.2 ENSMLUT00000005943.2 10 414 99,27536232 0,164119253 0,1646 0,4821 0,48227 -2731,000804 -2730,808618 0,825153376 RBM41 ENSG00000089682.16 ENST00000434854.1 19 203 90,14778325 0,152327093 0,1286 0,35954 0,36015 -1078,840722 -1077,193702 0,192623071 1146 382 0,22717 0,1755 0,0316 0,1393 1 1 1 1 1 1
C1orf50 C1orf50 nsp13 C1orf50 list26_COV_list4dataset2nonOrf chromosome 1 open reading frame 50 - no 22 600 200 0,3773 0,34911 0,0779 0,2064 1 1 1 1 0,51939 1 C1orf50 ENSMLUG00000013623.2 ENSMLUT00000013623.2 12 199 97,48743719 0,310808096 0,2794 1,03296 1,03184 -1795,529196 -1795,400335 0,879096151 C1orf50 ENSG00000164008.14 ENST00000372525.5 14 200 98,5 0,266935677 0,0986 0,24533 0,24533 -1096,064024 -1096,064068 0,999956001 486 162 0,37034 0,23435 0,0497 0,1343 1 1 1 1 0,99998 1
TMEM97 TMEM97 orf9c TMEM97 list24_COV_list2nonOrf transmembrane protein 97 MAC30 no 24 531 177 0,17626 0,56778 0,0801 0,4544 1 1 1 1 1 1 TMEM97 ENSMLUG00000008705.2 ENSMLUT00000008690.2 5 177 96,04519774 0,308861546 0,3549 0,91347 0,92408 -1313,650608 -1313,522072 0,879381904 TMEM97 ENSG00000109084.13 ENST00000336687.6 19 70 90 0,127951045 0,3597 0,49482 0,49517 -404,504572 -404,521939 0,982782937 414 138 0,22013 0,38439 0,0621 0,2823 1 1 0,99968 1 1 1
RAB2A RAB2A nsp7 RAB2A list26_COV_list4dataset2nonOrf RAB2A, member RAS oncogene family LHX|RAB2 no 23 639 213 0,01381 0,13108 0,0022 0,1564 1 1 1 1 0,9999 1 RAB2A ENSMLUG00000002855.2 ENSMLUT00000002854.2 3 213 89,6713615 0,086657441 3,2719 2,06104 2,08099 -1254,284249 -1254,161503 0,884488294 RAB2A 0,936556195 1 561 187 0,03013 0,06406 0,0024 0,0788 1 1 1 1 1 1
STC2 STC2 orf8 STC2 list23_COV_list1orf stanniocalcin 2 STC-2|STCRP no 23 909 303 0,17486 0,54238 0,0883 0,505 1 1 1 1 1 1 STC2 ENSMLUG00000016389.2 ENSMLUT00000016385.2 10 303 98,67986799 0,204277098 0,585 1,68281 1,69703 -3126,652494 -3126,533313 0,887647122 STC2 ENSG00000113739.10 ENST00000518455.1 19 53 100 0,174638631 0,1251 0,21928 0,21954 -271,119848 -270,942741 0,837690147 STC2 0,650541589 1 624 208 0,2661 0,35986 0,0757 0,2844 1 1 0,99992 1 1 1
SMOC1 SMOC1 orf8 SMOC1 list25_COV_list3dataset2orf SPARC related modular calcium binding 1 OAS no 22 1308 436 0,0953 0,42631 0,0405 0,4253 1 1 0,9833 1 1 1 SMOC1 ENSMLUG00000011966.2 ENSMLUT00000011968.2 10 421 98,09976247 0,042088961 0,2562 0,50629 0,50745 -2637,844924 -2637,807862 0,963616389 SMOC1 ENSG00000198732.10 ENST00000381280.4 17 435 98,62068966 0,093384735 0,1563 0,40881 0,40871 -2715,53357 -2715,532979 0,999409175 SMOC1 1 1 1011 337 0,10432 0,25769 0,0273 0,2617 0,77188 1 0,92916 1 0,95673 1
FAM98A FAM98A N FAM98A list26_COV_list4dataset2nonOrf family with sequence similarity 98 member A - no 24 1572 524 0,0412 0,21585 0,0092 0,2224 1 1 1 1 0,99963 1 FAM98A ENSMLUG00000002455.2 ENSMLUT00000002452.2 12 526 84,79087452 0,102934391 0,2089 0,51286 0,51334 -3021,009729 -3020,975453 0,966304768 FAM98A 1 1 1377 459 0,04339 0,15011 0,0071 0,1639 1 1 1 1 0,99978 1
INTS4 INTS4 M INTS4 list26_COV_list4dataset2nonOrf integrator complex subunit 4 INT4|MST093 no 24 2895 965 0,06348 0,30522 0,0184 0,2891 1 1 1 1 1 1 INTS4 ENSMLUG00000005683.2 ENSMLUT00000005694.2 7 965 99,37823834 0,066035482 0,2457 0,40161 0,40172 -5761,213561 -5761,183937 0,97081049 INTS4 0,347662133 1 2541 847 0,0898 0,23188 0,0186 0,207 1 1 1 1 1 1
RAB7A RAB7A nsp7 RAB7A list24_COV_list2nonOrf RAB7A, member RAS oncogene family PRO2706|RAB7 no 24 624 208 0,01158 0,15723 0,0022 0,1928 1 1 1 1 0,99987 1 RAB7A ENSMLUG00000008454.2 ENSMLUT00000008464.2 3 208 94,23076923 0,389627735 0,1863 0,43618 0,43832 -1137,365631 -1137,353927 0,988364225 RAB7A ENSG00000075785.12 ENST00000485280.1 17 92 96,73913043 0,1067 0,08982 0,08982 -389,352117 -389,352321 0,999796021 RAB7A 0,536592405 1 519 173 0,0001 0,07921 0 0,1258 1 1 1 1 0,99957 1
HECTD1 HECTD1 nsp8 HECTD1 list24_COV_list2nonOrf HECT domain E3 ubiquitin protein ligase 1 EULIR no 22 7833 2611 0,01563 0,18765 0,0036 0,2333 1 1 1 1 0,99988 1 HECTD1 ENSMLUG00000013003.2 ENSMLUT00000013007.2 9 2611 97,05093834 0,01987373 0,187 0,37051 0,3705 -14548,28543 -14548,279 0,993590628 HECTD1 ENSG00000092148.12 ENST00000611816.4 12 2615 99,88527725 0,062418501 0,0473 0,09215 0,09008 -12010,3272 -11985,79417 0,0000000000222 -12002,43351 0,00000000799 1 G945 HECTD1 0,907809819 1 7056 2352 0,02104 0,13241 0,0035 0,1645 1 1 0,99819 1 1 1
STOML2 STOML2 orf3b STOML2 list23_COV_list1orf stomatin like 2 HSPC108|SLP-2 no 22 1071 357 0,09191 0,34197 0,0287 0,3118 1 1 1 1 1 1 STOML2 ENSMLUG00000006166.2 ENSMLUT00000006174.2 12 357 99,15966387 0,068343149 0,3235 0,75741 0,75764 -2702,938036 -2702,932391 0,994370903 STOML2 ENSG00000165283.15 ENST00000619795.4 18 311 99,03536977 0,075839176 0,1482 0,26471 0,26471 -1729,466179 -1729,466189 0,99999 STOML2 0,998045592 1 807 269 0,18347 0,20681 0,0295 0,1606 1 1 1 1 1 1
POR POR nsp2 POR list26_COV_list4dataset2nonOrf cytochrome p450 oxidoreductase CPR|CYPOR|P450R no 23 2043 681 0,04527 0,69983 0,0518 1,1451 1 1 0,9344 1 1 1 POR ENSMLUG00000017792.2 ENSMLUT00000017796.2 9 685 99,12408759 0,146073289 0,3303 0,75413 0,75409 -4874,958514 -4874,954571 0,996064763 POR 0,04458031 0,618876279 1284 428 0,05143 0,28952 0,0263 0,5117 0,62656 1 0,52077 1 0,89448 1
CSNK2A2 CSNK2A2 N CSNK2A2 list26_COV_list4dataset2nonOrf casein kinase 2 alpha 2 CK2A2|CK2alpha'|CSNK2A1 no 24 1053 351 0,01752 0,19671 0,004 0,2297 1 1 1 1 1 1 CSNK2A2 ENSMLUG00000000283.2 ENSMLUT00000000282.2 6 282 93,26241135 0,1352 50,09872 50,09883 -1796,781788 -1796,784621 0,997171009 CSNK2A2 ENSG00000070770.8 ENST00000565188.1 10 135 85,92592593 0,180818547 0,0661 0,05104 0,05104 -491,876359 -491,87636 0,999999 CSNK2A2 0,998404232 1 888 296 0,03261 0,11613 0,0047 0,1428 1 1 1 1 0,99997 1
HS2ST1 HS2ST1 nsp7 HS2ST1 list26_COV_list4dataset2nonOrf heparan sulfate 2-O-sulfotransferase 1 dJ604K5.2 no 24 1071 357 0,06867 0,18426 0,013 0,1888 1 1 1 1 1 1 HS2ST1 ENSMLUG00000030087.1 ENSMLUT00000023452.1 11 316 99,05063291 0,016449146 0,5151 0,7068 0,70682 -2111,210982 -2111,212841 0,998142727 HS2ST1 0,998871621 1 927 309 0,06724 0,1107 0,0076 0,1127 1 1 1 1 0,99994 1
DCAKD DCAKD nsp7 DCAKD list26_COV_list4dataset2nonOrf dephospho-CoA kinase domain containing - no 24 696 232 0,0853 0,57967 0,058 0,6794 1 1 0,82369 1 1 1 DCAKD ENSMLUG00000012210.2 ENSMLUT00000012210.2 14 134 100 0,018187335 0,456 1,09575 1,09582 -1090,412227 -1090,413531 0,99869685 DCAKD ENSG00000172992.11 ENST00000310604.8 17 232 93,53448276 0,123103624 0,2779 0,71979 0,72647 -1647,951202 -1647,856512 0,909654884 DCAKD 0,97028099 1 453 151 0,04095 0,2582 0,0127 0,3103 1 1 1 1 0,99978 1
SRP54 SRP54 nsp8 SRP54 list24_COV_list2nonOrf signal recognition particle 54 - no 24 1515 505 0,0001 0,1979 0 0,2624 1 1 1 1 0,99785 1 SRP54 ENSMLUG00000008958.2 ENSMLUT00000008994.2 10 505 60,59405941 0,017058519 0,2085 50,34695 50,34695 -2571,565569 -2571,566848 0,998721818 SRP54 ENSG00000100883.11 ENST00000630962.1 18 54 94,44444444 0,176513031 0,2524 0,88567 1,07571 -330,698886 -327,498447 0,040744313 -330,479792 0,014611731 3 L32, C36, T37 SRP54 0,995212721 1 1395 465 0,0001 0,11791 0 0,1664 1 1 1 1 0,99856 1
SDF2 SDF2 orf8 SDF2 list25_COV_list3dataset2orf stromal cell derived factor 2 - no 23 636 212 0,13507 0,3131 0,0364 0,2694 1 1 1 1 1 1 SDF2 ENSMLUG00000012419.2 ENSMLUT00000012419.2 15 212 98,58490566 0,06852197 0,335 0,90704 0,90706 -1790,437725 -1790,438975 0,998750781 SDF2 ENSG00000132581.9 ENST00000591903.1 19 105 88,57142857 0,073449063 4,3067 9,69242 9,69891 -726,542628 -726,543552 0,999076427 SDF2 0,997743245 1 522 174 0,10167 0,24504 0,0225 0,2215 1 1 0,96895 1 1 1
ZNF503 ZNF503 nsp9 ZNF503 list26_COV_list4dataset2nonOrf zinc finger protein 503 NOLZ-1|NOLZ1|Nlz2 no 24 1962 654 0,02521 0,4826 0,0172 0,6813 1 1 1 1 1 1 ZNF503 ENSMLUG00000012474.2 ENSMLUT00000012475.2 8 645 87,28682171 0,031926297 0,3166 0,63753 0,63754 -3729,002853 -3729,00402 0,998833681 ZNF503 ENSG00000165655.16 ENST00000372524.4 13 647 95,98145286 0,067052231 0,166 0,29355 0,29706 -3368,350648 -3368,028261 0,724417786 1050 350 0,02597 0,21013 0,0078 0,2986 1 1 1 1 0,99979 1
SBNO1 SBNO1 nsp12 SBNO1 list24_COV_list2nonOrf strawberry notch homolog 1 MOP3|Sno no 24 4188 1396 0,07513 0,35738 0,0296 0,3938 1 1 1 1 1 1 SBNO1 ENSMLUG00000017159.2 ENSMLUT00000017165.2 11 1393 91,74443647 0,020893622 0,515 1,06225 1,06226 -10339,1447 -10339,14578 0,998917586 SBNO1 1 1 3594 1198 0,08783 0,23818 0,0235 0,2676 1 1 1 1 1 1
RAE1 RAE1 orf6 RAE1 list23_COV_list1orf ribonucleic acid export 1 MIG14|MRNP41|Mnrp41|dJ481F12.3|dJ800J21.1 no 24 1107 369 0,00791 0,40585 0,0038 0,4796 1 1 1 1 0,99892 1 RAE1 ENSMLUG00000011112.1 ENSMLUT00000011117.1 9 369 97,56097561 0,013932194 1,0889 1,36722 1,36724 -2952,601049 -2952,601896 0,999153359 RAE1 ENSG00000101146.12 ENST00000452119.1 19 87 100 0,006947847 0,2844 0,37107 0,37113 -493,477386 -493,511378 0,966579237 RAE1 0,985506504 1 900 300 0,00677 0,2128 0,0016 0,2377 1 1 0,97368 1 0,99955 1
TUBGCP3 TUBGCP3 M TUBGCP3 list26_COV_list4dataset2nonOrf tubulin gamma complex associated protein 3 104p|ALP6|GCP3|Grip104|SPBC98|Spc98|Spc98p no 24 2724 908 0,06191 0,48631 0,0295 0,4765 1 1 1 1 1 1 TUBGCP3 ENSMLUG00000003007.2 ENSMLUT00000003016.2 3 907 99,44873208 0,060376747 0,3149 0,32284 0,32287 -4783,799705 -4783,798937 0,999232295 TUBGCP3 1 1 2145 715 0,08588 0,30473 0,0254 0,2959 0,99403 1 0,94154 1 1 1
UPF1 UPF1 N UPF1 list26_COV_list4dataset2nonOrf UPF1, RNA helicase and ATPase HUPF1|NORF1|RENT1|pNORF1|smg-2 no 24 3390 1130 0,00093 0,54693 0,0008 0,8847 1 1 1 1 0,99397 1 UPF1 ENSMLUG00000012122.2 ENSMLUT00000012134.2 3 1119 98,39142091 0,001231162 0,2225 0,20224 0,20225 -5134,073382 -5134,074101 0,999281258 UPF1 ENSG00000005007.12 ENST00000599848.5 12 1130 90,08849558 0,062638737 0,1437 0,42917 0,40573 -5850,36909 -5756,191655 1,26E-041 -5819,088195 3,41E-029 11 A65, A67, A69, A70, A71, Q73, L74, D75, A76, G61, P62 UPF1 1 1 2289 763 0,0001 0,23447 0 0,3784 1 1 1 1 0,99607 1
ERGIC1 ERGIC1 nsp10 ERGIC1 list24_COV_list2nonOrf endoplasmic reticulum-golgi intermediate compartment 1 AMCN|ERGIC-32|ERGIC32|NET24 no 24 873 291 0,01551 0,43192 0,0116 0,7458 1 1 1 1 1 1 ERGIC1 ENSMLUG00000009783.2 ENSMLUT00000009784.2 11 255 80 0,124503375 0,5262 51,43345 51,43347 -2688,831553 -2688,832073 0,999480135 ERGIC1 ENSG00000113719.15 ENST00000519796.5 19 54 94,44444444 0,243017671 0,1918 0,63351 0,88712 -364,327009 -360,880333 0,031851334 -364,22746 0,009672413 1 P49 ERGIC1 0,988383036 1 612 204 0,01329 0,15529 0,0041 0,3077 1 1 1 1 0,99995 1
TLE1 TLE1 nsp13 TLE1 list26_COV_list4dataset2nonOrf transducin like enhancer of split 1 ESG|ESG1|GRG1 no 24 2343 781 0,00794 0,34901 0,0031 0,3879 1 1 1 1 0,99854 1 TLE1 ENSMLUG00000003088.2 ENSMLUT00000003094.2 4 771 98,70298314 0,022314958 0,3974 0,45031 0,45031 -4298,583177 -4298,583652 0,999525113 TLE1 0,96270875 1 1749 583 0,01766 0,12851 0,0025 0,1406 1 1 1 1 0,99981 1
TOMM70 TOMM70A orf9b TOMM70A list25_COV_list3dataset2orf translocase of outer mitochondrial membrane 70 TOMM70A|Tom70 no 24 1827 609 0,05298 0,33556 0,0185 0,3495 1 1 0,62948 1 1 1 TOMM70 ENSMLUG00000004351.2 ENSMLUT00000004349.2 7 625 82,56 0,033943485 0,4243 0,49252 0,49252 -3102,802866 -3102,803279 0,999587085 TOMM70 ENSG00000154174.7 ENST00000284320.5 17 609 99,50738916 0,051737723 0,143 0,27585 0,27572 -3384,838087 -3384,292878 0,579720609 TOMM70A 1 1 1458 486 0,03332 0,18164 0,0067 0,2014 1 1 0,69563 1 1 1
WASHC4 KIAA1033 nsp2 KIAA1033 list24_COV_list2nonOrf WASH complex subunit 4 KIAA1033|MRT43|SWIP no 24 3525 1175 0,05497 0,24576 0,0145 0,2639 1 1 1 1 1 1 WASHC4 ENSMLUG00000007308.2 ENSMLUT00000007385.2 9 1174 94,46337308 0,033211761 0,2526 0,43697 0,43697 -6679,499731 -6679,500143 0,999588085 WASHC4 ENSG00000136051.13 ENST00000620430.4 16 1175 95,74468085 0,032295069 0,1108 0,18987 0,18987 -5740,674872 -5740,67551 0,999362203 KIAA1033 0,649735307 1 3150 1050 0,04959 0,16797 0,0092 0,1849 1 1 1 1 1 1
DPH5 DPH5 orf9b DPH5 list25_COV_list3dataset2orf diphthamide biosynthesis 5 AD-018|CGI-30|HSPC143|NPD015 no 24 864 288 0,16124 0,45711 0,0616 0,3821 1 1 1 1 1 1 DPH5 ENSMLUG00000012639.2 ENSMLUT00000012641.2 9 285 89,12280702 0,075085407 6,0816 12,7134 12,71389 -2883,965457 -2883,965869 0,999588085 DPH5 ENSG00000117543.20 ENST00000427040.2 18 47 93,61702128 0,374482718 0,2169 0,74627 0,75015 -347,53853 -347,358466 0,835216756 DPH5 1 1 726 242 0,25724 0,30547 0,056 0,2176 0,85476 1 0,91658 1 0,98338 1
UBAP2L UBAP2L nsp12 UBAP2L list26_COV_list4dataset2nonOrf ubiquitin associated protein 2 like NICE-4|NICE4 no 20 3264 1088 0,05012 0,1409 0,0065 0,1304 1 1 1 1 0,99967 1 UBAP2L ENSMLUG00000012931.2 ENSMLUT00000012940.2 11 1088 98,4375 0,059892632 0,2704 0,52839 0,52839 -7053,772669 -7053,773041 0,999628069 UBAP2L ENSG00000143569.18 ENST00000428931.5 9 1088 99,72426471 0,023452657 0,0273 0,03667 0,03668 -4683,160524 -4683,161165 0,999359205 UBAP2L 1 1 2865 955 0,06899 0,09125 0,0058 0,0835 1 1 1 1 0,9999 1
CHPF2 CHPF2 orf8 CHPF2 list25_COV_list3dataset2orf chondroitin polymerizing factor 2 CSGLCA-T|CSGlcAT|ChSy-3|chPF-2 no 21 2319 773 0,10299 0,3802 0,0347 0,337 1 1 0,96492 1 1 1 CHPF2 ENSMLUG00000003546.2 ENSMLUT00000003546.2 11 775 96,51612903 0,057756561 0,363 1,03035 1,03035 -6033,059791 -6033,060055 0,999736035 CHPF2 0,998871621 1 1758 586 0,08267 0,22172 0,0174 0,2103 0,91855 1 0,99004 1 0,99466 1
GFER GFER nsp10 GFER list24_COV_list2nonOrf growth factor, augmenter of liver regeneration ALR|ERV1|HERV1|HPO|HPO1|HPO2|HSS no 23 621 207 0,17771 0,58417 0,1039 0,5846 1 1 1 1 1 1 GFER ENSMLUG00000030630.1 ENSMLUT00000026775.1 13 131 77,09923664 0,088040816 0,4963 1,0421 1,04211 -885,563028 -885,56325 0,999778025 GFER ENSG00000127554.12 ENST00000567719.1 17 131 95,41984733 0,175267705 0,176 0,35474 0,35544 -740,439687 -739,923914 0,597038905 381 127 0,21278 0,3544 0,0676 0,3177 0,77182 1 0,89367 1 0,95882 1
TIMM8B TIMM8B orf10 TIMM8B list25_COV_list3dataset2orf translocase of inner mitochondrial membrane 8 homolog B DDP2|TIM8B no 23 252 84 0,0408 0,44668 0,0215 0,5263 1 1 1 1 1 1 TIMM8B ENSMLUG00000012225.1 ENSMLUT00000012226.1 12 84 96,42857143 0,153186519 0,4987 1,23336 1,23336 -763,084152 -763,084359 0,999793021 TIMM8B 0,992182485 1 192 64 0,02394 0,23213 0,007 0,294 1 1 1 1 1 1
RAB5C RAB5C nsp7 RAB5C list26_COV_list4dataset2nonOrf RAB5C, member RAS oncogene family L1880|RAB5CL|RAB5L|RABL no 24 750 250 0,0396 0,28983 0,0127 0,3195 1 1 1 1 0,9999 1 RAB5C ENSMLUG00000012028.2 ENSMLUT00000012028.2 4 231 98,7012987 0,023643529 0,1822 0,25581 0,25581 -1154,967665 -1154,967824 0,999841013 RAB5C ENSG00000108774.14 ENST00000393860.7 11 217 84,79262673 0,157854272 2,3996 7,12002 7,20502 -2592,156591 -2589,267353 0,055618578 RAB5C 0,088837574 0,897128617 555 185 0,03881 0,17157 0,0083 0,2127 1 1 1 1 0,9999 1
GOLGA7 GOLGA7 S GOLGA7 list26_COV_list4dataset2nonOrf golgin A7 GCP16|GOLGA3AP1|GOLGA7A|HSPC041 no 24 414 138 0,0001 0,26083 0 0,306 1 1 1 1 0,99936 1 GOLGA7 ENSMLUG00000014516.2 ENSMLUT00000014518.2 10 138 86,95652174 0,088463359 3,6554 3,39644 3,39646 -1178,46202 -1178,462175 0,999845012 345 115 0,0001 0,15139 0 0,1592 0,99593 1 1 1 0,9997 1
TIMM10 TIMM10 nsp4 TIMM10 list24_COV_list2nonOrf translocase of inner mitochondrial membrane 10 TIM10|TIM10A|TIMM10A no 24 273 91 0,0001 0,21377 0 0,3674 1 1 1 1 0,9995 1 TIMM10 ENSMLUG00000007883.1 ENSMLUT00000007868.1 15 91 96,7032967 0,014279045 0,3297 0,54196 0,54197 -562,40929 -562,409426 0,999864009 TIMM10 ENSG00000134809.8 ENST00000525158.1 19 91 96,7032967 0,341568474 0,1794 0,51918 0,53555 -583,570719 -583,285201 0,751624812 231 77 0,0001 0,04889 0 0,1466 1 1 1 1 0,99976 1
HSBP1 HSBP1 nsp13 HSBP1 list26_COV_list4dataset2nonOrf heat shock factor binding protein 1 NPC-A-13 no 24 231 77 0,01253 0,3452 0,0058 0,4602 1 1 1 1 1 1 HSBP1 ENSMLUG00000007636.2 ENSMLUT00000007621.2 9 76 84,21052632 0,469211898 0,4944 2,74941 2,74944 -833,964215 -833,964329 0,999886006 189 63 0,0001 0,26772 0 0,3778 0,99549 1 1 1 0,99966 1
BCKDK BCKDK nsp12 BCKDK list24_COV_list2nonOrf branched chain ketoacid dehydrogenase kinase BCKDKD|BDK no 23 1239 413 0,06079 0,51593 0,0358 0,5888 1 1 1 1 1 1 BCKDK ENSMLUG00000004771.2 ENSMLUT00000004771.2 11 413 99,27360775 0,064361817 0,4158 0,82725 0,82725 -3137,031641 -3137,031697 0,999944002 BCKDK ENSG00000103507.13 ENST00000567682.1 7 145 100 0,239937421 0,1681 0,36019 0,37332 -819,027497 -818,724696 0,738746092 BCKDK 1 1 855 285 0,06821 0,26047 0,0194 0,2848 1 1 1 1 1 1
ACSL3 ACSL3 nsp7 ACSL3 list24_COV_list2nonOrf acyl-CoA synthetase long chain family member 3 ACS3|FACL3|LACS 3|LACS3|PRO2194 no 22 2163 721 0,0929 0,3698 0,0344 0,3705 1 1 1 1 1 1 ACSL3 ENSMLUG00000010952.2 ENSMLUT00000010950.2 6 721 99,58391123 0,076280564 0,4619 0,68965 0,68965 -4861,986231 -4861,986282 0,999949001 ACSL3 0,885467572 1 1806 602 0,12383 0,25536 0,0304 0,2453 0,33716 1 0,51788 1 0,63134 1
PDZD11 PDZD11 nsp12 PDZD11 list26_COV_list4dataset2nonOrf PDZ domain containing 11 AIPP1|PDZK11|PISP no 23 423 141 0,0001 0,22866 0 0,2764 1 1 1 1 1 1 PDZD11 ENSMLUG00000011031.2 ENSMLUT00000011024.2 5 141 75,17730496 0,24885785 0,2315 0,56966 0,56966 -679,731552 -679,731599 0,999953001 PDZD11 0,981912255 1 375 125 0,0001 0,1789 0 0,2204 1 1 1 1 1 1
GGCX GGCX M GGCX list26_COV_list4dataset2nonOrf gamma-glutamyl carboxylase VKCFD1 no 24 2277 759 0,19063 0,35762 0,0521 0,2736 1 1 1 1 1 1 GGCX ENSMLUG00000005356.2 ENSMLUT00000005357.2 13 760 91,18421053 0,116117678 0,2871 0,9313 0,9313 -6053,873495 -6053,87352 0,999975 GGCX ENSG00000115486.11 ENST00000428479.3 14 74 91,89189189 0,260610293 0,1642 0,31857 0,31857 -406,805413 -406,805414 0,999999 GGCX 0,546284268 1 1962 654 0,16691 0,24426 0,0325 0,1948 1 1 1 1 1 1
RALA RALA nsp7 RALA list26_COV_list4dataset2nonOrf RAS like proto-oncogene A RAL no 24 624 208 0,00906 0,22301 0,0023 0,2537 1 1 1 1 0,99974 1 RALA ENSMLUG00000009687.2 ENSMLUT00000009669.2 4 207 81,64251208 0,399885613 0,4528 1,02844 1,02844 -1276,11971 -1276,11972 0,99999 RALA ENSG00000006451.7 ENST00000434466.1 9 164 79,26829268 0,773783832 0,3227 2,48609 2,60629 -1281,308936 -1280,469168 0,431810692 RALA 1 1 549 183 0,01431 0,1379 0,0025 0,173 1 1 1 1 0,99987 1
LOX LOX orf8 LOX list23_COV_list1orf lysyl oxidase AAT10 no 23 1254 418 0,12674 0,39385 0,0496 0,3916 1 1 1 1 0,82371 1 LOX ENSMLUG00000026720.1 ENSMLUT00000024286.1 6 417 98,56115108 0,128529144 0,3142 0,55566 0,55566 -2643,25822 -2643,258229 0,999991 LOX ENSG00000113083.13 ENST00000639739.1 17 417 89,6882494 0,159084185 0,1725 0,3857 0,3857 -2373,668388 -2373,668728 0,999660058 LOX 0,9426572 1 810 270 0,09993 0,18784 0,02 0,2006 0,95383 1 0,99312 1 0,99835 1
TBKBP1 TBKBP1 nsp13 TBKBP1 list26_COV_list4dataset2nonOrf TBK1 binding protein 1 ProSAPiP2|SINTBAD no 24 1851 617 0,06496 0,63331 0,0555 0,855 1 1 0,99446 1 1 1 TBKBP1 ENSMLUG00000014537.2 ENSMLUT00000014538.2 8 385 93,76623377 0,087125125 0,4561 0,86161 0,86161 -2663,440804 -2663,440799 0,999995 TBKBP1 ENSG00000198933.9 ENST00000622396.1 14 223 98,65470852 0,126745863 0,1225 0,21815 0,21824 -1170,30195 -1170,296198 0,994264511 TBKBP1 0,544718639 1 1167 389 0,06461 0,26627 0,0229 0,3542 0,87415 1 0,98754 1 1 1
CCDC86 CCDC86 nsp8 CCDC86 list26_COV_list4dataset2nonOrf coiled-coil domain containing 86 - no 24 1083 361 0,4232 0,68187 0,1624 0,3837 1 1 0,9155 1 1 1 CCDC86 ENSMLUG00000005025.2 ENSMLUT00000005026.2 10 360 88,05555556 0,380392898 0,4329 2,01267 2,01267 -3831,995145 -3831,995149 0,999996 756 252 0,47195 0,448 0,1119 0,2371 0,049749 0,353647 0,13949 0,82621 0,14587 1 28,144 1,9259 2 103_A_0.949,157_K_0.911
NUP98 NUP98 orf6 NUP98 list23_COV_list1orf nucleoporin 98 ADIR2|NUP196|NUP96 no 36 5409 1803 0,17017 0,44274 0,0618 0,3631 1 1 0,034521 0,179862635135135 1 1 0,05 999 0 noneOver_0.9 NUP98 ENSMLUG00000013592.2 ENSMLUT00000013607.2 10 1801 98,33425875 0,118413081 0,2018 0,53296 0,53296 -12054,95795 -12054,95796 0,999997 NUP98 ENSG00000110713.15 ENST00000359171.8 11 1818 99,17491749 0,154326086 0,1011 0,215 0,21521 -9759,166224 -9758,903985 0,769327133 NUP98 1 1 4641 1547 0,1932 0,30858 0,0477 0,2471 1 1 1 1 1 1
SCCPDH SCCPDH nsp7 SCCPDH list24_COV_list2nonOrf saccharopine dehydrogenase (putative) CGI-49|NET11 no 24 1290 430 0,26038 0,57846 0,1053 0,4043 1 1 1 1 1 1 SCCPDH ENSG00000143653.9 ENST00000366510.3 15 430 97,6744186 0,196343201 0,1627 0,45905 0,45905 -2886,510879 -2886,510885 0,999994 SCCPDH 0,002019556 0,09129021 999 333 0,29684 0,36519 0,074 0,2492 1 1 1 1 1 1
SCARB1 SCARB1 nsp7 SCARB1 list26_COV_list4dataset2nonOrf scavenger receptor class B member 1 CD36L1|CLA-1|CLA1|HDLQTL6|SR-BI|SRB1 no 25 1530 510 0,10383 0,62207 0,0784 0,7554 1 1 1 1 1 1 SCARB1 ENSG00000073060.15 ENST00000545493.1 14 146 100 0,221176368 0,1874 0,49354 0,49355 -910,195166 -910,195401 0,999765028 SCARB1 0,06378625 0,750735805 1059 353 0,12908 0,34857 0,0509 0,3941 1 1 1 1 1 1
CRTC3 CRTC3 nsp12 CRTC3 list24_COV_list2nonOrf CREB regulated transcription coactivator 3 TORC-3|TORC3 no 24 1860 620 0,15898 0,49654 0,0622 0,3913 1 1 1 1 1 1 CRTC3 ENSG00000140577.15 ENST00000558005.1 15 189 100 0,175393258 0,1453 0,37919 0,42036 -1190,280278 -1172,866651 0,0000000274 -1189,827595 0,00000000574 CRTC3 0,076771389 0,831146916 1419 473 0,16941 0,2975 0,0408 0,241 1 1 1 1 1 1
ETFA ETFA M ETFA list26_COV_list4dataset2nonOrf electron transfer flavoprotein subunit alpha EMA|GA2|MADD no 24 1005 335 0,13168 0,35074 0,0413 0,3138 1 1 1 1 1 1 ETFA 0,479248898 1 837 279 0,16675 0,19725 0,0282 0,1694 1 1 1 1 1 1
CEP43 FGFR1OP nsp13 FGFR1OP list26_COV_list4dataset2nonOrf FGFR1 oncogene partner FOP no 24 1200 400 0,17666 0,45373 0,0665 0,3766 1 1 1 1 1 1 FGFR1OP 0,481654006 1 981 327 0,18516 0,33883 0,054 0,2918 1 1 1 1 1 1
SLC30A7 SLC30A7 M SLC30A7 list26_COV_list4dataset2nonOrf solute carrier family 30 member 7 ZNT7|ZnT-7|ZnTL2 no 24 1131 377 0,07294 0,25557 0,0186 0,2553 1 1 1 1 1 1 SLC30A7 ENSG00000162695.11 ENST00000370111.4 18 87 90,8045977 0,051964134 0,1617 0,20741 0,20741 -410,970434 -410,970485 0,999949001 SLC30A7 0,486651162 1 975 325 0,09588 0,1955 0,0184 0,1921 1 1 1 1 1 1
HEATR3 HEATR3 orf7a HEATR3 list25_COV_list3dataset2orf HEAT repeat containing 3 SYO1 no 24 2055 685 0,19063 0,47473 0,0711 0,3728 1 1 1 1 0,99916 1 HEATR3 ENSG00000155393.12 ENST00000299192.7 14 681 98,97209985 0,129618084 0,1751 0,33356 0,33357 -4068,040378 -4068,040482 0,999896005 HEATR3 0,495183326 1 1665 555 0,23887 0,3332 0,0585 0,2448 1 1 1 1 0,86287 1
COL6A1 COL6A1 orf8 COL6A1 list25_COV_list3dataset2orf collagen type VI alpha 1 chain BTHLM1|OPLL|UCHMD1 no 23 3087 1029 0,04809 0,87953 0,0553 1,1505 1 1 1 1 1 1 COL6A1 ENSG00000142156.14 ENST00000612273.1 13 1027 92,69717624 0,146502902 0,3357 0,82367 0,82367 -7083,510661 -7083,510716 0,999945002 COL6A1 0,53045283 1 1683 561 0,05345 0,28467 0,0221 0,4128 1 1 1 1 1 1
RDX RDX nsp13 RDX list26_COV_list4dataset2nonOrf radixin DFNB24 no 24 1818 606 0,04733 0,23036 0,0125 0,2643 1 1 1 1 0,99971 1 RDX 0,664925023 1 1602 534 0,0578 0,1471 0,0088 0,1527 1 1 1 1 0,9999 1
G3BP1 G3BP1 N G3BP1 list24_COV_list2nonOrf G3BP stress granule assembly factor 1 G3BP|HDH-VIII no 22 1401 467 0,07189 0,23663 0,0159 0,2217 1 1 1 1 0,99967 1 G3BP1 0,815160905 1 1224 408 0,1046 0,16582 0,0149 0,1429 0,80507 1 1 1 0,99987 1
TCF12 TCF12 nsp12 TCF12 list26_COV_list4dataset2nonOrf transcription factor 12 CRS3|HEB|HTF4|HsT17266|TCF-12|bHLHb20|p64 no 23 2121 707 0,09329 0,23129 0,0205 0,2194 1 1 1 1 1 1 TCF12 ENSG00000140262.17 ENST00000560948.1 19 44 93,18181818 0,195051608 0,0652 50,1049 50,10237 -293,114675 -291,985703 0,323365505 TCF12 0,867226426 1 1911 637 0,1229 0,17108 0,0194 0,1579 1 1 0,99581 1 1 1
ZC3H18 ZC3H18 E ZC3H18 list26_COV_list4dataset2nonOrf zinc finger CCCH-type containing 18 NHN1 no 24 2967 989 0,1117 0,58973 0,0604 0,5411 1 1 1 1 1 1 ZC3H18 ENSG00000158545.15 ENST00000565583.1 18 197 95,93908629 0,085936944 0,247 0,56059 0,56201 -1285,422209 -1285,384522 0,963014317 ZC3H18 0,871831956 1 1926 642 0,14281 0,26642 0,0343 0,2403 0,20603 0,90234375 0,17233 0,934465492957747 0,4399 1
G3BP2 G3BP2 N G3BP2 list24_COV_list2nonOrf G3BP stress granule assembly factor 2 - no 24 1449 483 0,02978 0,21311 0,0077 0,2588 1 1 1 1 0,99966 1 G3BP2 0,910244802 1 1281 427 0,03932 0,16116 0,0077 0,1953 1 1 1 1 0,99981 1
AP2A2 AP2A2 nsp10 AP2A2 list24_COV_list2nonOrf adaptor related protein complex 2 subunit alpha 2 ADTAB|CLAPA2|HIP-9|HIP9|HYPJ no 23 2826 942 0,0316 0,62439 0,0251 0,7931 1 1 1 1 1 1 AP2A2 0,95561292 1 1776 592 0,03047 0,26984 0,0109 0,357 1 1 1 1 0,99999 1
PLOD2 PLOD2 orf8 PLOD2 list23_COV_list1orf procollagen-lysine,2-oxoglutarate 5-dioxygenase 2 BRKS2|LH2|TLH no 23 2277 759 0,10317 0,29092 0,0301 0,2917 1 1 1 1 0,99093 1 PLOD2 ENSG00000152952.11 ENST00000480704.1 16 99 96,96969697 0,363039363 0,213 0,6353 0,6353 -733,040385 -733,040386 0,999999 PLOD2 0,97110571 1 2043 681 0,12848 0,21004 0,0248 0,1932 0,23467 0,983861290322581 0,459 1 0,52239 1
MARK2 MARK2 orf9b MARK2 list25_COV_list3dataset2orf microtubule affinity regulating kinase 2 EMK-1|EMK1|PAR-1|Par-1b|Par1b no 24 2367 789 0,01554 0,20698 0,004 0,2583 1 1 1 1 0,99909 1 MARK2 0,973207517 1 1857 619 0,01526 0,15055 0,0029 0,1916 1 1 1 1 0,99956 1
TIMM9 TIMM9 nsp4 TIMM9 list24_COV_list2nonOrf translocase of inner mitochondrial membrane 9 TIM9|TIM9A no 22 270 90 0,0001 0,08744 0 0,149 1 1 1 1 0,99973 1 TIMM9 ENSG00000100575.13 ENST00000555097.1 17 44 100 0,1194 0,07839 0,07839 -186,634969 -186,635077 0,999892006 TIMM9 0,979349576 1 258 86 0,0001 0,06635 0 0,114 1 1 1 1 0,9998 1
TIMM10B TIMM10B nsp4 TIMM10B list24_COV_list2nonOrf translocase of inner mitochondrial membrane 10B FXC1|TIM10B|Tim9b no 24 318 106 0,18622 0,25396 0,04 0,2149 1 1 0,98893 1 1 1 TIMM10B 0,993324497 1 243 81 0,13293 0,21534 0,0283 0,2132 1 1 1 1 1 1
RHOA RHOA nsp7 RHOA list26_COV_list4dataset2nonOrf ras homolog family member A ARH12|ARHA|RHO12|RHOH12 no 23 582 194 0,04509 0,25785 0,0122 0,2715 1 1 1 1 1 1 RHOA ENSG00000067560.10 ENST00000445425.4 18 129 88,37209302 0,071970158 2,0946 107,0399 106,9229 -1606,628616 -1601,617051 0,006660472 -1602,054288 0,349719687 RHOA 0,997743245 1 504 168 0,0242 0,20048 0,0056 0,2296 1 1 1 1 1 1
REEP5 REEP5 M REEP5 list24_COV_list2nonOrf receptor accessory protein 5 C5orf18|D5S346|DP1|TB2|YOP1|Yip2e no 24 570 190 0,05899 0,29277 0,0195 0,3303 1 1 1 1 1 1 REEP5 ENSG00000129625.12 ENST00000504247.1 15 100 96 0,148600334 0,1496 0,35671 0,36439 -565,263494 -565,198813 0,937366436 REEP5 1 1 441 147 0,06432 0,17395 0,0127 0,1969 0,93106 1 0,86352 1 0,99626 1
REEP6 REEP6 M REEP6 list26_COV_list4dataset2nonOrf receptor accessory protein 6 C19orf32|DP1L1|REEP6.1|REEP6.2|RP77|TB2L1|Yip2f no 24 555 185 0,19045 0,58601 0,1047 0,5499 1 1 1 1 0,99316 1 REEP6 ENSG00000115255.10 ENST00000395484.4 13 140 96,42857143 0,350200437 0,1831 0,57981 0,58542 -928,791184 -928,544205 0,781157098 REEP6 1 1 324 108 0,27398 0,30085 0,0593 0,2165 0,23693 0,983861290322581 0,47531 1 0,49332 1
FAM8A1 FAM8A1 M FAM8A1 list26_COV_list4dataset2nonOrf family with sequence similarity 8 member A1 AHCP no 24 1248 416 0,34231 0,64012 0,1349 0,394 1 1 1 1 1 1 FAM8A1 ENSG00000137414.5 ENST00000259963.3 5 414 94,92753623 0,25653807 0,1723 0,35366 0,35383 -2310,944362 -2310,929873 0,98561546 FAM8A1 1 1 768 256 0,30502 0,30989 0,0611 0,2005 0,95466 1 0,97397 1 0,99838 1
PPIL3 PPIL3 nsp12 PPIL3 list24_COV_list2nonOrf peptidylprolyl isomerase like 3 CYPJ no 22 486 162 0,08706 0,30564 0,0282 0,3245 1 1 1 1 1 1 PPIL3 1 1 444 148 0,10982 0,23897 0,0273 0,2485 1 1 1 1 1 1
TLE3 TLE3 nsp13 TLE3 list26_COV_list4dataset2nonOrf transducin like enhancer of split 3 ESG|ESG3|GRG3|HsT18976 no 23 2319 773 0,00491 0,33086 0,0023 0,4634 1 1 1 1 0,99863 1 TLE3 ENSG00000140332.15 ENST00000627388.2 3 773 99,61190168 0,011459711 0,0814 0,0779 0,0779 -3328,83834 -3328,838424 0,999916004 TLE3 1 1 1653 551 0,0001 0,12647 0 0,1908 0,99194 1 1 1 0,99811 1
TLE5 AES nsp13 AES list26_COV_list4dataset2nonOrf amino-terminal enhancer of split AES-1|AES-2|ESP1|GRG|GRG5|Grg-5|TLE5 no 23 795 265 0,03715 0,65866 0,034 0,9163 1 1 1 1 1 1 AES 1 1 528 176 0,03919 0,35186 0,0199 0,5084 0,33912 1 0,61225 1 1 1
RAP1GDS1 RAP1GDS1 nsp2 RAP1GDS1 list24_COV_list2nonOrf Rap1 GTPase-GDP dissociation stimulator 1 GDS1|SmgGDS no 24 1827 609 0,08686 0,17102 0,0147 0,1693 1 1 1 1 0,99803 1 RAP1GDS1 ENSG00000138698.14 ENST00000514139.2 5 101 94,05940594 0,582003659 0,1136 0,16559 0,16557 -469,004579 -469,004584 0,999995 RAP1GDS1 1 1 1707 569 0,07632 0,12971 0,01 0,1312 0,52455 1 0,62067 1 0,8167 1
HDAC2 HDAC2 nsp5 HDAC2 list26_COV_list4dataset2nonOrf histone deacetylase 2 HD2|RPD3|YAF1 no 24 1470 490 0,01067 0,25079 0,0039 0,3623 1 1 0,34431 0,804883333333333 1 1 HDAC2 ENSG00000196591.11 ENST00000524334.1 6 52 100 0,043758984 0,1375 5,36638 5,36721 -304,260393 -304,260649 0,999744033 HDAC2 1 1 1281 427 0,01705 0,17663 0,0044 0,259 0,9174 1 0,37419 1 0,99455 1
GTF2F2 GTF2F2 nsp9 GTF2F2 list24_COV_list2nonOrf general transcription factor IIF subunit 2 BTF4|RAP30|TF2F2|TFIIF no 24 750 250 0,02445 0,33417 0,0094 0,3845 1 1 1 1 1 1 GTF2F2 1 1 636 212 0,03639 0,2466 0,0108 0,2963 1 1 1 1 1 1
NUP54 NUP54 nsp9 NUP54 list24_COV_list2nonOrf nucleoporin 54 - no 24 1530 510 0,03565 0,21623 0,009 0,2532 1 1 0,97422 1 1 1 NUP54 ENSG00000138750.14 ENST00000513352.1 19 85 96,47058824 0,108626324 0,0807 0,15464 0,18092 -394,365771 -392,927146 0,237253758 NUP54 1 1 1380 460 0,04403 0,15203 0,0079 0,1796 1 1 1 1 0,9998 1
MAT2B MAT2B nsp9 MAT2B list24_COV_list2nonOrf methionine adenosyltransferase 2B MAT-II|MATIIbeta|Nbla02999|SDR23E1|TGR no 24 1005 335 0,07161 0,22972 0,0158 0,2209 1 1 1 1 0,99968 1 MAT2B 1 1 915 305 0,07952 0,16724 0,0127 0,1593 1 1 1 1 0,99984 1
ELOB TCEB2 orf10 TCEB2 list25_COV_list3dataset2orf elongin B SIII|TCEB2 no 22 354 118 0,18231 0,74417 0,128 0,7021 1 1 1 1 0,99981 1 ELOB ENSG00000103363.14 ENST00000572954.1 16 67 94,02985075 0,564142855 0,2216 1,44523 1,9231 -606,091885 -596,030799 0,0000427 -602,771538 0,00024093 1 M1 TCEB2 1 1 207 69 0,18938 0,28695 0,0511 0,2698 1 1 1 1 0,99996 1
MARK3 MARK3 orf9b MARK3 list25_COV_list3dataset2orf microtubule affinity regulating kinase 3 CTAK1|KP78|PAR1A|Par-1a no 23 2262 754 0,07222 0,26428 0,0184 0,2547 1 1 1 1 1 1 MARK3 ENSG00000075413.17 ENST00000556744.1 16 332 98,19277108 0,082112262 0,1058 0,29674 0,29722 -1892,469655 -1890,656982 0,163217273 MARK3 1 1 1830 610 0,13101 0,1388 0,0161 0,123 0,81544 1 1 1 1 1
PTBP2 PTBP2 orf9b PTBP2 list25_COV_list3dataset2orf polypyrimidine tract binding protein 2 PTBLP|brPTB|nPTB no 24 1614 538 0,0001 0,19238 0 0,2472 1 1 1 1 0,99787 1 PTBP2 1 1 1443 481 0,0001 0,12864 0 0,1638 0,99403 1 1 1 0,99863 1
RPL36 RPL36 N RPL36 list26_COV_list4dataset2nonOrf ribosomal protein L36 L36 no 24 318 106 0,01125 0,55677 0,0114 1,0159 1 1 1 1 0,99933 1 216 72 0,0001 0,38112 0,0001 0,5947 1 1 1 1 1 1
TBCA TBCA nsp11 TBCA list26_COV_list4dataset2nonOrf tubulin folding cofactor A - no 24 327 109 0,25534 0,36767 0,0685 0,2681 1 1 1 1 1 1 264 88 0,22215 0,28046 0,048 0,2161 1 1 1 1 1 1
NUTF2 NUTF2 nsp15 NUTF2 list26_COV_list4dataset2nonOrf nuclear transport factor 2 NTF-2|NTF2|PP15 no 23 384 128 0,0166 0,17861 0,0036 0,2174 1 1 1 1 0,99982 1 324 108 0,0001 0,05343 0 0,0754 1 1 1 1 0,99983 1
EIF4E2 EIF4E2 nsp2 EIF4E2 list26_COV_list4dataset2nonOrf eukaryotic translation initiation factor 4E family member 2 4E-LP|4EHP|EIF4EL3|IF4e|h4EHP no 23 738 246 0,03395 0,21063 0,0077 0,2281 1 1 1 1 0,8217 1 630 210 0,02442 0,155 0,0044 0,1816 1 1 1 1 0,99984 1
GPX1 GPX1 nsp5_C145A GPX1 list24_COV_list2nonOrf glutathione peroxidase 1 GPXD|GSHPX1 no 27 615 205 0,10057 0,68151 0,0776 0,7714 1 1 0,92503 1 1 1 348 116 0,09159 0,47798 0,0475 0,5191 1 1 1 1 1 1
RAB1A RAB1A nsp7 RAB1A list24_COV_list2nonOrf RAB1A, member RAS oncogene family RAB1|YPT1 no 24 621 207 0,12597 0,16359 0,0185 0,1468 1 1 1 1 1 1 RAB1A ENSG00000138069.16 ENST00000409751.1 9 174 82,75862069 0,499843608 0,1626 6,55767 9,93272 -1539,230194 -1538,975128 0,774865355 558 186 0,32499 0,07922 0,0173 0,0532 0,32736 1 0,60497 1 0,619 1
DNAJC19 DNAJC19 nsp7 DNAJC19 list24_COV_list2nonOrf DnaJ heat shock protein family (Hsp40) member C19 PAM18|TIM14|TIMM14 no 24 351 117 0,06151 0,19118 0,0147 0,2396 1 1 1 1 0,99993 1 330 110 0,07254 0,17571 0,0156 0,2151 1 1 1 1 0,99995 1
RBX1 RBX1 orf10 RBX1 list25_COV_list3dataset2orf ring-box 1 BA554C12.1|RNF75|ROC1 no 24 327 109 0,0001 0,16056 0 0,164 1 1 1 1 0,99971 1 261 87 0,0001 0,07561 0 0,0733 1 1 1 1 0,99989 1
CISD3 CISD3 orf8 CISD3 list25_COV_list3dataset2orf CDGSH iron sulfur domain 3 MiNT|Miner2 no 23 384 128 0,16332 0,56734 0,0939 0,5748 1 1 0,87087 1 0,94745 1 279 93 0,04635 0,22112 0,0141 0,3044 1 1 1 1 1 1
NPTX1 NPTX1 orf8 NPTX1 list23_COV_list1orf neuronal pentraxin 1 NP1 no 23 1299 433 0,01642 0,33748 0,0122 0,7454 1 1 1 1 0,99999 1 NPTX1 ENSG00000171246.5 ENST00000306773.4 14 433 75,75057737 0,036985109 0,1275 0,23805 0,23805 -1667,229725 -1667,230101 0,999624071 813 271 0,0123 0,14899 0,0045 0,3654 1 1 1 1 0,99926 1
BAG5 BAG5 orf9b BAG5 list25_COV_list3dataset2orf BCL2 associated athanogene 5 BAG-5 no 24 1467 489 0,06218 0,46292 0,0308 0,4953 1 1 0,97165 1 1 1 BAG5 ENSG00000166170.9 ENST00000299204.4 19 448 99,33035714 0,045009369 0,2604 0,40094 0,40095 -2802,170187 -2802,171045 0,999142368 1143 381 0,08204 0,25975 0,0203 0,247 1 1 0,77838 1 1 1
ACAD9 ACAD9 orf9c ACAD9 list24_COV_list2nonOrf acyl-CoA dehydrogenase family member 9 NPD002 no 24 1875 625 0,27034 0,70758 0,1376 0,509 1 1 1 1 1 1 ACAD9 ENSG00000177646.18 ENST00000505192.5 18 70 95,71428571 0,788848042 0,1923 0,87275 0,87275 -600,730687 -600,730723 0,999964001 1362 454 0,45958 0,45559 0,1151 0,2505 0,32167 1 0,52503 1 0,61198 1