README.md

+# 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 [1], 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 [2], 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/
+```
+Input tables in **data/**.
+
+Output tables in **out_tab**
+
+The tables output from this script will be used for the following analysis steps.
+
+
+## Primates and bats
+
+## Dataset comparison
+
+

data/DGINN_202011262248_hyphybpp-202012192053_codeml-summary.txt

+File	Name	Gene	GeneSize	NbSpecies	omegaM0Bpp	omegaM0codeml	BUSTED	BUSTED_p-value	MEME_NbSites	MEME_PSS	BppM1M2	BppM1M2_p-value	BppM1M2_NbSites	BppM1M2_PSS	BppM7M8	BppM7M8_p-value	BppM7M8_NbSites	BppM7M8_PSS	codemlM1M2	codemlM1M2_p-value	codemlM1M2_NbSites	codemlM1M2_PSS	codemlM7M8	codemlM7M8_p-value	codemlM7M8_NbSites	codemlM7M8_PSS
+ATE1_bat_select_cut_mafft_prank	ATE1	ATE1	571	11	0.15571103991607290751	0.147	N	0.0691	22	11, 17, 18, 27, 28, 29, 30, 108, 176, 223, 242, 243, 248, 251, 330, 346, 349, 350, 357, 361, 364, 571	N	0.9999998628509369	0	na	N	0.9999862060854904	0	na	N	1.0	0	na	N	1.0	0	na
+CEP43_bat_realign_mafft_prank	CEP43	FGFR1OP	744	9	0.22520577281291204175	na	Y	0.0000	13	8, 33, 158, 183, 184, 216, 226, 392, 527, 547, 606, 705, 722	N	0.9999995178687894	0	na	N	0.9999984890175757	0	na	na	na	0	na	na	na	0	na
+COL6A1_bat_cut_hipArm_mafft_prank	COL6A1	COL6A1	1078	9	0.082800084329500089897	0.077	Y	0.0023	46	15, 26, 99, 115, 180, 184, 202, 221, 226, 227, 283, 306, 337, 375, 387, 390, 399, 472, 603, 632, 678, 685, 725, 726, 738, 740, 761, 768, 770, 808, 834, 856, 895, 902, 911, 916, 919, 920, 956, 958, 967, 985, 996, 1009, 1060, 1078	N	0.9999996594925176	0	na	Y	1.4694972038785021e-06	8	64, 108, 227, 364, 387, 472, 727, 986	N	0.13931746906383644	0	na	Y	0.017650344100739557	1	472
+COQ8B_bat_select-2_mafft_prank	COQ8B	COQ8B	549	11	0.18249719548855317108	0.152	N	0.8349	3	20, 56, 542	N	0.9999990844975953	0	na	N	0.2543943107209184	0	na	N	1.0	0	na	Y	0.041794104084912305	0	
+CYB5B_bat_select-2_mafft_prank	CYB5B	CYB5B	278	12	0.26049862063255097011	0.265	Y	0.0000	4	34, 116, 117, 179	N	0.9999997690552203	0	na	N	0.9987889831935154	0	na	N	0.9831436846348578	0	na	N	0.6187833918061296	0	na
+DDX21_bat_select_mafft_prank	DDX21	DDX21	875	12	0.20291300096326300717	0.191	Y	0.0096	5	119, 163, 220, 852, 853	N	0.9999982397800553	0	na	N	0.4659703818153812	0	na	N	1.0	0	na	N	0.5106861833664071	0	na
+ELOB_bat_select_mafft_prank	ELOB	ELOB	124	7	0.022399397377356227573	0.023	Y	0.0017	0	na	N	0.999999863385606	0	na	N	0.8892346112479187	0	na	N	1.0	0	na	N	0.9990004998333987	0	na
+ERO1B_bat_select_mafft_prank	ERO1B	ERO1B	777	11	0.036193322757663994038	0.039	N	1.0000	1	386	N	0.9999999786846276	0	na	N	0.18314924606569427	0	na	N	1.0	0	na	N	1.0	0	na
+ETFA_bat_select_mafft_prank	ETFA	ETFA	463	9	0.14661583111874651464	0.130	N	0.0544	4	95, 149, 252, 281	N	0.9999990190681537	0	na	N	0.08502800413099425	0	na	N	1.0	0	na	N	0.997004495503217	0	na
+GNB1_bat_select_add-2_mafft_prank	GNB1	GNB1	340	12	0.0018342406212685069111	0.002	N	0.9997	0	na	N	0.9999999737615329	0	na	N	0.9999998460152829	0	na	N	0.997004495503217	0	na	N	0.997004495503217	0	na
+GOLGA7_bat_select_mafft_prank	GOLGA7	GOLGA7	149	12	0.22057358315093311685	0.207	Y	0.0000	0	na	Y	2.7070592544122577e-07	1	123	Y	1.7510477519039514e-09	1	123	Y	9.285332670143659e-11	1	123	Y	2.0005777681870837e-11	1	123
+HSBP1_bat-select_mafft_prank	HSBP1	HSBP1	110	12	0.15412257221850680922	0.152	N	1.0000	0	na	N	0.9999999941107944	0	na	N	0.9999805391217563	0	na	N	0.9990004998333987	0	na	N	1.0	0	na
+INTS4_bat_select_mafft_prank	INTS4	INTS4	1016	10	0.076028837169011515007	0.079	N	0.9908	3	218, 219, 731	N	0.9999989182894236	0	na	Y	0.004664170724173206	4	25, 521, 988, 989	N	1.0	0	na	N	0.5804219151408183	0	na
+MOV10_bat_cut_mafft_prank	MOV10	MOV10	1173	12	0.19324965973420044074	0.196	Y	0.0052	14	7, 34, 41, 263, 391, 455, 485, 527, 575, 657, 661, 1066, 1067, 1068	N	0.9999989373906107	0	na	Y	0.0032754616051401913	1	391	N	1.0	0	na	Y	0.012727376189877581	0	
+MTARC1_bat_select_mafft_prank	MTARC1	MARC1	386	9	0.17111475068673179245	0.188	N	0.0986	32	7, 30, 31, 38, 39, 41, 46, 48, 62, 64, 68, 74, 82, 85, 91, 94, 113, 120, 162, 179, 183, 211, 218, 220, 245, 270, 273, 300, 306, 331, 336, 371	N	0.9999997707509728	0	na	N	0.9999808885270796	0	na	N	1.0	0	na	N	1.0	0	na
+PABPC1_bat_select_mafft_prank	PABPC1	PABPC1	735	12	0.093895137051223293012	0.093	N	0.0509	2	471, 671	N	0.9720024792000442	0	na	N	0.609585807664959	0	na	N	0.7124824490892332	0	na	N	0.32497728220613026	0	na
+PCSK6_bat_select_mafft_prank	PCSK6	PCSK6	1654	8	0.098698549445369543331	0.089	Y	0.0000	16	28, 499, 522, 690, 749, 889, 948, 953, 966, 1010, 1303, 1310, 1324, 1325, 1350, 1398	N	0.9999997156783542	0	na	N	0.10593739617924755	0	na	N	1.0	0	na	Y	3.761624567607716e-10	0	
+PDE4DIP_bat_select_check_mafft_prank	PDE4DIP	PDE4DIP	2894	9	0.24383690569934904357	0.244	N	0.0597	49	179, 292, 315, 375, 443, 541, 546, 732, 888, 931, 991, 1201, 1208, 1397, 1437, 1559, 1564, 1583, 1640, 1697, 1783, 1891, 1927, 2005, 2008, 2016, 2018, 2021, 2023, 2027, 2028, 2041, 2135, 2187, 2193, 2194, 2276, 2325, 2364, 2388, 2392, 2450, 2516, 2517, 2526, 2531, 2580, 2855, 2869	N	0.9999979304287578	0	na	N	0.7675900915830035	0	na	N	1.0	0	na	N	0.20028778934513805	0	na
+RDX_bat_select_mafft_prank	RDX	RDX	677	11	0.070830287871767122487	0.065	Y	0.0012	3	2, 348, 608	N	0.9999984528438435	0	na	Y	0.00870082277450124	2	348, 601	N	1.0	0	na	N	0.9464851479532084	0	na
+REEP6_LA_bat_select_mafft_prank	REEP6	REEP6-A	382	10	0.29517622624592770864	0.237	N	0.6966	1	378	N	0.6758702216545132	0	na	N	0.1986200915147093	0	na	N	0.6616622828278411	0	na	N	0.31505753690349364	0	na
+REEP6_LB_bat_select_mafft_prank	REEP6	REEP6-B	810	6	0.93827969065999239362	0.952	Y	0.0000	11	377, 380, 492, 522, 526, 576, 589, 590, 599, 618, 793	N	0.11712587948916774	0	na	Y	0.04708383426090607	0		Y	0.01900601064703638	0		Y	0.014610716685120028	0	
+RIPK1_sequences_filtered_longestORFs_quickclean_mafft_prank	RIPK1	RIPK1	804	24	0.43700247549171972183	0.412	N	0.0863	20	55, 95, 103, 277, 348, 377, 404, 565, 574, 581, 584, 586, 613, 617, 667, 690, 695, 707, 725, 797	Y	0.046009519791645484	0		Y	0.001203426338327248	1	797	Y	0.024551066100545648	1	797	Y	0.0002102948425097665	1	797
+SDF2_bat_select_mafft_prank	SDF2	SDF2	246	12	0.082797829312180992734	0.080	N	0.2145	0	na	N	0.999999439606811	0	na	N	0.9999999930209924	0	na	N	1.0	0	na	N	1.0	0	na
+SELENOS_bat_select_mafft_prank	SELENOS	SELENOS	218	8	0.16939756613179995925	0.172	N	0.8176	5	71, 72, 85, 128, 173	N	0.9999995791182277	0	na	N	0.389714156459387	0	na	N	0.9970044955034437	0	na	N	0.2133118712228997	0	na
+TARS2_bat_same_mafft_prank	TARS2	TARS2	1893	11	0.13453722174632359865	0.164	N	1.0000	6	121, 571, 1151, 1732, 1832, 1833	N	0.9999988295192026	0	na	N	0.9999999986184775	0	na	N	1.0	0	na	N	0.4479829971847956	0	na
+TBCA_bat_same_mafft_prank	TBCA	TBCA	190	12	0.13752129483741223903	0.242	Y	0.0000	1	112	N	0.9999997004843402	0	na	N	0.9935970583143424	0	na	Y	5.8900579536612135e-09	3	2, 3, 4	na	na	0	na
+TMEM39B_bat_same_mafft_prank	TMEM39B	TMEM39B	880	12	0.027235007039035353388	0.041	Y	0.0000	3	217, 331, 332	N	0.9999999952856342	0	na	N	0.9997082226309755	0	na	Y	1.5181774326767543e-06	2	169, 216	na	na	0	na
+TMPRSS2_bat_same_mafft_prank	TMPRSS2	TMPRSS2	1174	12	0.14029058400872645995	0.145	N	0.9333	12	630, 644, 649, 688, 775, 888, 921, 1003, 1051, 1055, 1066, 1173	N	0.9999990104220509	0	na	N	0.6218822946709852	0	na	N	1.0	0	na	N	0.788991288016829	0	na
+TMPRSS2_bat_select_cut_mafft_prank	TMPRSS2	TMPRSS2	574	12	0.12948903836486919117	0.127	N	0.9358	19	59, 73, 78, 108, 115, 117, 121, 133, 144, 241, 259, 288, 321, 403, 421, 451, 455, 466, 573	N	0.9999999060492017	0	na	N	0.3348934269948105	0	na	N	1.0	0	na	N	0.4210515526274131	0	na
+TYSND1_bat_select_mafft_prank	TYSND1	TYSND1	676	8	0.21586721789783006042	0.184	N	0.2118	5	54, 93, 95, 124, 205	N	0.9999989615094754	0	na	Y	0.0016413879993395736	1	95	N	1.0	0	na	Y	1.0838402897292556e-06	2	95, 317
\ No newline at end of file

data/resSELENOS.tab

+File	Name	Gene	GeneSize	NbSpecies	omegaM0Bpp	omegaM0codeml	BUSTED	BUSTED_p-value	MEME_NbSites	MEME_PSS	BppM1M2	BppM1M2_PSS	BppM1M2_NbSites	BppM1M2_p-value	BppM7M8_PSS	BppM7M8_p-value	BppM7M8_NbSites	BppM7M8	BppDFP07_0DFP07	BppDFP07_0DFP07_PSS	BppDFP07_0DFP07_NbSites	BppDFP07_0DFP07_p-value	codemlM1M2	codemlM1M2_p-value	codemlM1M2_NbSites	codemlM1M2_PSS	codemlM7M8	codemlM7M8_NbSites	codemlM7M8_PSS	codemlM7M8_p-value
+SELENOS_sequences_filtered_longestORFs_mafft_mincov_prank	SELENOS_all	SELENOS	367	24	0.20728717475362265499	0.196	Y	0.0000	1	90	N	na	0	0.999999228305	na	0.986116909613	0	N	na	na	0	na	N	1.0	0	na	N	0	na	0.692117181689

data/table_gene_name_correspondence.csv

+Us;Else
COQ8B;ADCK4
ELOC;TCEB1
ERO1B;ERO1LB
MARC1;MTARC1
NSD2;WHSC1
NUP58;NUPL1
PCSK5;
RETREG3;FAM134C
SPART;SPG20
TIMM29;C19orf52
TOMM70;TOMM70A
WASHC4;KIAA1033
\ No newline at end of file

rnw_scripts/covid_comp_script0_table.Rnw

+\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, Data formating}
+\author{Marie Cariou}
+\date{Janvier 2021}							% Activate to display a given date or no date
+
+\begin{document}
+\maketitle
+
+\tableofcontents
+
+\newpage
+
+\section{1st table}
+
+Table containing the DGINN results for both Primates and bats. Conserve all genes.
+
+\subsection{Primates}
+Workdir must be adapted to local environment
+<<>>=
+workdir<-"/home/adminmarie/Documents/CIRI_BIBS_projects/2020_05_Etienne_covid/2020_dginn_covid19/"
+#workdir<-getwd()
+@
+
+<<>>=
+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")
+@
+
+Add SELENOS
+
+<<selenos>>=
+selenos<-read.delim(paste0(workdir,
+      "data/resSELENOS.tab"))
+
+# liste of colonne
+
+colonnes<-c("File", "Name", "Gene", "GeneSize",
+  "NbSpecies", "omegaM0Bpp", "omegaM0codeml", "BUSTED",
+  "BUSTED_p.value",  "MEME_NbSites", "MEME_PSS", "BppM1M2",
+  "BppM1M2_p.value", "BppM1M2_NbSites", "BppM1M2_PSS", "BppM7M8", 
+  "BppM7M8_p.value", "BppM7M8_NbSites", "BppM7M8_PSS","codemlM1M2",
+  "codemlM1M2_p.value", "codemlM1M2_NbSites",  "codemlM1M2_PSS", 
+  "codemlM7M8", "codemlM7M8_p.value", "codemlM7M8_NbSites", 
+  "codemlM7M8_PSS")         
+
+selenos<-selenos[,colonnes]
+
+@
+
+<<>>=
+names(selenos)<-names(dginnT)
+selenos[,6]<-as.factor(selenos[,6])
+selenos[,9]<-as.factor(selenos[,9])
+selenos[,11]<-as.factor(selenos[,11])
+
+selenos[,13]<-as.factor(selenos[,13])
+selenos[,17]<-as.factor(selenos[,17])
+selenos[,21]<-as.factor(selenos[,21])
+selenos[,25]<-as.factor(selenos[,25])
+
+## convertir les pvalues
+dginnT<-rbind(dginnT, selenos)
+@
+    
+\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$status)
+
+write.table(tab, paste0(
+  workdir, "out_tab/covid_comp_alldginn.txt"), sep="\t")
+@
+
+
+\section{Complete data}
+
+Merge the previous tab with J Young's original table. 
+
+\subsection{Read the original Young table}
+<<>>=
+young<-read.delim(paste0(workdir, 
+  "data/COVID_PAMLresults_332hits_plusBatScreens_2020_Apr14.csv"),
+	fill=T, h=T, dec=",")
+dim(young)
+
+young$PreyGene<-as.character(young$PreyGene)
+young$PreyGene[young$PreyGene=="MTARC1"]<-"MARC1"
+
+@
+
+\subsection{Read the gene names conversion table}
+
+<<>>=
+usthem<-read.delim(paste0(workdir, 
+  "/data/table_gene_name_correspondence.csv"),	
+  h=T, sep=";")
+
+young[young$PreyGene %in% usthem$Us, c("PreyGene", "Gene.name")]
+usthem[order(usthem$Us),]
+@
+
+\subsection{Merge Young and DGINN table}
+
+\textbf{Based on which column?}
+
+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
+
+<<>>=
+# creation of a dedicated column
+young$merge.Gene<-young$PreyGene
+tab$merge.Gene<-tab$Gene.name
+tablo<-merge(young, tab, by="merge.Gene", all.x=TRUE)
+
+write.table(tablo, paste0(
+  workdir, "/out_tab/covid_comp_complete.txt"), row.names=FALSE, quote=TRUE, sep="\t")
+@
+
+\end{document}
+