From 0117f4ca6c0d4e1b3001cecc0ac11fb88dd90ec5 Mon Sep 17 00:00:00 2001
From: Fontrodona Nicolas <nicolas.fontrodona@ens-lyon.fr>
Date: Mon, 15 Mar 2021 18:16:31 +0100
Subject: [PATCH] src/bed_handler/select_regulated_near_ctcf_exons.py: new
function get_bed_ctcf_exon to only recover exons close to CTCF peaks in a
given bed file containing exons
---
.../select_regulated_near_ctcf_exons.py | 86 ++++++++++++++++++-
1 file changed, 82 insertions(+), 4 deletions(-)
diff --git a/src/bed_handler/select_regulated_near_ctcf_exons.py b/src/bed_handler/select_regulated_near_ctcf_exons.py
index a9c2fdb..11d37e4 100644
--- a/src/bed_handler/select_regulated_near_ctcf_exons.py
+++ b/src/bed_handler/select_regulated_near_ctcf_exons.py
@@ -13,6 +13,8 @@ from doctest import testmod
from .filter_gene import filter_bed
import warnings
from .get_other_exon_in_same_gene import create_gene_bed4norm
+from pathlib import Path
+import lazyparser as lp
def filter_ctcf_distance_table(df: pd.DataFrame, reg: str, threshold: int,
@@ -128,9 +130,10 @@ def create_bed_ctcf_exon(reg: str, threshold: int,
location: str, include0: bool = False,
near_ctcf: bool = True) -> None:
"""
- Filter the dataframe to recover only regulated exons near CTCF.
+ Filter the dataframe of exon regulated by ddx5/17 to recover only \
+ regulated exons near CTCF.
- :param reg: The regulation by CTCF
+ :param reg: The regulation by DDX5/17
:param threshold: The threshold distance
:param location: The location of interest
:param include0: True to include exons containing a CTCF site if the \
@@ -163,7 +166,9 @@ def create_bed_ctcf_exon(reg: str, threshold: int,
"id"].to_list()
else:
tmp_exons = df_reg['id'].to_list()
- list_exons = [e for e in tmp_exons if e not in df['id'].to_list()]
+ bad_id = df['id'].to_list() if include0 \
+ else df['id'].to_list() + df.loc[df['dist'] == 0, 'id'].to_list()
+ list_exons = [e for e in tmp_exons if e not in bad_id]
list_genes = [int(exon.split('_')[0]) for exon in list_exons]
df_exon = filter_bed(BedConfig.exon_bed, list_exons)
df_gene = filter_bed(BedConfig.gene_bed, list_genes)
@@ -185,6 +190,79 @@ def create_bed_ctcf_exon(reg: str, threshold: int,
index=False)
+@lp.parse(exon_bed="file", location=["upstream", "downstream", "both"],
+ threshold="threshold >= 0")
+def get_bed_ctcf_exon(exon_bed: str, threshold: int,
+ location: str, include0: bool = False,
+ near_ctcf: bool = True,
+ name_near: str = "") -> None:
+ """
+ Filter a bed file containing exons to recover only regulated exons \
+ near or far from CTCF.
+
+ :param exon_bed: a bed containing exons
+ :param threshold: The threshold distance
+ :param location: The location of interest
+ :param include0: True to include exons containing a CTCF site if the \
+ threshold is greater than 0.
+ :param near_ctcf: True to recover exons near CTCF False to recover \
+ those far from CTCF
+ :param name_near: prefix of the output file name
+ """
+ if name_near == "":
+ name_near = Path(exon_bed).name.replace(".bed", "")
+ threshold = max(threshold, 0)
+ if threshold == 0:
+ location = "both"
+ if not include0 and threshold == 0:
+ warnings.warn("include0 must be True when threshold = 0."
+ "Setting include0 to true !")
+ include0 = True
+ if not near_ctcf and not include0:
+ warnings.warn("include0 must be True when near_ctcf is False")
+ include0 = True
+ i0 = "with0" if include0 else "without0"
+ df_reg = load_sipp_vs_ctcf(BedConfig.all_vs_ctcf,
+ format_exon_bed(Path(exon_bed),
+ BedConfig.gene_bed))
+ df_reg = df_reg[-df_reg["gene_id"].isna()]
+ df_reg["gene_id"] = df_reg["gene_id"].astype(int)
+ df_reg["exon_pos"] = df_reg["exon_pos"].astype(int)
+ df = filter_ctcf_distance_table(df_reg, 'all', threshold, location,
+ include0)
+ if near_ctcf:
+ name_near = name_near + "_near_"
+ list_exons = df['id'].to_list()
+ else:
+ name_near = name_near + "_far_"
+ tmp_exons = df_reg['id'].to_list()
+ bad_id = df['id'].to_list() if include0 \
+ else df['id'].to_list() + df.loc[df['dist'] == 0, 'id'].to_list()
+ list_exons = [e for e in tmp_exons if e not in bad_id]
+ list_genes = [int(exon.split('_')[0]) for exon in list_exons]
+ df_exon = filter_bed(BedConfig.exon_bed, list_exons)
+ df_gene = filter_bed(BedConfig.gene_bed, list_genes)
+ df_exon.to_csv(BedConfig.bed.output /
+ f"{name_near}CTCF_{threshold}_{location}_ddx_"
+ f"{i0}_exon.bed",
+ sep="\t",
+ index=False)
+ df_gene.to_csv(BedConfig.bed.output /
+ f"{name_near}CTCF_{threshold}_{location}_ddx_"
+ f"{i0}_gene.bed",
+ sep="\t",
+ index=False)
+ df_gene = create_gene_bed4norm(BedConfig.gene_bed, df_exon)
+ df_gene.to_csv(BedConfig.bed.output /
+ f"{name_near}CTCF_{threshold}_{location}_ddx_"
+ f"{i0}_gene-dup.bed",
+ sep="\t",
+ index=False)
+
if __name__ == "__main__":
- testmod()
+ import sys
+ if len(sys.argv) == 1:
+ testmod()
+ else:
+ get_bed_ctcf_exon()
--
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