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IR_ePydoor_part1.py
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IR_ePydoor_part1.py
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"""
@authors: Juan L. Trincado
@email: juanluis.trincado@upf.edu
IR_ePydoor.py: get significat exonizations
"""
import os
import pandas as pd
from lib.IR.extract_significant_IR import *
from lib.IR.IR_associate_gene_ids import *
from lib.IR.IR_kma_associate_gene_ids import *
from lib.IR.filter_IR import *
from lib.IR.filter_IR_CHESS import *
from lib.IR.generate_random_intronic_positions import *
# create logger
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# create console handler and set level to info
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
# create formatter
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# add formatter to ch
ch.setFormatter(formatter)
# add ch to logger
logger.addHandler(ch)
def main():
try:
logger.info("Starting execution IR_ePydoor_part1")
introns_path = "/projects_rg/SCLC_cohorts/cis_analysis/v5/SCLC_v5/tables/iso_tpm_introns_George_Peifer_Rudin_Yokota.txt"
bam_path = "/projects_rg/SCLC_cohorts/George/STAR/all_bams"
TPM_threshold = 1
tumor_specific = True
flag_Rudin = False
introns_Normal_path = "/projects_rg/SCLC_cohorts/cis_analysis/v5/SCLC_v5/tables/iso_tpm_introns_Rudin_Normal.txt"
introns_GTEX_path = "/projects_rg/SCLC_cohorts/annotation/chess2.0_assembly_hg19_CrossMap.events_RI_strict.ioe"
gtf_path = "/projects_rg/SCLC_cohorts/annotation/Homo_sapiens.GRCh37.75.formatted.gtf"
gtf_protein_coding_path = "/projects_rg/SCLC_cohorts/annotation/Homo_sapiens.GRCh37.75.formatted.only_protein_coding.gtf"
output_path = "/users/genomics/juanluis/SCLC_cohorts/SCLC/epydoor/IR"
# 0. Format the intron file
logger.info("Part0...")
dir_path = os.path.dirname(os.path.realpath(__file__))
# command0 = "module load R; Rscript " + dir_path + "/lib/IR/format_intron_file.R " + introns_path + " " + output_path + "/IR_formatted.tab"
# os.system(command0)
# 1. Get the IR expressed
logger.info("Part1...")
# extract_significant_IR(output_path + "/IR_formatted.tab", TPM_threshold, output_path + "/IR_expressed.tab")
extract_significant_IR(introns_path, TPM_threshold, output_path + "/IR_expressed.tab")
# 2. Obtain the gene ids for the introns.
logger.info("Part2...")
# Separate between introns from kma (U2) and U12
command1="head -n1 "+output_path + "/IR_expressed.tab > "+output_path + "/IR_kma_expressed.tab; grep kma_introns "\
+output_path + "/IR_expressed.tab >> "+output_path + "/IR_kma_expressed.tab"
os.system(command1)
command2 = "grep -v kma_introns "+output_path + "/IR_expressed.tab > "+output_path + "/IR_no_kma_expressed.tab"
os.system(command2)
IR_associate_gene_ids(output_path + "/IR_no_kma_expressed.tab", gtf_path, output_path + "/IR_no_kma_expressed_genes.tab")
IR_kma_associate_gene_ids(output_path + "/IR_kma_expressed.tab", gtf_path, output_path + "/IR_kma_expressed_genes.tab")
command3 = "cat "+output_path + "/IR_kma_expressed_genes.tab > "+output_path + "/IR_expressed_genes.tab; tail -n+2 "\
+output_path + "/IR_no_kma_expressed_genes.tab >> "+output_path + "/IR_expressed_genes.tab"
os.system(command3)
# 3. Get the IR tumor specific
if(tumor_specific):
if(flag_Rudin):
#Get the significant introns for the set of normal
extract_significant_IR(introns_Normal_path, TPM_threshold, output_path + "/IR_expressed_Normal.tab")
#Filter by a set of Normal
output_path_filtered = output_path + "/IR_expressed_genes_filtered.tab"
filter_IR(output_path + "/IR_expressed_genes.tab", output_path + "/IR_expressed_Normal.tab", output_path_filtered)
# Filter by a set of Normal (GTEX)
output_path_filtered2 = output_path + "/IR_expressed_genes_filtered2.tab"
filter_IR_CHESS(output_path_filtered, introns_GTEX_path, output_path_filtered2)
else:
# Filter by a set of Normal (GTEX)
output_path_filtered2 = output_path + "/IR_expressed_genes_filtered2.tab"
filter_IR_CHESS(output_path + "/IR_expressed_genes.tab", introns_GTEX_path, output_path_filtered2)
else:
output_path_filtered2 = output_path + "/IR_expressed_genes.tab"
# 4. Generate random positions for each intron
logger.info("Part4...")
generate_random_intronic_positions(output_path_filtered2, gtf_protein_coding_path, 100, output_path + "/random_introns.gtf",
output_path + "/random_introns.bed")
# 5. Run coverageBed on the samples in the cluster
logger.info("Part5...")
dir_path = os.path.dirname(os.path.realpath(__file__))
# 5.1. If there is any chr missing in the bed file, add an extra line with this info
introns = pd.read_table(output_path + "/random_introns.bed",names=["chr", "start", "end", "id", "strand", "zero"])
chr_unique = introns.chr.unique().tolist()
chr_set = ["chr1","chr2","chr3","chr4","chr5","chr6","chr7","chr8","chr9","chr10","chr11","chr12","chr13",
"chr14","chr15","chr16","chr17","chr18","chr19","chr20","chr21","chr22","chrX","chrY"]
for element in chr_set:
if (element not in chr_unique):
with open(output_path + "/random_introns.bed", "a") as file:
file.write(element+"\t1\t1\tExonization_0_Random_0\t+\t0\n")
#Sort the df by chr
introns = pd.read_table(output_path + "/random_introns.bed",names=["chr", "start", "end", "id", "strand", "zero"])
#Add a numeric columns associated with the chromosome
introns["chr_num"] = introns["chr"].apply(lambda x: x[3:].rstrip())
#X tranform it to 23 and Y to 24
introns["chr_num"] = introns["chr_num"].replace('X', 23)
introns["chr_num"] = introns["chr_num"].replace('Y', 24)
introns.chr_num = pd.to_numeric(introns.chr_num, errors='coerce')
introns.start = pd.to_numeric(introns.start, errors='coerce')
introns.end = pd.to_numeric(introns.end, errors='coerce')
introns_sorted = introns.sort_values(by=['chr_num','start','end'])
# remove the last column and save
del introns_sorted['chr_num']
introns_sorted.to_csv(output_path + "/random_introns.bed", sep="\t", index=False)
# 5.2. Run a job per sample
command3="for sample in $(ls "+bam_path+"/*/*.bam);do " \
"sample_id=$(echo $sample | awk -F '/' '{print $(NF-1)}');" \
"echo \"Processing file $sample: \"$(date); sbatch -J $(echo $sample)_coverageBed "+dir_path+"/coverageBed.sh $(echo $sample) " \
+ output_path + "/random_introns.bed "+output_path+"/$(echo $sample_id).coverage_sorted;done"
os.system(command3)
logger.info("Wait until all jobs have finished. Then, go on with part2")
exit(0)
except Exception as error:
logger.error('ERROR: ' + repr(error))
logger.error("Aborting execution")
sys.exit(1)
if __name__ == '__main__':
main()