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Snake_downbam_UMI.py
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Snake_downbam_UMI.py
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#configfile: "config.yaml"
# snakemake -j 9 -s Snake_downbam_UMI.py --configfile config/estimate_read_saturation/se_downsample.yaml --cluster "qsub -l walltime={params.run_time}:00:00 -l nodes=1:ppn={params.cores} -q home-yeo" -n
# snakemake -j 9 -s Snake_downbam_UMI.py --configfile config/estimate_read_saturation/se_downsample_SLBP.yaml --cluster "qsub -l walltime={params.run_time}:00:00 -l nodes=1:ppn={params.cores} -q home-yeo" -n
# downsample bams to match the # of mapped reads
import pandas as pd
import os
import sys
import glob
import numpy as np
manifest = pd.read_table(config['DOWNSAMPLE_MENIFEST'], index_col = 0, sep = ',')
config['CLIPper_pvalthes'] = None
workdir: config['WORKDIR']
def get_total_reads(sample_label):
with open(f"downsample_bam_UMI/{sample_label}.totalcount", 'r') as f:
nread = int(f.readlines())[0]
return int(nread.rstrip())
# downsample targets
targets = [int(i) for i in np.array([0.1, 0.2, 0.5, 0.8, 1, 2, 4]) * int(config['max_read'])]
seeds = []
sample_labels = manifest.Sample.tolist()
module Snake_CLIPper:
snakefile:
# here, plain paths, URLs and the special markers for code hosting providers (see below) are possible.
#"Snake_downbam_UMI",
"rules/Snake_CLIPper.py"
config:
config
module peak_anno:
snakefile:
"rules/Snake_peakanno.py"
config: config
rule all:
input:
expand("downsample_bam_UMI_counts/{sample_label}.{nread}.rmDup.count",
sample_label = [s for s in sample_labels if 'Dan' in s],
nread = targets),
# expand("downsample_bam_UMI/CLIPper/{sample_label}.{nread}.peaks.normed.compressed.annotate.bed",
# sample_label = ['Dan_singleplex_K562_rep1_RBFOX2', 'katieoligo_RBFOX2_rep1'],
# nread = np.array(targets)[[0,1,2]])
output:
"snakeUMI.txt"
params:
error_out_file = "error_files/all",
run_time = 1,
cores = "1",
memory = "20",
job_name = "all"
shell:
"""
echo $(date) > {output};
echo created by HLH and the Yeo lab >> {output}
"""
rule count_read_begin:
input:
bam=lambda wildcards: glob.glob(manifest.loc[manifest.Sample == wildcards.sample_label]["bam"].values[0])
output:
"downsample_bam_UMI/{sample_label}.totalcount"
params:
run_time=1,
error_out_file = "error_files/downsample",
cores = "1",
shell:
"""
module load samtools
samtools view -cF 4 {input.bam} > {output}
"""
rule downsample_bam:
input:
bam=lambda wildcards: glob.glob(manifest.loc[manifest.Sample == wildcards.sample_label]["bam"].values[0]),
count_csv = "downsample_bam_UMI/{sample_label}.totalcount"
output:
subsample_bam="downsample_bam_UMI/{sample_label}.{nread}.bam",
subsample_bai="downsample_bam_UMI/{sample_label}.{nread}.bam.bai"
params:
run_time=1,
error_out_file = "error_files/downsample",
cores = "1",
nread="{nread}"
shell:
"""
module load samtools
total=$(cat {input.count_csv})
frac=$(awk "BEGIN {{print ({params.nread})/$total}}")
samtools view -h -F 4 -s 5$frac {input.bam} | samtools sort - | samtools view -Sb - > {output.subsample_bam}
samtools index {output.subsample_bam}
"""
rule umi_dedup:
input:
bam="downsample_bam_UMI/{sample_label}.{nread}.bam",
bai="downsample_bam_UMI/{sample_label}.{nread}.bam.bai"
output:
bam_dedup="downsample_bam_UMI/{sample_label}.{nread}.rmDup.bam"
params:
error_out_file="error_files/umidedup",
run_time = 4,
cores = "4",
memory = "10000",
job_name = "sortbam",
prefix='downsample_bam_UMI/{sample_label}.{nread}'
shell:
"""
module load eclip;
umi_tools dedup \
--random-seed 1 \
-I {input.bam} \
--method unique \
--output-stats {params.prefix} \
-S {output.bam_dedup}
"""
rule count_read:
input:
bam_dedup="downsample_bam_UMI/{sample_label}.{nread}.rmDup.bam"
output:
"downsample_bam_UMI_counts/{sample_label}.{nread}.rmDup.count"
params:
error_out_file = "error_files/all",
run_time = 1,
cores = "1",
memory = "20",
job_name = "all"
shell:
"""
module load samtools
samtools view -cF 4 {input.bam_dedup} > {output}
"""
rule index_bam:
input:
bam_dedup="downsample_bam_UMI/{sample_label}.{nread}.rmDup.bam"
output:
"downsample_bam_UMI/{sample_label}.{nread}.rmDup.bam.bai"
params:
error_out_file = "error_files/all",
run_time = 1,
cores = "1",
memory = "20",
job_name = "all"
shell:
"""
module load samtools
samtools index {input.bam_dedup}
"""
use rule clipper from Snake_CLIPper as clipper with:
input:
subsample_bam="downsample_bam_UMI/{sample_label}.{nread}.rmDup.bam",
subsample_bai="downsample_bam_UMI/{sample_label}.{nread}.rmDup.bam.bai"
output:
peak="downsample_bam_UMI/CLIPper/{sample_label}.{nread}.peaks.bed"
params:
error_out_file = "error_files/all",
run_time = 16,
cores = "10",
memory = "20",
job_name = "all",
species=config['SPECIES'],
use rule count_read_num from Snake_CLIPper as count_read2 with:
input:
subsample_bam_ip="downsample_bam_UMI/{sample_label}.{nread}.rmDup.bam",
subsample_bam_in=lambda wildcards: glob.glob(manifest.loc[manifest.Sample == wildcards.sample_label]["bam_control"].values[0])
output:
nread_ip="downsample_bam_UMI/output/{sample_label}.{nread}.ip.readnum.txt",
nread_in="downsample_bam_UMI/output/{sample_label}.{nread}.in.readnum.txt"
use rule norm_peaks from Snake_CLIPper as norm_peak with:
input:
subsample_bam_ip="downsample_bam_UMI/{sample_label}.{nread}.rmDup.bam",
subsample_bam_in=lambda wildcards: glob.glob(manifest.loc[manifest.Sample == wildcards.sample_label]["bam_control"].values[0]),
nread_ip="downsample_bam_UMI/output/{sample_label}.{nread}.ip.readnum.txt",
nread_in="downsample_bam_UMI/output/{sample_label}.{nread}.in.readnum.txt",
peak="downsample_bam_UMI/CLIPper/{sample_label}.{nread}.peaks.bed"
output:
norm_peak="downsample_bam_UMI/CLIPper/{sample_label}.{nread}.peaks.normed.bed"
use rule compress_peak from Snake_CLIPper as compress_peak with:
input:
norm_peak="downsample_bam_UMI/CLIPper/{sample_label}.{nread}.peaks.normed.bed"
output:
compress_peak="downsample_bam_UMI/CLIPper/{sample_label}.{nread}.peaks.normed.compressed.bed"
use rule annotate from peak_anno as annotate_peak with:
input:
peak="downsample_bam_UMI/CLIPper/{sample_label}.{nread}.peaks.normed.compressed.bed"
output:
"downsample_bam_UMI/CLIPper/{sample_label}.{nread}.peaks.normed.compressed.annotate.bed"