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Snakefile
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Snakefile
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#####################################################################################################
# Bisulphite sequencing (methyl-seek) analysis workflow
#
# Execution mode: run and dmr
# Last Modified: August, 2022
#
#####################################################################################################
from os.path import join
from snakemake.io import expand, glob_wildcards
from os import listdir
import pandas as pd
import json
# Helper functions
def allocated(resource, rule, lookup, default="__default__"):
"""Pulls resource information for a given rule. If a rule does not have any information
for a given resource type, then it will pull from the default. Information is pulled from
definitions in the cluster.json (which is used a job submission). This ensures that any
resources used at runtime mirror the resources that were allocated.
:param resource <str>: resource type to look in cluster.json (i.e. threads, mem, time, gres)
:param rule <str>: rule to lookup its information
:param lookup <dict>: Lookup containing allocation information (i.e. cluster.json)
:param default <str>: default information to use if rule information cannot be found
:return allocation <str>:
allocation information for a given resource type for a given rule
"""
try:
# Try to get allocation information
# for a given rule
allocation = lookup[rule][resource]
except KeyError:
# Use default allocation information
allocation = lookup[default][resource]
return allocation
# Global workflow variables
sample_file = config["samples"]
rawdata_dir = config["rawdata_dir"]
working_dir = config["result_dir"]
# References
hg38_fa = config["hg38_fa"]
phage_fa = config["phage_fa"]
hg38_gtf = config["hg38_gtf"]
hg38_rRNA_intervals = config["hg38_rRNA_intervals"]
hg38_bed_ref = config["hg38_bed_ref"]
hg38_refFlat = config["hg38_refFlat"]
bisulphite_genome_path = config["bisulphite_ref"]
phage_genome_path = config["phage_ref"]
bisulphite_fa = config["bisulphite_fa"]
species = config["species"]
REF_ATLAS = config["REF_ATLAS"]
CpG_MAP_TABLE = config["CpG_MAP_TABLE"]
wgbsDir = config["wgbsDir"]
mode = config["Mode"]
REF_MARKERS = config["REF_MARKERS"]
REF_IDS = config["REF_IDS"]
GOLD_MARKERS = config["GOLD_MARKERS"]
HG38TOHG19 = config["HG38TOHG19"]
if species == "hg38":
atlas_bed = config["atlas_bed_hg38"]
atlas_tsv = config["atlas_tsv_hg38"]
if species == "hg19":
atlas_bed = config["atlas_bed_hg19"]
atlas_tsv = config["atlas_tsv_hg19"]
# sample list
df = pd.read_csv(sample_file, header=0, sep='\t')
SAMPLES = list(set(df['samples'].tolist()))
GROUPS = list(set(df['group'].tolist()))
# Read in resource information,
# containing information about
# threads, mem, walltimes, etc.
with open(join('cluster.json')) as fh:
cluster = json.load(fh)
CHRS = [
'chr1', 'chr2', 'chr3',
'chr4', 'chr5', 'chr6',
'chr7', 'chr8', 'chr9',
'chr10', 'chr11', 'chr12',
'chr13', 'chr14', 'chr15',
'chr16', 'chr17', 'chr18',
'chr19', 'chr20', 'chr21',
'chr22', 'chr23', 'chrX'
]
RN = ['R1', 'R2']
dmr_dir = working_dir
if mode == "dmr":
# contrasts list
df2 = pd.read_csv(working_dir + "/contrasts.txt", header=0, sep='\t')
GROUPS=list(set(df2['comparisons'].tolist()))
GRPSAMPLES=list(set(df2['samples'].tolist()))
dmr_dir = join(working_dir, "dmr", GROUPS[0])
def output_from_modes():
outputs = list()
if mode == "run":
outputs.append(join(working_dir, "multiqc_report.html"))
if mode == "dmr":
outputs.append(join(working_dir, "multiqc_report.html"))
#homer files
outputs.append(expand(join(dmr_dir, "homer/{group}_{chr}.homerOutput.txt"),group=GROUPS,chr=CHRS))
outputs.append(expand(join(dmr_dir, "homer/{group}_{chr}.homerOutput2.txt"),group=GROUPS,chr=CHRS))
# lm methylation sites from bismark alignments
outputs.append(expand(join(dmr_dir, "bsseq/{group}_{chr}_betas_pval.bed"),chr=CHRS,group=GROUPS))
#outputs.append(expand(join(dmr_dir, "bsseq/{group}_{chr}_betas_wtFDR_unfiltered.bed"),chr=CHRS,group=GROUPS))
outputs.append(expand(join(dmr_dir, "combP/{group}_{chr}.regions-p.bed.gz"),chr=CHRS,group=GROUPS))
# summary figures of analysis
outputs.append(expand(join(dmr_dir,"figs/{group}_manhatten_beta.png"),group=GROUPS))
# combp summary of results
outputs.append(expand(join(dmr_dir, "combP/{group}.regions-p.bed"),group=GROUPS))
outputs.append(expand(join(dmr_dir, "figs/{group}_GREATfig1.png"),group=GROUPS))
outputs.append(expand(join(dmr_dir, "figs/{group}_manhatten.png"),group=GROUPS))
outputs.append(expand(join(dmr_dir, "combP/{group}.GREATprocesses.txt"),group=GROUPS))
outputs.append(expand(join(dmr_dir, "combP/{group}.GREATfunction.txt"),group=GROUPS))
#MVP mvp_plots
outputs.append(expand(join(dmr_dir, "combP/{group}.miami.png"),group=GROUPS))
outputs.append(expand(join(dmr_dir, "combP/{group}.violin.png"),group=GROUPS))
#outputs.append(expand(join(dmr_dir, "combP/{group}.pie-cpg.png"),group=GROUPS))
#outputs.append(expand(join(dmr_dir, "combP/{group}.pie-genic.png"),group=GROUPS))
if mode == "dcv":
outputs.append(join(working_dir, "multiqc_report.html"))
outputs.append(expand(join(working_dir, "CpG_CSV/{samples}.csv"),samples=SAMPLES))
outputs.append(expand(join(working_dir, "deconvolution_CSV/{samples}.csv"),samples=SAMPLES))
outputs.append(expand(join(working_dir, "deconvolution_CSV/{samples}_deconv.log"),samples=SAMPLES))
outputs.append(join(working_dir, "deconvolution_CSV/total.csv"))
outputs.append(join(working_dir, "deconvolution_CSV/total_deconv_output.csv"))
outputs.append(join(working_dir, "deconvolution_CSV/total_deconv_plot.png"))
#outputs.append(expand(join(working_dir, "bismarkAlign/{samples}.bismark_pe.deduplicated.bam"),samples=SAMPLES))
outputs.append(expand(join(working_dir,"CpG/{samples}/{samples}.cfDNAmeInput.bedGraph"),samples=SAMPLES))
outputs.append(expand(join(working_dir,"CpG/{samples}/{samples}.cfDNAmeDeconvolution.tsv"),samples=SAMPLES))
outputs.append(expand(join(working_dir,"UXM/{samples}.pat.gz"), samples=SAMPLES))
outputs.append(expand(join(working_dir,"UXM/{samples}_deconv.250.csv"), samples=SAMPLES))
return(outputs)
rule All:
input:
# Creating data links:
expand(join(working_dir, "raw/{samples}.{rn}.fastq.gz"), samples=SAMPLES, rn=RN),
# Checking data quality:
expand(join(working_dir, "rawQC/{samples}.{rn}_fastqc.html"), samples=SAMPLES, rn=RN),
# Quality trimming output:
expand(join(working_dir, "trimGalore/{samples}_val_1.fq.gz"),samples=SAMPLES),
expand(join(working_dir, "trimGalore/{samples}_val_2.fq.gz"),samples=SAMPLES),
# kraken output
#expand(join(working_dir, "kraken","{samples}.trim.kraken_bacteria.out.txt"),samples=SAMPLES),
#expand(join(working_dir, "kraken","{samples}.trim.kraken_bacteria.taxa.txt"),samples=SAMPLES),
#expand(join(working_dir, "kraken","{samples}.trim.kraken_bacteria.krona.html"),samples=SAMPLES),
#FQscreen output
#expand(join(working_dir, "FQscreen","{samples}_val_1_screen.txt"),samples=SAMPLES),
#expand(join(working_dir, "FQscreen","{samples}_val_1_screen.png"),samples=SAMPLES),
#expand(join(working_dir, "FQscreen","{samples}_val_2_screen.txt"),samples=SAMPLES),
#expand(join(working_dir, "FQscreen","{samples}_val_2_screen.png"),samples=SAMPLES),
#expand(join(working_dir, "FQscreen2","{samples}_val_1_screen.txt"),samples=SAMPLES),
#expand(join(working_dir, "FQscreen2","{samples}_val_1_screen.png"),samples=SAMPLES),
#expand(join(working_dir, "FQscreen2","{samples}_val_2_screen.txt"),samples=SAMPLES),
#expand(join(working_dir, "FQscreen2","{samples}_val_2_screen.png"), samples=SAMPLES),
# bisulphite genome preparation
join(bisulphite_genome_path, species, "Bisulfite_Genome/CT_conversion/genome_mfa.CT_conversion.fa"),
join(bisulphite_genome_path, species, "Bisulfite_Genome/GA_conversion/genome_mfa.GA_conversion.fa"),
# bismark align to human reference genomes
expand(join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.bam"),samples=SAMPLES),
expand(join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.deduplicated.cram"),samples=SAMPLES),
expand(join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.flagstat"),samples=SAMPLES),
expand(join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.deduplicated.flagstat"),samples=SAMPLES),
# get alignment statistics
expand(join(working_dir, "bismarkAlign/{samples}.RnaSeqMetrics.txt"),samples=SAMPLES),
expand(join(working_dir, "bismarkAlign/{samples}.flagstat.concord.txt"),samples=SAMPLES),
#expand(join(working_dir, "rseqc/{samples}.inner_distance_freq.txt"),samples=SAMPLES),
#expand(join(working_dir, "rseqc/{samples}.strand.info"),samples=SAMPLES),
#expand(join(working_dir, "rseqc/{samples}.Rdist.info"),samples=SAMPLES),
#expand(join(working_dir, "trimGalore/{samples}_insert_sizes.txt"),samples=SAMPLES),
expand(join(working_dir, "CpG/{samples}/{samples}.bismark_bt2_pe.deduplicated.CpG_report.txt.gz"),samples=SAMPLES),
expand(join(working_dir, "CpG/{samples}/{samples}.bismark_bt2_pe.deduplicated.bedGraph.gz"),samples=SAMPLES),
join(working_dir, "bismark_summary_report.txt"),
join(working_dir, "bismark_summary_report.html"),
output_from_modes()
######### generate multiqc output
#join(working_dir, "multiqc_report.html"),
######## Following lines are applicable only when "dcv" mode is enabled ###############
# Deconvolution output
#expand(join(working_dir, "CpG_CSV/{samples}.csv"),samples=SAMPLES),
#expand(join(working_dir, "deconvolution_CSV/{samples}.csv"),samples=SAMPLES),
#expand(join(working_dir, "deconvolution_CSV/{samples}_deconv.log"),samples=SAMPLES),
#join(working_dir, "deconvolution_CSV/total.csv"),
#join(working_dir, "deconvolution_CSV/total_deconv_output.csv"),
#join(working_dir, "deconvolution_CSV/total_deconv_plot.png"),
## Copy raw data to working directory
rule raw_data_links:
input:
join(rawdata_dir, "{samples}.{rn}.fastq.gz")
output:
join(working_dir, "raw/{samples}.{rn}.fastq.gz")
params:
rname="raw_data_links",
dir=directory(join(working_dir, "raw")),
resources:
mem = allocated("mem", "raw_data_links", cluster),
gres = allocated("gres", "raw_data_links", cluster),
time = allocated("time", "raw_data_links", cluster),
partition = allocated("partition", "raw_data_links", cluster),
threads:
int(allocated("threads", "raw_data_links", cluster)),
shell:
"""
mkdir -p {params.dir}
ln -s {input} {output}
"""
## Run fastqc on raw data to visually assess quality
rule raw_fastqc:
input:
join(working_dir, "raw/{samples}.{rn}.fastq.gz")
output:
join(working_dir, "rawQC/{samples}.{rn}_fastqc.html")
params:
rname="raw_fastqc",
dir=directory(join(working_dir, "rawQC")),
batch='--cpus-per-task=2 --mem=8g --time=8:00:00',
resources:
mem = allocated("mem", "raw_fastqc", cluster),
gres = allocated("gres", "raw_fastqc", cluster),
time = allocated("time", "raw_fastqc", cluster),
partition = allocated("partition", "raw_fastqc", cluster),
threads:
int(allocated("threads", "raw_fastqc", cluster)),
shell:
"""
module load fastqc/0.11.9
mkdir -p {params.dir}
fastqc -o {params.dir} -f fastq --threads {threads} --extract {input}
"""
## trimming/filtering with trimGalore
rule trimGalore:
input:
F1=join(working_dir, "raw/{samples}.R1.fastq.gz"),
F2=join(working_dir, "raw/{samples}.R2.fastq.gz"),
output:
join(working_dir, "trimGalore/{samples}_val_1.fq.gz"),
join(working_dir, "trimGalore/{samples}_val_2.fq.gz")
params:
rname="trimGalore",
dir=directory(join(working_dir, "trimGalore")),
tag='{samples}',
fastqcdir=directory(join(working_dir, "postTrimQC")),
command="--fastqc --clip_R1 10 --clip_R2 10 --three_prime_clip_R1 10 --three_prime_clip_R2 10 --length 50 --gzip",
batch='--cpus-per-task=16 --partition=norm --gres=lscratch:100 --mem=25g --time=10:00:00',
workdir = working_dir,
resources:
mem = allocated("mem", "trimGalore", cluster),
gres = allocated("gres", "trimGalore", cluster),
time = allocated("time", "trimGalore", cluster),
partition = allocated("partition", "trimGalore", cluster),
threads:
int(allocated("threads", "trimGalore", cluster)),
shell:
"""
module load trimgalore/0.6.7
mkdir -p {params.dir}
trim_galore --paired --cores {threads} {params.command} --basename {params.tag} --output_dir {params.dir} --fastqc_args "--outdir {params.fastqcdir}" {input.F1} {input.F2}
"""
rule bbmerge:
input:
R1=join(working_dir, "trimGalore/{samples}_val_1.fq.gz"),
R2=join(working_dir, "trimGalore/{samples}_val_2.fq.gz"),
output:
join(working_dir, "trimGalore/{samples}_insert_sizes.txt"),
params:
rname='pl:bbmerge',
script_dir=join(working_dir,"scripts"),
resources:
mem = allocated("mem", "bbmerge", cluster),
gres = allocated("gres", "bbmerge", cluster),
time = allocated("time", "bbmerge", cluster),
partition = allocated("partition", "bbmerge", cluster),
threads:
int(allocated("threads", "bbmerge", cluster)),
shell:
"""
# Get encoding of Phred Quality Scores
module load python
encoding=$(python {params.script_dir}/phred_encoding.py {input.R1})
echo "Detected Phred+${{encoding}} ASCII encoding"
module load bbtools/38.87
bbtools bbmerge-auto in1={input.R1} in2={input.R2} qin=${{encoding}} \
ihist={output} k=62 extend2=200 rem ecct -Xmx900G
"""
rule fastq_screen:
input:
file1=join(working_dir, "trimGalore/{samples}_val_1.fq.gz"),
file2=join(working_dir, "trimGalore/{samples}_val_2.fq.gz"),
output:
out1=join(working_dir,"FQscreen","{samples}_val_1_screen.txt"),
out2=join(working_dir,"FQscreen","{samples}_val_1_screen.png"),
out3=join(working_dir,"FQscreen","{samples}_val_2_screen.txt"),
out4=join(working_dir,"FQscreen","{samples}_val_2_screen.png"),
out5=join(working_dir,"FQscreen2","{samples}_val_1_screen.txt"),
out6=join(working_dir,"FQscreen2","{samples}_val_1_screen.png"),
out7=join(working_dir,"FQscreen2","{samples}_val_2_screen.txt"),
out8=join(working_dir,"FQscreen2","{samples}_val_2_screen.png")
params:
rname='pl:fqscreen',
outdir = join(working_dir,"FQscreen"),
outdir2 = join(working_dir,"FQscreen2"),
fastq_screen_config="/data/CCBR_Pipeliner/db/PipeDB/lib/fastq_screen.conf",
fastq_screen_config2="/data/CCBR_Pipeliner/db/PipeDB/lib/fastq_screen_2.conf",
resources:
mem = allocated("mem", "fastq_screen", cluster),
gres = allocated("gres", "fastq_screen", cluster),
time = allocated("time", "fastq_screen", cluster),
partition = allocated("partition", "fastq_screen", cluster),
threads:
int(allocated("threads", "fastq_screen", cluster)),
shell:
"""
module load fastq_screen/0.14.1
module load bowtie/2-2.3.4
module load perl/5.24.3
fastq_screen --conf {params.fastq_screen_config} --outdir {params.outdir} \
--threads {threads} --subset 1000000 \
--aligner bowtie2 --force {input.file1} {input.file2}
fastq_screen --conf {params.fastq_screen_config2} --outdir {params.outdir2} \
--threads {threads} --subset 1000000 \
--aligner bowtie2 --force {input.file1} {input.file2}
"""
rule kraken_pe:
input:
fq1=join(working_dir, "trimGalore/{samples}_val_1.fq.gz"),
fq2=join(working_dir, "trimGalore/{samples}_val_2.fq.gz"),
output:
krakenout = join(working_dir, "kraken","{samples}.trim.kraken_bacteria.out.txt"),
krakentaxa = join(working_dir, "kraken","{samples}.trim.kraken_bacteria.taxa.txt"),
kronahtml = join(working_dir, "kraken","{samples}.trim.kraken_bacteria.krona.html"),
params:
rname='pl:kraken',
dir=join(working_dir,"kraken"),
#bacdb="/fdb/kraken/20170202_bacteria",
bacdb="/fdb/kraken/20210223_standard_kraken2",
prefix="{samples}",
resources:
mem = allocated("mem", "kraken_pe", cluster),
gres = allocated("gres", "kraken_pe", cluster),
time = allocated("time", "kraken_pe", cluster),
partition = allocated("partition", "kraken_pe", cluster),
threads:
int(allocated("threads", "kraken_pe", cluster)),
shell:
"""
module load kraken
module load kronatools/2.7
mkdir -p {params.dir}
cd /lscratch/$SLURM_JOBID;
cp -rv {params.bacdb} /lscratch/$SLURM_JOBID/;
kdb_base=$(basename {params.bacdb})
kraken2 --db /lscratch/$SLURM_JOBID/${{kdb_base}} \
--threads {threads} --report {output.krakentaxa} \
--output {output.krakenout} \
--gzip-compressed \
--paired {input.fq1} {input.fq2}
# Generate Krona Report
cut -f2,3 {output.krakenout} | ktImportTaxonomy - -o {output.kronahtml}
#kdb_base=$(basename {params.bacdb})
#kraken --db /lscratch/$SLURM_JOBID/`echo {params.bacdb}|awk -F "/" '{{print \$NF}}'` --fastq-input --gzip-compressed --threads {threads} --output /lscratch/$SLURM_JOBID/{params.prefix}.krakenout --preload--paired {input.fq1} {input.fq2}
#kraken-translate --mpa-format --db /lscratch/$SLURM_JOBID/`echo {params.bacdb}|awk -F "/" '{{print \$NF}}'` /lscratch/$SLURM_JOBID/{params.prefix}.krakenout |cut -f2|sort|uniq -c|sort -k1,1nr > /lscratch/$SLURM_JOBID/{params.prefix}.krakentaxa
#cut -f 2,3 /lscratch/$SLURM_JOBID/{params.prefix}.krakenout | ktImportTaxonomy - -o /lscratch/$SLURM_JOBID/{params.prefix}.kronahtml
"""
######## NEED TO PUT CONDITION HERE:
# If user provided different genome version, then run this "prep_bisulphite_genome" rule:
#
##########
rule prep_bisulphite_genome:
input:
bisulphite_fa
output:
join(bisulphite_genome_path, species, "Bisulfite_Genome/CT_conversion/genome_mfa.CT_conversion.fa"),
join(bisulphite_genome_path, species, "Bisulfite_Genome/GA_conversion/genome_mfa.GA_conversion.fa"),
params:
rname="prep_bisulphite_genome",
dir=directory(join(bisulphite_genome_path, species)),
batch='--cpus-per-task=16 --partition=norm --gres=lscratch:100 --mem=20g --time=2:00:00',
resources:
mem = allocated("mem", "prep_bisulphite_genome", cluster),
gres = allocated("gres", "prep_bisulphite_genome", cluster),
time = allocated("time", "prep_bisulphite_genome", cluster),
partition = allocated("partition", "prep_bisulphite_genome", cluster),
threads:
int(allocated("threads", "prep_bisulphite_genome", cluster)),
shell:
"""
module load bismark/0.23.0
mkdir -p {params.dir}
cd {params.dir}
#cp reference_genome {params.dir}/genome.fa
bismark_genome_preparation --verbose --parallel {threads} {params.dir} #--single_fasta
"""
rule bismark_align:
input:
F1=join(working_dir, "trimGalore/{samples}_val_1.fq.gz"),
F2=join(working_dir, "trimGalore/{samples}_val_2.fq.gz"),
output:
B1=join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.bam"),
B2=join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.flagstat"),
FQ1=temp(join(working_dir, "bismarkAlign/{samples}_val_1.fq.gz_unmapped_reads_1.fq.gz")),
FQ2=temp(join(working_dir, "bismarkAlign/{samples}_val_2.fq.gz_unmapped_reads_2.fq.gz")),
FQ3=temp(join(working_dir, "bismarkAlign/{samples}_val_1.fq.gz_ambiguous_reads_1.fq.gz")),
FQ4=temp(join(working_dir, "bismarkAlign/{samples}_val_2.fq.gz_ambiguous_reads_2.fq.gz")),
params:
rname="bismark_align",
dir=directory(join(working_dir, "bismarkAlign")),
genome_dir=directory(join(bisulphite_genome_path, species)),
command="--bowtie2 -N 1 --bam -L 22 --X 1000 --un --ambiguous -p 4 --score_min L,-0.6,-0.6",
batch='--cpus-per-task=16 --partition=norm --gres=lscratch:100 --mem=100g --time=10:00:00',
outbam=join(working_dir, "bismarkAlign/{samples}_val_1_bismark_bt2_pe.bam"),
R1=join(working_dir,"bismarkAlign/{samples}_val_1_bismark_bt2_PE_report.txt"),
R2=join(working_dir,"bismarkAlign/{samples}.bismark_bt2_PE_report.txt"),
resources:
mem = allocated("mem", "bismark_align", cluster),
gres = allocated("gres", "bismark_align", cluster),
time = allocated("time", "bismark_align", cluster),
partition = allocated("partition", "bismark_align", cluster),
threads:
int(allocated("threads", "bismark_align", cluster)),
shell:
"""
module load bismark/0.23.0 samtools
mkdir -p {params.dir}
bismark --multicore {threads} --temp_dir /lscratch/$SLURM_JOBID/ {params.command} --output_dir {params.dir} --genome {params.genome_dir} -1 {input.F1} -2 {input.F2}
mv {params.outbam} {output.B1}
mv {params.R1} {params.R2}
samtools flagstat -@ {threads} {output.B1} > {output.B2}
"""
rule bismark_dedup:
input:
F1=join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.bam"),
output:
T1=temp(join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.deduplicated.deduplicated.bam")),
B1=temp(join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.deduplicated.bam")),
B2=join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.deduplicated.flagstat"),
C1=join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.deduplicated.cram"),
params:
rname="bismark_dedup",
dir=directory(join(working_dir, "bismarkAlign")),
prefix=join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe"),
genome=bisulphite_fa,
resources:
mem = allocated("mem", "bismark_dedup", cluster),
gres = allocated("gres", "bismark_dedup", cluster),
time = allocated("time", "bismark_dedup", cluster),
partition = allocated("partition", "bismark_dedup", cluster),
threads:
int(allocated("threads", "bismark_dedup", cluster)),
shell:
"""
module load bismark/0.23.0
module load samtools/1.15
cd {params.dir}
samtools view -hb {input.F1} | samtools sort -n -@ {threads} -O BAM -o {output.T1}
deduplicate_bismark --paired --bam --outfile {params.prefix} {output.T1}
samtools view -T {params.genome} -@ 16 -h -C {output.B1} > {output.C1}
samtools flagstat -@ {threads} {output.B1} > {output.B2}
"""
rule bismark_extract:
input:
bam=join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.deduplicated.bam"),
output:
cov=join(working_dir, "CpG/{samples}/{samples}.bismark_bt2_pe.deduplicated.CpG_report.txt.gz"),
bed=join(working_dir,"CpG/{samples}/{samples}.bismark_bt2_pe.deduplicated.bedGraph.gz"),
params:
rname='pl:bismark_extract',
bismark_index=join(bisulphite_genome_path,species),
outdir=join(working_dir, "CpG/{samples}"),
genome=bisulphite_fa,
resources:
mem = lambda wc, attempt: allocated("mem", "bismark_extract", cluster) if attempt == 1 else "900g",
gres = allocated("gres", "bismark_extract", cluster),
time = allocated("time", "bismark_extract", cluster),
partition = lambda wc, attempt: allocated("partition", "bismark_extract", cluster) if attempt == 1 else "largemem",
threads:
int(allocated("threads", "bismark_extract", cluster)),
shell:
"""
mkdir -p {params.outdir}
module load bismark samtools bowtie
bismark_methylation_extractor --paired-end --no_overlap --multicore 8 --gzip --report --bedGraph --counts --buffer_size '50%' --no_header --cytosine_report --output {params.outdir} --scaffolds --genome_folder {params.bismark_index} {input.bam}
"""
rule prep_bisulphite_phage_genome:
input:
phage_fa
output:
join(phage_genome_path, "Bisulfite_Genome/CT_conversion/genome_mfa.CT_conversion.fa"),
join(phage_genome_path, "Bisulfite_Genome/GA_conversion/genome_mfa.GA_conversion.fa"),
params:
rname="prep_phage_genome",
dir=directory(phage_genome_path),
batch='--cpus-per-task=16 --partition=norm --gres=lscratch:100 --mem=20g --time=2:00:00',
resources:
mem = allocated("mem", "prep_bisulphite_phage_genome", cluster),
gres = allocated("gres", "prep_bisulphite_phage_genome", cluster),
time = allocated("time", "prep_bisulphite_phage_genome", cluster),
partition = allocated("partition", "prep_bisulphite_phage_genome", cluster),
threads:
int(allocated("threads", "prep_bisulphite_phage_genome", cluster)),
shell:
"""
module load bismark/0.23.0
mkdir -p {params.dir}
cd {params.dir}
cp reference_genome {params.dir}/genome.fa
bismark_genome_preparation --verbose --parallel {threads} {params.dir} #--single_fasta
"""
rule bismark_phage:
input:
F1=join(working_dir, "trimGalore/{samples}_val_1.fq.gz"),
F2=join(working_dir, "trimGalore/{samples}_val_2.fq.gz"),
output:
join(working_dir, "bismark_phage/{samples}_val_1_bismark_bt2_pe.bam"),
params:
rname="bismark_phage",
dir=directory(join(working_dir, "bismark_phage")),
genome_dir=directory(phage_genome_path),
command="--bowtie2 -N 1 --bam -L 22 --X 1000 --un --ambiguous -p 2 --score_min L,-0.6,-0.6",
batch='--cpus-per-task=16 --partition=norm --gres=lscratch:100 --mem=100g --time=10:00:00',
resources:
mem = allocated("mem", "bismark_phage", cluster),
gres = allocated("gres", "bismark_phage", cluster),
time = allocated("time", "bismark_phage", cluster),
partition = allocated("partition", "bismark_phage", cluster),
threads:
int(allocated("threads", "bismark_phage", cluster)),
shell:
"""
module load bismark/0.23.0
mkdir -p {params.dir}
bismark --multicore {threads} --temp_dir /lscratch/$SLURM_JOBID/ {params.command} --output_dir {params.dir} --genome {params.genome_dir} -1 {input.F1} -2 {input.F2}
"""
rule rseqc:
input:
file1=join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.deduplicated.bam"),
output:
out1=join(working_dir,"rseqc/{samples}.strand.info"),
out2=join(working_dir,"rseqc/{samples}.Rdist.info")
params:
bedref=hg38_bed_ref,
dir=join(working_dir,"rseqc"),
rname="pl:rseqc",
resources:
mem = allocated("mem", "rseqc", cluster),
gres = allocated("gres", "rseqc", cluster),
time = allocated("time", "rseqc", cluster),
partition = allocated("partition", "rseqc", cluster),
threads:
int(allocated("threads", "rseqc", cluster)),
shell:
"""
module load rseqc/4.0.0
mkdir -p {params.dir}
infer_experiment.py -r {params.bedref} -i {input.file1} -s 1000000 > {output.out1}
read_distribution.py -i {input.file1} -r {params.bedref} > {output.out2}
"""
rule inner_distance:
input:
bam=join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.deduplicated.bam"),
output:
innerdists=join(working_dir,"rseqc/{samples}.inner_distance_freq.txt"),
params:
prefix=join(working_dir,"rseqc/{samples}"),
dir=join(working_dir,"rseqc"),
genemodel=hg38_bed_ref,
rname="pl:inner_distance",
resources:
mem = allocated("mem", "inner_distance", cluster),
gres = allocated("gres", "inner_distance", cluster),
time = allocated("time", "inner_distance", cluster),
partition = allocated("partition", "inner_distance", cluster),
threads:
int(allocated("threads", "inner_distance", cluster)),
shell:
"""
module load rseqc/4.0.0
mkdir -p {params.dir}
inner_distance.py -i {input.bam} -r {params.genemodel} -k 10000000 -o {params.prefix}
"""
rule stats:
input:
file1=join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.deduplicated.bam"),
output:
outstar1=join(working_dir,"bismarkAlign/{samples}.RnaSeqMetrics.txt"),
outstar2=join(working_dir,"bismarkAlign/{samples}.flagstat.concord.txt"),
params:
rname='pl:stats',
refflat=hg38_refFlat,
rrnalist=hg38_rRNA_intervals,
picardstrand="SECOND_READ_TRANSCRIPTION_STRAND",
statscript=join("workflow", "scripts", "bam_count_concord_stats.py"),
resources:
mem = allocated("mem", "stats", cluster),
gres = allocated("gres", "stats", cluster),
time = allocated("time", "stats", cluster),
partition = allocated("partition", "stats", cluster),
threads:
int(allocated("threads", "stats", cluster)),
shell:
"""
module load python/3.8 samtools/1.15 picard/2.27.3
java -Xmx110g -jar ${{PICARDJARPATH}}/picard.jar CollectRnaSeqMetrics REF_FLAT={params.refflat} I={input.file1} O={output.outstar1} RIBOSOMAL_INTERVALS={params.rrnalist} STRAND_SPECIFICITY=SECOND_READ_TRANSCRIPTION_STRAND TMP_DIR=/lscratch/$SLURM_JOBID VALIDATION_STRINGENCY=SILENT;
sed -i 's/CollectRnaSeqMetrics/picard.analysis.CollectRnaSeqMetrics/g' {output.outstar1}
samtools flagstat {input.file1} > {output.outstar2};
## python3 {params.statscript} {input.file1} >> {output.outstar2} ## does require "Rstat" not available in biowulf
"""
rule multiqc:
input:
expand(join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.bam"),samples=SAMPLES),
expand(join(working_dir, "bismarkAlign/{samples}.bismark_bt2_pe.deduplicated.bam"),samples=SAMPLES),
expand(join(working_dir, "trimGalore/{samples}_val_1.fq.gz"),samples=SAMPLES),
expand(join(working_dir, "trimGalore/{samples}_val_2.fq.gz"),samples=SAMPLES),
output:
join(working_dir, "bismark_summary_report.txt"),
join(working_dir, "bismark_summary_report.html"),
join(working_dir, "multiqc_report.html"),
params:
rname="multiqc",
dir=working_dir,
bis_dir=directory(join(working_dir,"bismarkAlign")),
script_dir=join(working_dir,"scripts"),
resources:
mem = allocated("mem", "multiqc", cluster),
gres = allocated("gres", "multiqc", cluster),
time = allocated("time", "multiqc", cluster),
partition = allocated("partition", "multiqc", cluster),
threads:
int(allocated("threads", "multiqc", cluster)),
shell:
"""
module load multiqc/1.9 bismark
cd {params.bis_dir}
bismark2report
bismark2summary
mv bismark_summary_report.html {params.dir}/
mv bismark_summary_report.txt {params.dir}/
cd {params.dir}
multiqc --ignore '*/.singularity/*' -f --interactive .
"""
###############
# Deconvolution rules begin here
# run following rules in dcv mode only
#################
rule get_CpG:
input:
join(working_dir, "CpG/{samples}/{samples}.bismark_bt2_pe.deduplicated.CpG_report.txt.gz"),
output:
join(working_dir, "CpG_CSV/{samples}.csv"),
params:
rname="get_CpG",
cutoff=5,
script_dir=join(working_dir,"scripts"),
dir1=join(working_dir,"CpG_CSV"),
dir2=join(working_dir,"deconvolution_CSV"),
resources:
mem = allocated("mem", "get_CpG", cluster),
gres = allocated("gres", "get_CpG", cluster),
time = allocated("time", "get_CpG", cluster),
partition = allocated("partition", "get_CpG", cluster),
threads:
int(allocated("threads", "get_CpG", cluster)),
shell:
"""
mkdir -p {params.dir1}
mkdir -p {params.dir2}
module load R
Rscript {params.script_dir}/get_methy.R {input} {wildcards.samples} {params.cutoff} {output}
"""
rule get_overlap_meth:
input:
join(working_dir, "CpG_CSV/{samples}.csv"),
output:
join(working_dir, "deconvolution_CSV/{samples}.csv"),
params:
rname="get_overlap_meth",
map_table=CpG_MAP_TABLE,
resources:
mem = allocated("mem", "get_overlap_meth", cluster),
gres = allocated("gres", "get_overlap_meth", cluster),
time = allocated("time", "get_overlap_meth", cluster),
partition = allocated("partition", "get_overlap_meth", cluster),
threads:
int(allocated("threads", "get_overlap_meth", cluster)),
run:
df_ref=pd.read_csv(params.map_table,sep='\t',header=None)
df_ref.columns=['chromosome','start','end','cgid']
df_ref=df_ref.loc[(df_ref['chromosome'].isin(CHRS)),]
dfm=pd.read_csv(input[0])
dfm=pd.merge(df_ref,dfm,on=['chromosome','start','end'],how='inner')
dfm=dfm.drop(labels=['chromosome','start','end'],axis=1)
dfm=dfm.set_index('cgid')
dfm.to_csv(output[0])
rule run_deconv:
input:
join(working_dir, "deconvolution_CSV/{samples}.csv"),
output:
join(working_dir, "deconvolution_CSV/{samples}_deconv.log"),
params:
script_dir=join(working_dir,"scripts"),
dir=join(working_dir,"deconvolution_CSV"),
rname="run_deconv",
ref=REF_ATLAS,
resources:
mem = allocated("mem", "run_deconv", cluster),
gres = allocated("gres", "run_deconv", cluster),
time = allocated("time", "run_deconv", cluster),
partition = allocated("partition", "run_deconv", cluster),
threads:
int(allocated("threads", "run_deconv", cluster)),
shell:
"""
module load python
cd {params.dir}
python {params.script_dir}/deconvolve2.py --atlas_path {params.ref} --residuals {input} > {output} 2>&1
"""
rule merge_tables:
input:
expand(join(working_dir, "deconvolution_CSV/{samples}.csv"),samples=SAMPLES),
output:
join(working_dir, "deconvolution_CSV/total.csv"),
params:
rname="merge_tables",
resources:
mem = allocated("mem", "merge_tables", cluster),
gres = allocated("gres", "merge_tables", cluster),
time = allocated("time", "merge_tables", cluster),
partition = allocated("partition", "merge_tables", cluster),
threads:
int(allocated("threads", "merge_tables", cluster)),
run:
dfm=pd.read_csv(input[0])
for f in input[1:]:
df=pd.read_csv(f)
dfm=pd.merge(dfm,df,on='cgid',how='outer')
dfm.to_csv(output[0],index=False)
rule run_deconv_merged:
input:
join(working_dir, "deconvolution_CSV/total.csv"),
output:
join(working_dir, "deconvolution_CSV/total_deconv_output.csv"),
join(working_dir, "deconvolution_CSV/total_deconv_plot.png"),
params:
rname="run_deconv_merged",
ref=REF_ATLAS,
dir=join(working_dir,"deconvolution_CSV"),
script_dir=join(working_dir,"scripts"),
resources:
mem = allocated("mem", "run_deconv_merged", cluster),
gres = allocated("gres", "run_deconv_merged", cluster),
time = allocated("time", "run_deconv_merged", cluster),
partition = allocated("partition", "run_deconv_merged", cluster),
threads:
int(allocated("threads", "run_deconv_merged", cluster)),
shell:
"""
module load python
cd {params.dir}
python {params.script_dir}/deconvolve2.py --atlas_path {params.ref} --residuals {input}
"""
###############CFDNAME RULES######################
rule sorting_CpG_bedgraph:
input:
bed=join(working_dir,"CpG/{samples}/{samples}.bismark_bt2_pe.deduplicated.bedGraph.gz"),
output:
bed=temp(join(working_dir,"CpG/{samples}/{samples}.mapped_autosomal_CpG.bedGraph.tmp")),
params:
rname="pl:format1",
resources:
mem = allocated("mem", "sorting_CpG_bedgraph", cluster),
gres = allocated("gres", "sorting_CpG_bedgraph", cluster),
time = allocated("time", "sorting_CpG_bedgraph", cluster),
partition = allocated("partition", "sorting_CpG_bedgraph", cluster),
threads:
int(allocated("threads", "sorting_CpG_bedgraph", cluster)),
shell:
"""
module load bedtools
zcat {input.bed} | tail -n +2 | bedtools sort -i - > {output.bed}
"""
rule liftover_bedgraph:
input:
bed=join(working_dir,"CpG/{samples}/{samples}.mapped_autosomal_CpG.bedGraph.tmp"),
output:
graph=temp(join(working_dir,"CpG/{samples}/{samples}.liftover.bedGraph.tmp")),
params:
rname="pl:format2",
lift_file=HG38TOHG19,
resources:
mem = allocated("mem", "liftover_bedgraph", cluster),
gres = allocated("gres", "liftover_bedgraph", cluster),
time = allocated("time", "liftover_bedgraph", cluster),
partition = allocated("partition", "liftover_bedgraph", cluster),
threads:
int(allocated("threads", "liftover_bedgraph", cluster)),
shell:
"""
module load bedtools crossmap
crossmap bed {params.lift_file} {input.bed} {output.graph}
"""
rule extract_signature_beds:
input:
graph=join(working_dir,"CpG/{samples}/{samples}.liftover.bedGraph.tmp"),
output:
sort=temp(join(working_dir,"CpG/{samples}/{samples}.sorted.bedGraph")),
params:
rname="pl:format3",
markers=GOLD_MARKERS,
resources:
mem = allocated("mem", "extract_signature_beds", cluster),
gres = allocated("gres", "extract_signature_beds", cluster),
time = allocated("time", "extract_signature_beds", cluster),
partition = allocated("partition", "extract_signature_beds", cluster),
threads:
int(allocated("threads", "extract_signature_beds", cluster)),
shell:
"""
module load bedtools
bedtools sort -i {input.graph} | bedtools intersect -wo -a {params.markers} -b stdin -sorted | awk '$6-$5==1 {{print $0}}' | awk 'NF{{NF-=1}};1' > {output.sort}
"""
rule aggregate_over_regions:
input:
sort=join(working_dir,"CpG/{samples}/{samples}.sorted.bedGraph"),
output:
tsv=join(working_dir,"CpG/{samples}/{samples}.cfDNAmeInput.bedGraph"),
params:
rname="pl:format4",
script_dir=join(working_dir,"scripts"),
resources:
mem = allocated("mem", "aggregate_over_regions", cluster),
gres = allocated("gres", "aggregate_over_regions", cluster),
time = allocated("time", "aggregate_over_regions", cluster),
partition = allocated("partition", "aggregate_over_regions", cluster),
threads:
int(allocated("threads", "aggregate_over_regions", cluster)),
shell:
"""
module load R
Rscript {params.script_dir}/aggregate_over_regions.R {input.sort} {output.tsv}
"""
rule cfDNAme:
input:
bed=join(working_dir,"CpG/{samples}/{samples}.cfDNAmeInput.bedGraph"),
output:
tsv=join(working_dir,"CpG/{samples}/{samples}.cfDNAmeDeconvolution.tsv"),
params:
rname="pl:cfDNAme",
script_dir=join(working_dir,"scripts"),
sampleName="{samples}",
reference_markers=REF_MARKERS,
reference_IDs=REF_IDS,
resources:
mem = allocated("mem", "cfDNAme", cluster),
gres = allocated("gres", "cfDNAme", cluster),
time = allocated("time", "cfDNAme", cluster),
partition = allocated("partition", "cfDNAme", cluster),
threads:
int(allocated("threads", "cfDNAme", cluster)),
shell:
"""
module load R
Rscript ${params.script_dir}/tissues_of_origin_v2.R \
{input.bed} \
{params.reference_markers} \
{output.tsv} \
{params.reference_IDs} \
{params.sampleName} \
TRUE \
FALSE \
TRUE \
colon_5/hsc_5/hsc_2/spleen_1/spleen_2/spleen_3/spleen_4/
"""
###############
# Differential methylation rules begin here
# run following rules in dmr mode only
#################
rule bsseq_bismark:
input:
bizfile=join(working_dir,"contrasts.txt"),
output:
bed1=join(dmr_dir, "bsseq/{group}_{chr}_betas_pval.bed"),
params:
rname="bsseq_bismark",
chr='{chr}',
dir=directory(join(dmr_dir, "bsseq")),
cov="2",
sample_prop="0.25",
script_dir=join(working_dir, "dmr","scripts"),
resources:
mem = allocated("mem", "bsseq_bismark", cluster),
gres = allocated("gres", "bsseq_bismark", cluster),
time = allocated("time", "bsseq_bismark", cluster),
partition = allocated("partition", "bsseq_bismark", cluster),
threads:
int(allocated("threads", "bsseq_bismark", cluster)),
shell:
"""
module load R
mkdir -p {params.dir}
Rscript {params.script_dir}/bsseq_lm.R {params.chr} {input.bizfile} {output.bed1} {params.sample_prop} {params.cov}
"""
rule combP:
input:
join(dmr_dir, "bsseq/{group}_{chr}_betas_pval.bed"),
output:
RP=join(dmr_dir, "combP/{group}_{chr}.regions-p.bed.gz"),
homerInput=temp(join(dmr_dir, "homer/{group}_{chr}.homerInput.txt")),
homerOutput=join(dmr_dir, "homer/{group}_{chr}.homerOutput.txt"),
homerOutput2=join(dmr_dir, "homer/{group}_{chr}.homerOutput2.txt"),
homerAnn=temp(join(dmr_dir,"homer/{group}_{chr}.homer.annStats.txt")),