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snakefile
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snakefile
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# ----------------------------------------------------- #
# snakemake pipeline for automatic analysis/QC of mass #
# spectrometry data #
# #
# Author: Michael Jahn #
# Date: 2022-11-24 #
# License: GPL v3 (for all 3rd party tools #
# separate licenses apply) #
# ----------------------------------------------------- #
# import basic packages
from os import path
# check command line arguments
# -----------------------------------------------------
print("\n +++ SNAKEMAKE GLOBAL PARAMETERS +++ \n")
for i in config.keys():
print(f" - {i}: {config.get(i)}")
print("\n")
# function to construct target paths
def out(file):
return os.path.join(config["output"], file)
# target rule
# -----------------------------------------------------
rule all:
input:
out("email/log.txt"),
out("clean_up/log.txt"),
out("versions/log_env.txt"),
out("module_logs/log.txt"),
# module to fetch protein database from NCBI
# -----------------------------------------------------
rule database:
params:
term=config["database"],
output:
path=directory(out("database")),
database=out("database/database.fasta"),
conda:
"envs/database.yml"
log:
path=out("database/log.txt"),
script:
"scripts/prepare_database.py"
# module to generate decoys
# -----------------------------------------------------
rule decoypyrat:
input:
path=rules.database.output.database,
output:
path=out("decoypyrat/decoy_database.fasta"),
conda:
"envs/decoypyrat.yml"
params:
cleavage_sites=config["decoypyrat"]["cleavage_sites"],
decoy_prefix=config["decoypyrat"]["decoy_prefix"],
log:
path=out("decoypyrat/log.txt"),
shell:
"if ! grep -q '>rev_' {input.path};"
"then decoypyrat {input.path} \
-c {params.cleavage_sites} \
-d {params.decoy_prefix} \
-o {output.path} \
-k > {log.path}; fi;"
"cat {input.path} >> {output.path}"
# module to prepare samplesheet
# -----------------------------------------------------
rule samplesheet:
input:
path=config["samplesheet"],
output:
path=out("samplesheet/samplesheet.tsv"),
conda:
"envs/samplesheet.yml"
log:
path=out("samplesheet/log.txt"),
script:
"scripts/prepare_samplesheet.py"
# module to prepare workflow
# -----------------------------------------------------
rule workflow:
input:
samplesheet=rules.samplesheet.output.path,
database=rules.decoypyrat.output.path,
output:
path=out("workflow/workflow.txt"),
conda:
"envs/workflow.yml"
params:
workflow=config["workflow"],
log:
path=out("workflow/log.txt"),
script:
"scripts/prepare_workflow.py"
# module to run fragpipe
# -----------------------------------------------------
rule fragpipe:
input:
fragpipe_bin=config["fragpipe"]["path"],
samplesheet=rules.samplesheet.output.path,
workflow=rules.workflow.output.path,
output:
path=directory(out("fragpipe")),
msstats=out("fragpipe/MSstats.csv"),
conda:
"envs/fragpipe.yml"
params:
dummyParam=0,
log:
path=out("fragpipe/log.txt"),
shell:
"{input.fragpipe_bin}/fragpipe \
--headless \
--workflow {input.workflow} \
--manifest {input.samplesheet} \
--workdir {output.path} \
> {log.path}"
# module to run MSstats
# -----------------------------------------------------
rule msstats:
input:
samplesheet=rules.samplesheet.output.path,
table_msstats=rules.fragpipe.output.msstats,
output:
feature_level_data=out("msstats/feature_level_data.csv"),
protein_level_data=out("msstats/protein_level_data.csv"),
comparison_result=out("msstats/comparison_result.csv"),
model_qc=out("msstats/model_qc.csv"),
uniprot=out("msstats/uniprot.csv"),
conda:
"envs/msstats.yml"
params:
config_msstats=config["msstats"],
log:
path=out("msstats/log.txt"),
script:
"scripts/run_msstats.R"
# module to clean up files after pipeline execution
# -----------------------------------------------------
rule clean_up:
input:
samplesheet=rules.samplesheet.output.path,
msstats=rules.fragpipe.output.msstats,
output:
log=out("clean_up/log.txt"),
params:
pattern="_uncalibrated.mzML",
shell:
"echo 'removed the following files:' >> {output.log};"
"while read -r line;"
"do filename=`echo ${{line}} | cut -f 1 -d ' '`;"
"filename=`echo ${{filename//.raw/{params.pattern}}}`;"
"if test -f ${{filename}}; then rm ${{filename}}; echo ${{filename}} >> {output.log}; fi;"
"done < {input.samplesheet};"
# module to fetch software versions from conda envs
# -----------------------------------------------------
rule versions:
input:
expand(
"envs/{module}.yml",
module=[
"database",
"decoypyrat",
"email",
"fragpipe",
"msstats",
"pdf",
"report",
"samplesheet",
"workflow",
],
),
output:
path=out("versions/log_packages.txt"),
log:
path=out("versions/log_env.txt"),
shell:
"conda env export > {log.path};"
"cat {input} >> {output.path}"
# module to combine all module log files to single log
# -----------------------------------------------------
rule module_logs:
input:
rules.database.log.path,
rules.decoypyrat.log.path,
rules.samplesheet.log.path,
rules.workflow.log.path,
rules.fragpipe.log.path,
rules.msstats.log.path,
rules.clean_up.output.log,
log:
path=out("module_logs/log.txt"),
shell:
"cat {input} >> {log.path}"
# module to generate full HTML report using R markdown
# -----------------------------------------------------
rule report:
input:
feature_level_data=rules.msstats.output.feature_level_data,
protein_level_data=rules.msstats.output.protein_level_data,
comparison_result=rules.msstats.output.comparison_result,
model_qc=rules.msstats.output.model_qc,
versions=rules.versions.output.path,
output:
html=out("report/report.html"),
conda:
"envs/report.yml"
params:
config_report=config["report"],
script:
"notebooks/report.Rmd"
# module to convert HTML to PDF output
# -----------------------------------------------------
rule pdf:
input:
html=rules.report.output.html,
output:
pdf=out("report/report.pdf"),
conda:
"envs/pdf.yml"
log:
path=out("report/log.txt"),
shell:
"weasyprint -v {input.html} {output.pdf} &> {log.path}"
# module to send out emails using custom mail server
# -----------------------------------------------------
rule email:
input:
html=rules.report.output.html,
pdf=rules.pdf.output.pdf,
protein=rules.msstats.output.protein_level_data,
comparison=rules.msstats.output.comparison_result,
output:
log=out("email/log.txt"),
params:
config_email=config["email"],
config_database=config["database"],
config_workflow=config["workflow"],
config_samplesheet=config["samplesheet"],
config_out_dir=config["output"],
script:
"scripts/send_email.py"