def process(sample, pipe_manager, args):
"""
This takes unmapped Bam files and makes trimmed, aligned, duplicate marked
and removed, indexed (and shifted if necessary) Bam files
along with a UCSC browser track.
:param Sample sample: individual Sample object to process
:param pypiper.PipelineManager pipe_manager: PipelineManager to use during
Sample processing
:param argparse.Namespace args: binding between command-line option and
argument, for specifying values various pipeline parameters
"""
print("Start processing ChIP-seq sample %s." % sample.name)
for path in ["sample_root"] + list(sample.paths.__dict__.keys()):
try:
exists = os.path.exists(sample.paths[path])
except TypeError:
continue
if not exists:
try:
os.mkdir(sample.paths[path])
except OSError("Cannot create '%s' path: %s" %
(path, sample.paths[path])):
raise
# Create NGSTk instance
tk = NGSTk(pm=pipe_manager)
# Merge Bam files if more than one technical replicate
if len(sample.input_file_paths) > 1:
pipe_manager.timestamp("Merging bam files from replicates")
cmd = tk.merge_bams(input_bams=sample.input_file_paths,
merged_bam=sample.unmapped)
pipe_manager.run(cmd, sample.unmapped, shell=True)
sample.data_source = sample.unmapped
# Fastqc
pipe_manager.timestamp("Measuring sample quality with Fastqc")
cmd = tk.fastqc(file=sample.data_source,
output_dir=sample.paths.sample_root)
pipe_manager.run(cmd, sample.fastqc_initial_output, shell=False)
# # rename output
if os.path.exists(sample.fastqc_initial_output):
os.rename(sample.fastqc_initial_output, sample.fastqc)
report_dict(pipe_manager, parse_fastqc(sample.fastqc, prefix="fastqc_"))
# Convert bam to fastq
pipe_manager.timestamp("Converting to Fastq format")
cmd = tk.bam2fastq(
input_bam=sample.data_source,
output_fastq=sample.fastq1 if sample.paired else sample.fastq,
output_fastq2=sample.fastq2 if sample.paired else None,
unpaired_fastq=sample.fastq_unpaired if sample.paired else None)
pipe_manager.run(cmd,
sample.fastq1 if sample.paired else sample.fastq,
shell=True)
if not sample.paired:
pipe_manager.clean_add(sample.fastq, conditional=True)
if sample.paired:
pipe_manager.clean_add(sample.fastq1, conditional=True)
pipe_manager.clean_add(sample.fastq2, conditional=True)
pipe_manager.clean_add(sample.fastq_unpaired, conditional=True)
# Trim reads
pipe_manager.timestamp("Trimming adapters from sample")
if pipe_manager.config.parameters.trimmer == "trimmomatic":
cmd = tk.trimmomatic(
input_fastq1=sample.fastq1 if sample.paired else sample.fastq,
input_fastq2=sample.fastq2 if sample.paired else None,
output_fastq1=sample.trimmed1 if sample.paired else sample.trimmed,
output_fastq1_unpaired=sample.trimmed1_unpaired
if sample.paired else None,
output_fastq2=sample.trimmed2 if sample.paired else None,
output_fastq2_unpaired=sample.trimmed2_unpaired
if sample.paired else None,
cpus=args.cores,
adapters=pipe_manager.config.resources.adapters,
log=sample.trimlog)
pipe_manager.run(cmd,
sample.trimmed1 if sample.paired else sample.trimmed,
shell=True)
if not sample.paired:
pipe_manager.clean_add(sample.trimmed, conditional=True)
else:
pipe_manager.clean_add(sample.trimmed1, conditional=True)
pipe_manager.clean_add(sample.trimmed1_unpaired, conditional=True)
pipe_manager.clean_add(sample.trimmed2, conditional=True)
pipe_manager.clean_add(sample.trimmed2_unpaired, conditional=True)
elif pipe_manager.config.parameters.trimmer == "skewer":
cmd = tk.skewer(
input_fastq1=sample.fastq1 if sample.paired else sample.fastq,
input_fastq2=sample.fastq2 if sample.paired else None,
output_prefix=os.path.join(sample.paths.unmapped,
sample.sample_name),
output_fastq1=sample.trimmed1 if sample.paired else sample.trimmed,
output_fastq2=sample.trimmed2 if sample.paired else None,
log=sample.trimlog,
cpus=args.cores,
adapters=pipe_manager.config.resources.adapters)
pipe_manager.run(cmd,
sample.trimmed1 if sample.paired else sample.trimmed,
shell=True)
if not sample.paired:
pipe_manager.clean_add(sample.trimmed, conditional=True)
else:
pipe_manager.clean_add(sample.trimmed1, conditional=True)
pipe_manager.clean_add(sample.trimmed2, conditional=True)
report_dict(
pipe_manager,
parse_trim_stats(sample.trimlog,
prefix="trim_",
paired_end=sample.paired))
# Map
pipe_manager.timestamp("Mapping reads with Bowtie2")
cmd = tk.bowtie2_map(
input_fastq1=sample.trimmed1 if sample.paired else sample.trimmed,
input_fastq2=sample.trimmed2 if sample.paired else None,
output_bam=sample.mapped,
log=sample.aln_rates,
metrics=sample.aln_metrics,
genome_index=getattr(pipe_manager.config.resources.genome_index,
sample.genome),
max_insert=pipe_manager.config.parameters.max_insert,
cpus=args.cores)
pipe_manager.run(cmd, sample.mapped, shell=True)
report_dict(
pipe_manager,
parse_mapping_stats(sample.aln_rates, paired_end=sample.paired))
# Filter reads
pipe_manager.timestamp("Filtering reads for quality")
cmd = tk.filter_reads(input_bam=sample.mapped,
output_bam=sample.filtered,
metrics_file=sample.dups_metrics,
paired=sample.paired,
cpus=args.cores,
Q=pipe_manager.config.parameters.read_quality)
pipe_manager.run(cmd, sample.filtered, shell=True)
report_dict(pipe_manager, parse_duplicate_stats(sample.dups_metrics))
# Index bams
pipe_manager.timestamp("Indexing bamfiles with samtools")
cmd = tk.index_bam(input_bam=sample.mapped)
pipe_manager.run(cmd, sample.mapped + ".bai", shell=True)
cmd = tk.index_bam(input_bam=sample.filtered)
pipe_manager.run(cmd, sample.filtered + ".bai", shell=True)
# Plot fragment distribution
if sample.paired and not os.path.exists(sample.insertplot):
pipe_manager.timestamp("Plotting insert size distribution")
tk.plot_atacseq_insert_sizes(bam=sample.filtered,
plot=sample.insertplot,
output_csv=sample.insertdata)
# Count coverage genome-wide
pipe_manager.timestamp("Calculating genome-wide coverage")
cmd = tk.genome_wide_coverage(
input_bam=sample.filtered,
genome_windows=getattr(pipe_manager.config.resources.genome_windows,
sample.genome),
output=sample.coverage)
pipe_manager.run(cmd, sample.coverage, shell=True)
# Calculate NSC, RSC
pipe_manager.timestamp("Assessing signal/noise in sample")
cmd = tk.run_spp(input_bam=sample.filtered,
output=sample.qc,
plot=sample.qc_plot,
cpus=args.cores)
pipe_manager.run(cmd, sample.qc_plot, shell=True, nofail=True)
report_dict(pipe_manager, parse_nsc_rsc(sample.qc))
# If the sample is a control, we're finished.
# The type/value for the comparison Sample in this case should be either
# absent or a null-indicative/-suggestive value.
comparison = getattr(sample, CHIP_COMPARE_COLUMN, None)
if comparison in [None, "", "NA"]:
pipe_manager.stop_pipeline()
print("Finished processing sample {}".format(sample.name))
return
# The pipeline will now wait for the comparison sample file to be completed
pipe_manager._wait_for_file(
sample.filtered.replace(sample.name, comparison))
# Call peaks.
broad_mode = sample.broad
peaks_folder = sample.paths.peaks
treatment_file = sample.filtered
control_file = sample.filtered.replace(sample.name, comparison)
if not os.path.exists(peaks_folder):
os.makedirs(peaks_folder)
# TODO: include the filepaths as caller-neutral positionals/keyword args
# TODO (cont.) once NGSTK API is tweaked.
peak_call_kwargs = {
"output_dir": peaks_folder,
"broad": broad_mode,
"qvalue": args.qvalue
}
if args.peak_caller == "macs2":
cmd = tk.macs2_call_peaks(treatment_bams=treatment_file,
control_bams=control_file,
sample_name=sample.name,
pvalue=args.pvalue,
genome=sample.genome,
paired=sample.paired,
**peak_call_kwargs)
else:
cmd = tk.spp_call_peaks(treatment_bam=treatment_file,
control_bam=control_file,
treatment_name=sample.name,
control_name=comparison,
cpus=args.cpus,
**peak_call_kwargs)
pipe_manager.run(cmd, target=sample.peaks, shell=True)
report_dict(pipe_manager, parse_peak_number(sample.peaks))
# Do plotting as desired.
if args.peak_caller == "macs2" and not broad_mode:
pipe_manager.timestamp("Plotting MACS2 model")
model_files_base = sample.name + "_model"
# Create the command to run the model script.
name_model_script = model_files_base + ".r"
path_model_script = os.path.join(peaks_folder, name_model_script)
exec_model_script = \
"{} {}".format(pipe_manager.config.tools.Rscript, path_model_script)
# Create the command to create and rename the model plot.
plot_name = model_files_base + ".pdf"
src_plot_path = os.path.join(os.getcwd(), plot_name)
dst_plot_path = os.path.join(peaks_folder, plot_name)
rename_model_plot = "mv {} {}".format(src_plot_path, dst_plot_path)
# Run the model script and rename the model plot.
pipe_manager.run([exec_model_script, rename_model_plot],
target=dst_plot_path,
shell=True,
nofail=True)
# Calculate fraction of reads in peaks (FRiP)
pipe_manager.timestamp("Calculating fraction of reads in peaks (FRiP)")
cmd = tk.calculate_frip(input_bam=sample.filtered,
input_bed=sample.peaks,
output=sample.frip,
cpus=args.cores)
pipe_manager.run(cmd, sample.frip, shell=True)
total = (float(pipe_manager.stats_dict["filtered_single_ends"]) +
(float(pipe_manager.stats_dict["filtered_paired_ends"]) / 2.))
report_dict(pipe_manager, parse_frip(sample.frip, total))
# on an oracle peak list
if hasattr(pipe_manager.config.resources.oracle_peak_regions,
sample.genome):
cmd = calculate_frip(
input_bam=sample.filtered,
input_bed=getattr(
pipe_manager.config.resources.oracle_peak_regions,
sample.genome),
output=sample.oracle_frip,
cpus=args.cores)
pipe_manager.run(cmd, sample.oracle_frip, shell=True)
report_dict(pipe_manager,
parse_frip(sample.oracle_frip, total, prefix="oracle_"))
# Make tracks
track_dir = os.path.dirname(sample.bigwig)
if not os.path.exists(track_dir):
os.makedirs(track_dir)
# right now tracks are only made for bams without duplicates
pipe_manager.timestamp("Making bigWig tracks from BAM file")
cmd = bam_to_bigwig(input_bam=sample.filtered,
output_bigwig=sample.bigwig,
genome=sample.genome,
normalization_method="RPGC")
pipe_manager.run(cmd, sample.bigwig, shell=True)
print("Finished processing sample %s." % sample.name)
pipe_manager.stop_pipeline()
def process(sample, pipe_manager, args):
"""
This takes unmapped Bam files and makes trimmed, aligned, duplicate marked
and removed, indexed (and shifted if necessary) Bam files
along with a UCSC browser track.
"""
print("Start processing ChIP-seq sample '{}'.".format(sample.name))
for path in ["sample_root"] + list(sample.paths.__dict__.keys()):
try:
exists = os.path.exists(sample.paths[path])
except TypeError:
continue
if not exists:
try:
os.mkdir(sample.paths[path])
except OSError("Cannot create '%s' path: %s" %
(path, sample.paths[path])):
raise
# Create NGSTk instance
tk = NGSTk(pm=pipe_manager)
# Merge Bam files if more than one technical replicate
if len(sample.data_path.split(" ")) > 1:
pipe_manager.timestamp("Merging bam files from replicates")
cmd = tk.merge_bams(
input_bams=sample.data_path.split(
" "), # this is a list of sample paths
merged_bam=sample.unmapped)
pipe_manager.run(cmd, sample.unmapped, shell=True)
sample.data_path = sample.unmapped
# Fastqc
pipe_manager.timestamp("Measuring sample quality with Fastqc")
cmd = tk.fastqc_rename(input_bam=sample.data_path,
output_dir=sample.paths.sample_root,
sample_name=sample.sample_name)
pipe_manager.run(cmd,
os.path.join(sample.paths.sample_root,
sample.sample_name + "_fastqc.zip"),
shell=True)
report_dict(
pipe_manager,
parse_fastqc(os.path.join(sample.paths.sample_root,
sample.sample_name + "_fastqc.zip"),
prefix="fastqc_"))
# Convert bam to fastq
pipe_manager.timestamp("Converting to Fastq format")
cmd = tk.bam2fastq(
inputBam=sample.data_path,
outputFastq=sample.fastq1 if sample.paired else sample.fastq,
outputFastq2=sample.fastq2 if sample.paired else None,
unpairedFastq=sample.fastq_unpaired if sample.paired else None)
pipe_manager.run(cmd,
sample.fastq1 if sample.paired else sample.fastq,
shell=True)
if not sample.paired:
pipe_manager.clean_add(sample.fastq, conditional=True)
if sample.paired:
pipe_manager.clean_add(sample.fastq1, conditional=True)
pipe_manager.clean_add(sample.fastq2, conditional=True)
pipe_manager.clean_add(sample.fastq_unpaired, conditional=True)
# Trim reads
pipe_manager.timestamp("Trimming adapters from sample")
if pipe_manager.config.parameters.trimmer == "trimmomatic":
cmd = tk.trimmomatic(
inputFastq1=sample.fastq1 if sample.paired else sample.fastq,
inputFastq2=sample.fastq2 if sample.paired else None,
outputFastq1=sample.trimmed1 if sample.paired else sample.trimmed,
outputFastq1unpaired=sample.trimmed1_unpaired
if sample.paired else None,
outputFastq2=sample.trimmed2 if sample.paired else None,
outputFastq2unpaired=sample.trimmed2_unpaired
if sample.paired else None,
cpus=args.cores,
adapters=pipe_manager.config.resources.adapters,
log=sample.trimlog)
pipe_manager.run(cmd,
sample.trimmed1 if sample.paired else sample.trimmed,
shell=True)
if not sample.paired:
pipe_manager.clean_add(sample.trimmed, conditional=True)
else:
pipe_manager.clean_add(sample.trimmed1, conditional=True)
pipe_manager.clean_add(sample.trimmed1_unpaired, conditional=True)
pipe_manager.clean_add(sample.trimmed2, conditional=True)
pipe_manager.clean_add(sample.trimmed2_unpaired, conditional=True)
elif pipe_manager.config.parameters.trimmer == "skewer":
cmd = tk.skewer(
inputFastq1=sample.fastq1 if sample.paired else sample.fastq,
inputFastq2=sample.fastq2 if sample.paired else None,
outputPrefix=os.path.join(sample.paths.unmapped,
sample.sample_name),
outputFastq1=sample.trimmed1 if sample.paired else sample.trimmed,
outputFastq2=sample.trimmed2 if sample.paired else None,
trimLog=sample.trimlog,
cpus=args.cores,
adapters=pipe_manager.config.resources.adapters)
pipe_manager.run(cmd,
sample.trimmed1 if sample.paired else sample.trimmed,
shell=True)
if not sample.paired:
pipe_manager.clean_add(sample.trimmed, conditional=True)
else:
pipe_manager.clean_add(sample.trimmed1, conditional=True)
pipe_manager.clean_add(sample.trimmed2, conditional=True)
report_dict(
pipe_manager,
parse_trim_stats(sample.trimlog,
prefix="trim_",
paired_end=sample.paired))
# Map
pipe_manager.timestamp("Mapping reads with Bowtie2")
cmd = tk.bowtie2Map(
inputFastq1=sample.trimmed1 if sample.paired else sample.trimmed,
inputFastq2=sample.trimmed2 if sample.paired else None,
outputBam=sample.mapped,
log=sample.aln_rates,
metrics=sample.aln_metrics,
genomeIndex=getattr(pipe_manager.config.resources.genomes,
sample.genome),
maxInsert=pipe_manager.config.parameters.max_insert,
cpus=args.cores)
pipe_manager.run(cmd, sample.mapped, shell=True)
report_dict(
pipe_manager,
parse_mapping_stats(sample.aln_rates, paired_end=sample.paired))
# Filter reads
pipe_manager.timestamp("Filtering reads for quality")
cmd = tk.filterReads(inputBam=sample.mapped,
outputBam=sample.filtered,
metricsFile=sample.dups_metrics,
paired=sample.paired,
cpus=args.cores,
Q=pipe_manager.config.parameters.read_quality)
pipe_manager.run(cmd, sample.filtered, shell=True)
report_dict(pipe_manager, parse_duplicate_stats(sample.dups_metrics))
# Index bams
pipe_manager.timestamp("Indexing bamfiles with samtools")
cmd = tk.indexBam(inputBam=sample.mapped)
pipe_manager.run(cmd, sample.mapped + ".bai", shell=True)
cmd = tk.indexBam(inputBam=sample.filtered)
pipe_manager.run(cmd, sample.filtered + ".bai", shell=True)
track_dir = os.path.dirname(sample.bigwig)
if not os.path.exists(track_dir):
os.makedirs(track_dir)
# Make tracks
# right now tracks are only made for bams without duplicates
pipe_manager.timestamp("Making bigWig tracks from bam file")
cmd = bamToBigWig(
inputBam=sample.filtered,
outputBigWig=sample.bigwig,
genomeSizes=getattr(pipe_manager.config.resources.chromosome_sizes,
sample.genome),
genome=sample.genome,
tagmented=pipe_manager.config.parameters.
tagmented, # by default make extended tracks
normalize=pipe_manager.config.parameters.normalize_tracks,
norm_factor=pipe_manager.config.parameters.norm_factor)
pipe_manager.run(cmd, sample.bigwig, shell=True)
# Plot fragment distribution
if sample.paired and not os.path.exists(sample.insertplot):
pipe_manager.timestamp("Plotting insert size distribution")
tk.plot_atacseq_insert_sizes(bam=sample.filtered,
plot=sample.insertplot,
output_csv=sample.insertdata)
pipe_manager.report_figure("insert_sizes", sample.insertplot)
# Count coverage genome-wide
pipe_manager.timestamp("Calculating genome-wide coverage")
cmd = tk.genomeWideCoverage(
inputBam=sample.filtered,
genomeWindows=getattr(pipe_manager.config.resources.genome_windows,
sample.genome),
output=sample.coverage)
pipe_manager.run(cmd, sample.coverage, shell=True)
# Calculate NSC, RSC
pipe_manager.timestamp("Assessing signal/noise in sample")
cmd = tk.peakTools(inputBam=sample.filtered,
output=sample.qc,
plot=sample.qc_plot,
cpus=args.cores)
pipe_manager.run(cmd, sample.qc_plot, shell=True, nofail=True)
report_dict(pipe_manager, parse_nsc_rsc(sample.qc))
pipe_manager.report_figure("cross_correlation", sample.qc_plot)
print("Finished processing sample '{}'.".format(sample.name))
return pipe_manager
def process(sample, pipe_manager, args):
"""
This takes unmapped Bam files and makes trimmed, aligned, duplicate marked
and removed, indexed, shifted Bam files along with a UCSC browser track.
Peaks are called and filtered.
"""
print("Start processing ATAC-seq sample %s." % sample.sample_name)
for path in ["sample_root"] + sample.paths.__dict__.keys():
try:
exists = os.path.exists(sample.paths[path])
except TypeError:
continue
if not exists:
try:
os.mkdir(sample.paths[path])
except OSError("Cannot create '%s' path: %s" %
(path, sample.paths[path])):
raise
# Create NGSTk instance
tk = NGSTk(pm=pipe_manager)
# Merge Bam files if more than one technical replicate
if len(sample.data_path.split(" ")) > 1:
pipe_manager.timestamp("Merging bam files from replicates")
cmd = tk.merge_bams(
input_bams=sample.data_path.split(
" "), # this is a list of sample paths
merged_bam=sample.unmapped)
pipe_manager.run(cmd, sample.unmapped, shell=True)
sample.data_path = sample.unmapped
# Fastqc
pipe_manager.timestamp("Measuring sample quality with Fastqc")
cmd = tk.fastqc_rename(input_bam=sample.data_path,
output_dir=sample.paths.sample_root,
sample_name=sample.sample_name)
pipe_manager.run(cmd,
os.path.join(sample.paths.sample_root,
sample.sample_name + "_fastqc.zip"),
shell=True)
report_dict(
pipe_manager,
parse_fastqc(os.path.join(sample.paths.sample_root,
sample.sample_name + "_fastqc.zip"),
prefix="fastqc_"))
# Convert bam to fastq
pipe_manager.timestamp("Converting to Fastq format")
cmd = tk.bam2fastq(
inputBam=sample.data_path,
outputFastq=sample.fastq1 if sample.paired else sample.fastq,
outputFastq2=sample.fastq2 if sample.paired else None,
unpairedFastq=sample.fastq_unpaired if sample.paired else None)
pipe_manager.run(cmd,
sample.fastq1 if sample.paired else sample.fastq,
shell=True)
if not sample.paired:
pipe_manager.clean_add(sample.fastq, conditional=True)
if sample.paired:
pipe_manager.clean_add(sample.fastq1, conditional=True)
pipe_manager.clean_add(sample.fastq2, conditional=True)
pipe_manager.clean_add(sample.fastq_unpaired, conditional=True)
# Trim reads
pipe_manager.timestamp("Trimming adapters from sample")
if pipe_manager.config.parameters.trimmer == "trimmomatic":
cmd = tk.trimmomatic(
inputFastq1=sample.fastq1 if sample.paired else sample.fastq,
inputFastq2=sample.fastq2 if sample.paired else None,
outputFastq1=sample.trimmed1 if sample.paired else sample.trimmed,
outputFastq1unpaired=sample.trimmed1_unpaired
if sample.paired else None,
outputFastq2=sample.trimmed2 if sample.paired else None,
outputFastq2unpaired=sample.trimmed2_unpaired
if sample.paired else None,
cpus=args.cores,
adapters=pipe_manager.config.resources.adapters,
log=sample.trimlog)
pipe_manager.run(cmd,
sample.trimmed1 if sample.paired else sample.trimmed,
shell=True)
if not sample.paired:
pipe_manager.clean_add(sample.trimmed, conditional=True)
else:
pipe_manager.clean_add(sample.trimmed1, conditional=True)
pipe_manager.clean_add(sample.trimmed1_unpaired, conditional=True)
pipe_manager.clean_add(sample.trimmed2, conditional=True)
pipe_manager.clean_add(sample.trimmed2_unpaired, conditional=True)
elif pipe_manager.config.parameters.trimmer == "skewer":
cmd = tk.skewer(
inputFastq1=sample.fastq1 if sample.paired else sample.fastq,
inputFastq2=sample.fastq2 if sample.paired else None,
outputPrefix=os.path.join(sample.paths.unmapped,
sample.sample_name),
outputFastq1=sample.trimmed1 if sample.paired else sample.trimmed,
outputFastq2=sample.trimmed2 if sample.paired else None,
trimLog=sample.trimlog,
cpus=args.cores,
adapters=pipe_manager.config.resources.adapters)
pipe_manager.run(cmd,
sample.trimmed1 if sample.paired else sample.trimmed,
shell=True)
if not sample.paired:
pipe_manager.clean_add(sample.trimmed, conditional=True)
else:
pipe_manager.clean_add(sample.trimmed1, conditional=True)
pipe_manager.clean_add(sample.trimmed2, conditional=True)
report_dict(
pipe_manager,
parse_trim_stats(sample.trimlog,
prefix="trim_",
paired_end=sample.paired))
# Map
pipe_manager.timestamp("Mapping reads with Bowtie2")
cmd = tk.bowtie2Map(
inputFastq1=sample.trimmed1 if sample.paired else sample.trimmed,
inputFastq2=sample.trimmed2 if sample.paired else None,
outputBam=sample.mapped,
log=sample.aln_rates,
metrics=sample.aln_metrics,
genomeIndex=getattr(pipe_manager.config.resources.genomes,
sample.genome),
maxInsert=pipe_manager.config.parameters.max_insert,
cpus=args.cores)
pipe_manager.run(cmd, sample.mapped, shell=True)
report_dict(
pipe_manager,
parse_mapping_stats(sample.aln_rates, paired_end=sample.paired))
# Get mitochondrial reads
pipe_manager.timestamp("Getting mitochondrial stats")
cmd = tk.get_mitochondrial_reads(bam_file=sample.mapped,
output=sample.mitochondrial_stats,
cpus=args.cores)
pipe_manager.run(cmd, sample.mitochondrial_stats, shell=True, nofail=True)
report_dict(
pipe_manager,
parse_duplicate_stats(sample.mitochondrial_stats, prefix="MT_"))
# Filter reads
pipe_manager.timestamp("Filtering reads for quality")
cmd = tk.filterReads(inputBam=sample.mapped,
outputBam=sample.filtered,
metricsFile=sample.dups_metrics,
paired=sample.paired,
cpus=args.cores,
Q=pipe_manager.config.parameters.read_quality)
pipe_manager.run(cmd, sample.filtered, shell=True)
report_dict(pipe_manager, parse_duplicate_stats(sample.dups_metrics))
# Shift reads
if sample.tagmented:
pipe_manager.timestamp("Shifting reads of tagmented sample")
cmd = tk.shiftReads(inputBam=sample.filtered,
genome=sample.genome,
outputBam=sample.filteredshifted)
pipe_manager.run(cmd, sample.filteredshifted, shell=True)
# Index bams
pipe_manager.timestamp("Indexing bamfiles with samtools")
cmd = tk.indexBam(inputBam=sample.mapped)
pipe_manager.run(cmd, sample.mapped + ".bai", shell=True)
cmd = tk.indexBam(inputBam=sample.filtered)
pipe_manager.run(cmd, sample.filtered + ".bai", shell=True)
if sample.tagmented:
cmd = tk.indexBam(inputBam=sample.filteredshifted)
pipe_manager.run(cmd, sample.filteredshifted + ".bai", shell=True)
track_dir = os.path.dirname(sample.bigwig)
if not os.path.exists(track_dir):
os.makedirs(track_dir)
# Make tracks
# right now tracks are only made for bams without duplicates
pipe_manager.timestamp("Making bigWig tracks from bam file")
cmd = bamToBigWig(
inputBam=sample.filtered,
outputBigWig=sample.bigwig,
genomeSizes=getattr(pipe_manager.config.resources.chromosome_sizes,
sample.genome),
genome=sample.genome,
tagmented=pipe_manager.config.parameters.
tagmented, # by default make extended tracks
normalize=pipe_manager.config.parameters.normalize_tracks,
norm_factor=pipe_manager.config.parameters.norm_factor)
pipe_manager.run(cmd, sample.bigwig, shell=True)
# Plot fragment distribution
if sample.paired and not os.path.exists(sample.insertplot):
pipe_manager.timestamp("Plotting insert size distribution")
tk.plot_atacseq_insert_sizes(bam=sample.filtered,
plot=sample.insertplot,
output_csv=sample.insertdata)
pipe_manager.report_figure("insert_sizes", sample.insertplot)
# Count coverage genome-wide
pipe_manager.timestamp("Calculating genome-wide coverage")
cmd = tk.genomeWideCoverage(
inputBam=sample.filtered,
genomeWindows=getattr(pipe_manager.config.resources.genome_windows,
sample.genome),
output=sample.coverage)
pipe_manager.run(cmd, sample.coverage, shell=True)
# Calculate NSC, RSC
pipe_manager.timestamp("Assessing signal/noise in sample")
cmd = tk.peakTools(inputBam=sample.filtered,
output=sample.qc,
plot=sample.qc_plot,
cpus=args.cores)
pipe_manager.run(cmd, sample.qc_plot, shell=True, nofail=True)
report_dict(pipe_manager, parse_nsc_rsc(sample.qc))
pipe_manager.report_figure("cross_correlation", sample.qc_plot)
# Call peaks
pipe_manager.timestamp("Calling peaks with MACS2")
# make dir for output (macs fails if it does not exist)
if not os.path.exists(sample.paths.peaks):
os.makedirs(sample.paths.peaks)
cmd = tk.macs2CallPeaksATACSeq(treatmentBam=sample.filtered,
outputDir=sample.paths.peaks,
sampleName=sample.sample_name,
genome=sample.genome)
pipe_manager.run(cmd, sample.peaks, shell=True)
report_dict(pipe_manager, parse_peak_number(sample.peaks))
# Filter peaks
if hasattr(pipe_manager.config.resources.blacklisted_regions,
sample.genome):
pipe_manager.timestamp("Filtering peaks from blacklisted regions")
cmd = filter_peaks(
peaks=sample.peaks,
exclude=getattr(pipe_manager.config.resources.blacklisted_regions,
sample.genome),
filtered_peaks=sample.filtered_peaks)
pipe_manager.run(cmd, sample.filtered_peaks, shell=True)
report_dict(
pipe_manager,
parse_peak_number(sample.filtered_peaks, prefix="filtered_"))
# Calculate fraction of reads in peaks (FRiP)
pipe_manager.timestamp("Calculating fraction of reads in peaks (FRiP)")
# on the sample's peaks
cmd = tk.calculate_FRiP(inputBam=sample.filtered,
inputBed=sample.peaks,
output=sample.frip,
cpus=args.cores)
pipe_manager.run(cmd, sample.frip, shell=True)
total = (float(pipe_manager.stats_dict["filtered_single_ends"]) +
(float(pipe_manager.stats_dict["filtered_paired_ends"]) / 2.))
report_dict(pipe_manager, parse_FRiP(sample.frip, total))
# on an oracle peak list
if hasattr(pipe_manager.config.resources.oracle_peak_regions,
sample.genome):
cmd = tk.calculate_FRiP(
inputBam=sample.filtered,
inputBed=getattr(pipe_manager.config.resources.oracle_peak_regions,
sample.genome),
output=sample.oracle_frip,
cpus=args.cores)
pipe_manager.run(cmd, sample.oracle_frip, shell=True)
report_dict(pipe_manager,
parse_FRiP(sample.oracle_frip, total, prefix="oracle_"))
# Finish up
print(pipe_manager.stats_dict)
pipe_manager.stop_pipeline()
print("Finished processing sample %s." % sample.sample_name)
def process(sample, pipe_manager, args):
"""
This takes unmapped Bam files and makes trimmed, aligned, duplicate marked
and removed, indexed (and shifted if necessary) Bam files
along with a UCSC browser track.
"""
print("Start processing ChIP-seq sample '{}'.".format(sample.name))
for path in ["sample_root"] + list(sample.paths.__dict__.keys()):
try:
exists = os.path.exists(sample.paths[path])
except TypeError:
continue
if not exists:
try:
os.mkdir(sample.paths[path])
except OSError("Cannot create '%s' path: %s" % (path, sample.paths[path])):
raise
# Create NGSTk instance
tk = NGSTk(pm=pipe_manager)
# Merge Bam files if more than one technical replicate
if len(sample.data_path.split(" ")) > 1:
pipe_manager.timestamp("Merging bam files from replicates")
cmd = tk.merge_bams(
input_bams=sample.data_path.split(" "), # this is a list of sample paths
merged_bam=sample.unmapped
)
pipe_manager.run(cmd, sample.unmapped, shell=True)
sample.data_path = sample.unmapped
# Fastqc
pipe_manager.timestamp("Measuring sample quality with Fastqc")
cmd = tk.fastqc_rename(
input_bam=sample.data_path,
output_dir=sample.paths.sample_root,
sample_name=sample.sample_name
)
pipe_manager.run(cmd, os.path.join(sample.paths.sample_root, sample.sample_name + "_fastqc.zip"), shell=True)
report_dict(pipe_manager, parse_fastqc(os.path.join(sample.paths.sample_root, sample.sample_name + "_fastqc.zip"), prefix="fastqc_"))
# Convert bam to fastq
pipe_manager.timestamp("Converting to Fastq format")
cmd = tk.bam2fastq(
inputBam=sample.data_path,
outputFastq=sample.fastq1 if sample.paired else sample.fastq,
outputFastq2=sample.fastq2 if sample.paired else None,
unpairedFastq=sample.fastq_unpaired if sample.paired else None
)
pipe_manager.run(cmd, sample.fastq1 if sample.paired else sample.fastq, shell=True)
if not sample.paired:
pipe_manager.clean_add(sample.fastq, conditional=True)
if sample.paired:
pipe_manager.clean_add(sample.fastq1, conditional=True)
pipe_manager.clean_add(sample.fastq2, conditional=True)
pipe_manager.clean_add(sample.fastq_unpaired, conditional=True)
# Trim reads
pipe_manager.timestamp("Trimming adapters from sample")
if pipe_manager.config.parameters.trimmer == "trimmomatic":
cmd = tk.trimmomatic(
inputFastq1=sample.fastq1 if sample.paired else sample.fastq,
inputFastq2=sample.fastq2 if sample.paired else None,
outputFastq1=sample.trimmed1 if sample.paired else sample.trimmed,
outputFastq1unpaired=sample.trimmed1_unpaired if sample.paired else None,
outputFastq2=sample.trimmed2 if sample.paired else None,
outputFastq2unpaired=sample.trimmed2_unpaired if sample.paired else None,
cpus=args.cores,
adapters=pipe_manager.config.resources.adapters,
log=sample.trimlog
)
pipe_manager.run(cmd, sample.trimmed1 if sample.paired else sample.trimmed, shell=True)
if not sample.paired:
pipe_manager.clean_add(sample.trimmed, conditional=True)
else:
pipe_manager.clean_add(sample.trimmed1, conditional=True)
pipe_manager.clean_add(sample.trimmed1_unpaired, conditional=True)
pipe_manager.clean_add(sample.trimmed2, conditional=True)
pipe_manager.clean_add(sample.trimmed2_unpaired, conditional=True)
elif pipe_manager.config.parameters.trimmer == "skewer":
cmd = tk.skewer(
inputFastq1=sample.fastq1 if sample.paired else sample.fastq,
inputFastq2=sample.fastq2 if sample.paired else None,
outputPrefix=os.path.join(sample.paths.unmapped, sample.sample_name),
outputFastq1=sample.trimmed1 if sample.paired else sample.trimmed,
outputFastq2=sample.trimmed2 if sample.paired else None,
trimLog=sample.trimlog,
cpus=args.cores,
adapters=pipe_manager.config.resources.adapters
)
pipe_manager.run(cmd, sample.trimmed1 if sample.paired else sample.trimmed, shell=True)
if not sample.paired:
pipe_manager.clean_add(sample.trimmed, conditional=True)
else:
pipe_manager.clean_add(sample.trimmed1, conditional=True)
pipe_manager.clean_add(sample.trimmed2, conditional=True)
report_dict(pipe_manager, parse_trim_stats(sample.trimlog, prefix="trim_", paired_end=sample.paired))
# Map
pipe_manager.timestamp("Mapping reads with Bowtie2")
cmd = tk.bowtie2Map(
inputFastq1=sample.trimmed1 if sample.paired else sample.trimmed,
inputFastq2=sample.trimmed2 if sample.paired else None,
outputBam=sample.mapped,
log=sample.aln_rates,
metrics=sample.aln_metrics,
genomeIndex=getattr(pipe_manager.config.resources.genome_index, sample.genome),
maxInsert=pipe_manager.config.parameters.max_insert,
cpus=args.cores
)
pipe_manager.run(cmd, sample.mapped, shell=True)
report_dict(pipe_manager, parse_mapping_stats(sample.aln_rates, paired_end=sample.paired))
# Filter reads
pipe_manager.timestamp("Filtering reads for quality")
cmd = tk.filterReads(
inputBam=sample.mapped,
outputBam=sample.filtered,
metricsFile=sample.dups_metrics,
paired=sample.paired,
cpus=args.cores,
Q=pipe_manager.config.parameters.read_quality
)
pipe_manager.run(cmd, sample.filtered, shell=True)
report_dict(pipe_manager, parse_duplicate_stats(sample.dups_metrics))
# Index bams
pipe_manager.timestamp("Indexing bamfiles with samtools")
cmd = tk.indexBam(inputBam=sample.mapped)
pipe_manager.run(cmd, sample.mapped + ".bai", shell=True)
cmd = tk.indexBam(inputBam=sample.filtered)
pipe_manager.run(cmd, sample.filtered + ".bai", shell=True)
track_dir = os.path.dirname(sample.bigwig)
if not os.path.exists(track_dir):
os.makedirs(track_dir)
# Report total efficiency
usable = (
float(pipe_manager.stats_dict["filtered_single_ends"]) +
(float(pipe_manager.stats_dict["filtered_paired_ends"]) / 2.))
total = float(pipe_manager.stats_dict['fastqc_total_pass_filter_reads'])
report_dict(
pipe_manager,
{"total_efficiency": (usable / total) * 100})
# Make tracks
track_dir = os.path.dirname(sample.bigwig)
if not os.path.exists(track_dir):
os.makedirs(track_dir)
# right now tracks are only made for bams without duplicates
pipe_manager.timestamp("Making bigWig tracks from BAM file")
cmd = bam_to_bigwig(
input_bam=sample.filtered,
output_bigwig=sample.bigwig,
genome=sample.genome,
normalization_method="RPGC")
pipe_manager.run(cmd, sample.bigwig, shell=True)
# Plot fragment distribution
if sample.paired and not os.path.exists(sample.insertplot):
pipe_manager.timestamp("Plotting insert size distribution")
tk.plot_atacseq_insert_sizes(
bam=sample.filtered,
plot=sample.insertplot,
output_csv=sample.insertdata
)
pipe_manager.report_figure("insert_sizes", sample.insertplot)
# Count coverage genome-wide
pipe_manager.timestamp("Calculating genome-wide coverage")
cmd = tk.genomeWideCoverage(
inputBam=sample.filtered,
genomeWindows=getattr(pipe_manager.config.resources.genome_windows, sample.genome),
output=sample.coverage
)
pipe_manager.run(cmd, sample.coverage, shell=True)
# Calculate NSC, RSC
pipe_manager.timestamp("Assessing signal/noise in sample")
cmd = tk.peakTools(
inputBam=sample.filtered,
output=sample.qc,
plot=sample.qc_plot,
cpus=args.cores
)
pipe_manager.run(cmd, sample.qc_plot, shell=True, nofail=True)
report_dict(pipe_manager, parse_nsc_rsc(sample.qc))
pipe_manager.report_figure("cross_correlation", sample.qc_plot)
print("Finished processing sample '{}'.".format(sample.name))
return pipe_manager
def process(sample, pipe_manager, args):
"""
This takes unmapped Bam files and makes trimmed, aligned, duplicate marked
and removed, indexed, shifted Bam files along with a UCSC browser track.
Peaks are called and filtered.
"""
print("Start processing ATAC-seq sample %s." % sample.sample_name)
# for path in ["sample_root"] + list(sample.__dict__.keys()):
for path in [
"sample_root",
"unmapped_dir",
"mapped_dir",
"peaks_dir",
"coverage_dir",
"tss_dir",
]:
p = getattr(sample, path)
try:
exists = os.path.exists(p)
except TypeError:
continue
if not exists:
msg = "Cannot create '{}' path: {}".format(path, p)
try:
os.mkdir(p)
except OSError(msg):
raise
# Create NGSTk instance
tk = NGSTk(pm=pipe_manager)
# Merge Bam files if more than one technical replicate
# if len(sample.data_source.split(" ")) > 1:
if (type(sample.data_source) == list) & (len(sample.data_source) > 1):
pipe_manager.timestamp("Merging bam files from replicates")
cmd = tk.merge_bams(
input_bams=sample.data_source, # this is a list of sample paths
merged_bam=sample.unmapped,
)
pipe_manager.run(cmd, sample.unmapped, shell=True)
sample.data_source = sample.unmapped
# Fastqc
pipe_manager.timestamp("Measuring sample quality with Fastqc")
if not os.path.exists(sample.fastqc):
cmd = tk.fastqc(file=sample.data_source, output_dir=sample.sample_root)
pipe_manager.run(cmd, sample.fastqc_initial_output, shell=False)
# # rename output
if os.path.exists(sample.fastqc_initial_output):
os.rename(sample.fastqc_initial_output, sample.fastqc)
report_dict(pipe_manager, parse_fastqc(sample.fastqc, prefix="fastqc_"))
# Convert bam to fastq
pipe_manager.timestamp("Converting to Fastq format")
cmd = tk.bam2fastq(
input_bam=sample.data_source,
output_fastq=sample.fastq1 if sample.paired else sample.fastq,
output_fastq2=sample.fastq2 if sample.paired else None,
unpaired_fastq=sample.fastq_unpaired if sample.paired else None,
)
pipe_manager.run(cmd,
sample.fastq1 if sample.paired else sample.fastq,
shell=True)
if not sample.paired:
pipe_manager.clean_add(sample.fastq, conditional=True)
if sample.paired:
pipe_manager.clean_add(sample.fastq1, conditional=True)
pipe_manager.clean_add(sample.fastq2, conditional=True)
pipe_manager.clean_add(sample.fastq_unpaired, conditional=True)
# Trim reads
pipe_manager.timestamp("Trimming adapters from sample")
if pipe_manager.config.parameters.trimmer == "trimmomatic":
cmd = tk.trimmomatic(
input_fastq1=sample.fastq1 if sample.paired else sample.fastq,
input_fastq2=sample.fastq2 if sample.paired else None,
output_fastq1=sample.trimmed1 if sample.paired else sample.trimmed,
output_fastq1_unpaired=sample.trimmed1_unpaired
if sample.paired else None,
output_fastq2=sample.trimmed2 if sample.paired else None,
output_fastq2_unpaired=sample.trimmed2_unpaired
if sample.paired else None,
cpus=args.cores,
adapters=pipe_manager.config.resources.adapters,
log=sample.trimlog,
)
pipe_manager.run(
cmd,
sample.trimmed1 if sample.paired else sample.trimmed,
shell=True,
)
if not sample.paired:
pipe_manager.clean_add(sample.trimmed, conditional=True)
else:
pipe_manager.clean_add(sample.trimmed1, conditional=True)
pipe_manager.clean_add(sample.trimmed1_unpaired, conditional=True)
pipe_manager.clean_add(sample.trimmed2, conditional=True)
pipe_manager.clean_add(sample.trimmed2_unpaired, conditional=True)
elif pipe_manager.config.parameters.trimmer == "skewer":
cmd = tk.skewer(
input_fastq1=sample.fastq1 if sample.paired else sample.fastq,
input_fastq2=sample.fastq2 if sample.paired else None,
output_prefix=pjoin(sample.unmapped_dir, sample.sample_name),
output_fastq1=sample.trimmed1 if sample.paired else sample.trimmed,
output_fastq2=sample.trimmed2 if sample.paired else None,
log=sample.trimlog,
cpus=args.cores,
adapters=pipe_manager.config.resources.adapters,
)
pipe_manager.run(
cmd,
sample.trimmed1 if sample.paired else sample.trimmed,
shell=True,
)
if not sample.paired:
pipe_manager.clean_add(sample.trimmed, conditional=True)
else:
pipe_manager.clean_add(sample.trimmed1, conditional=True)
pipe_manager.clean_add(sample.trimmed2, conditional=True)
report_dict(
pipe_manager,
parse_trim_stats(sample.trimlog,
prefix="trim_",
paired_end=sample.paired),
)
# Map
pipe_manager.timestamp("Mapping reads with Bowtie2")
cmd = tk.bowtie2_map(
input_fastq1=sample.trimmed1 if sample.paired else sample.trimmed,
input_fastq2=sample.trimmed2 if sample.paired else None,
output_bam=sample.mapped,
log=sample.aln_rates,
metrics=sample.aln_metrics,
genome_index=getattr(pipe_manager.config.resources.genome_index,
sample.genome),
max_insert=pipe_manager.config.parameters.max_insert,
cpus=args.cores,
)
pipe_manager.run(cmd, sample.mapped, shell=True)
report_dict(
pipe_manager,
parse_mapping_stats(sample.aln_rates, paired_end=sample.paired),
)
# Get mitochondrial reads
pipe_manager.timestamp("Getting mitochondrial stats")
cmd = tk.get_mitochondrial_reads(
bam_file=sample.mapped,
output=sample.mitochondrial_stats,
cpus=args.cores,
)
pipe_manager.run(cmd, sample.mitochondrial_stats, shell=True, nofail=True)
report_dict(
pipe_manager,
parse_duplicate_stats(sample.mitochondrial_stats, prefix="MT_"),
)
# Filter reads
pipe_manager.timestamp("Filtering reads for quality")
cmd = tk.filter_reads(
input_bam=sample.mapped,
output_bam=sample.filtered,
metrics_file=sample.dups_metrics,
paired=sample.paired,
cpus=args.cores,
Q=pipe_manager.config.parameters.read_quality,
)
pipe_manager.run(cmd, sample.filtered, shell=True)
report_dict(pipe_manager, parse_duplicate_stats(sample.dups_metrics))
# Index bams
pipe_manager.timestamp("Indexing bamfiles with samtools")
cmd = tk.index_bam(input_bam=sample.mapped)
pipe_manager.run(cmd, sample.mapped + ".bai", shell=True)
cmd = tk.index_bam(input_bam=sample.filtered)
pipe_manager.run(cmd, sample.filtered + ".bai", shell=True)
# Shift reads
if args.shift_reads:
pipe_manager.timestamp("Shifting reads of tagmented sample")
cmd = tk.shift_reads(
input_bam=sample.filtered,
genome=sample.genome,
output_bam=sample.filteredshifted,
)
pipe_manager.run(cmd, sample.filteredshifted, shell=True)
cmd = tk.index_bam(input_bam=sample.filteredshifted)
pipe_manager.run(cmd, sample.filteredshifted + ".bai", shell=True)
# Run TSS enrichment
tss_enrichment = run_tss_analysis(
sample=sample,
bam_file=sample.filtered,
chrom_file=getattr(pipe_manager.config.resources.chromosome_sizes,
sample.genome),
tss_file=getattr(pipe_manager.config.resources.unique_tss,
sample.genome),
)
report_dict(pipe_manager, {"tss_enrichment": tss_enrichment})
# Call peaks
pipe_manager.timestamp("Calling peaks with MACS2")
# make dir for output (macs fails if it does not exist)
if not os.path.exists(os.path.dirname(sample.peaks)):
os.makedirs(os.path.dirname(sample.peaks))
cmd = tk.macs2_call_peaks_atacseq(
treatment_bam=sample.filtered,
output_dir=sample.peaks_dir,
sample_name=sample.sample_name,
genome=sample.genome,
)
pipe_manager.run(cmd, sample.peaks, shell=True)
report_dict(pipe_manager, parse_peak_number(sample.peaks))
# Calculate fraction of reads in peaks (FRiP)
pipe_manager.timestamp("Calculating fraction of reads in peaks (FRiP)")
cmd = tk.calculate_frip(
input_bam=sample.filtered,
input_bed=sample.peaks,
output=sample.frip,
cpus=args.cores,
)
pipe_manager.run(cmd, sample.frip, shell=True)
total = float(pipe_manager.stats_dict["filtered_single_ends"]) + (
float(pipe_manager.stats_dict["filtered_paired_ends"]) / 2.0)
report_dict(pipe_manager, parse_frip(sample.frip, total))
# on an oracle peak list
if hasattr(pipe_manager.config.resources.oracle_peak_regions,
sample.genome):
cmd = calculate_frip(
input_bam=sample.filtered,
input_bed=getattr(
pipe_manager.config.resources.oracle_peak_regions,
sample.genome),
output=sample.oracle_frip,
cpus=args.cores,
)
pipe_manager.run(cmd, sample.oracle_frip, shell=True)
report_dict(
pipe_manager,
parse_frip(sample.oracle_frip, total, prefix="oracle_"),
)
# Plot fragment distribution
if sample.paired and not os.path.exists(sample.insertplot):
pipe_manager.timestamp("Plotting insert size distribution")
tk.plot_atacseq_insert_sizes(
bam=sample.filtered,
plot=sample.insertplot,
output_csv=sample.insertdata,
)
# # Count coverage genome-wide
# pipe_manager.timestamp("Calculating genome-wide coverage")
# cmd = tk.genome_wide_coverage(
# input_bam=sample.filtered,
# genome_windows=getattr(pipe_manager.config.resources.genome_windows, sample.genome),
# output=sample.coverage)
# pipe_manager.run(cmd, sample.coverage, shell=True)
# Calculate NSC, RSC
pipe_manager.timestamp("Assessing signal/noise in sample")
cmd = tk.run_spp(
input_bam=sample.filtered,
output=sample.qc,
plot=sample.qc_plot,
cpus=args.cores,
)
pipe_manager.run(cmd, sample.qc_plot, shell=True, nofail=True)
report_dict(pipe_manager, parse_nsc_rsc(sample.qc))
# Make tracks
track_dir = os.path.dirname(sample.bigwig)
if not os.path.exists(track_dir):
os.makedirs(track_dir)
# right now tracks are only made for bams without duplicates
pipe_manager.timestamp("Making bigWig tracks from BAM file")
cmd = bam_to_bigwig(
input_bam=sample.filtered,
output_bigwig=sample.bigwig,
genome=sample.genome,
normalization_method="RPGC",
)
pipe_manager.run(cmd, sample.bigwig, shell=True)
print(pipe_manager.stats_dict)
pipe_manager.stop_pipeline()
print("Finished processing sample %s." % sample.sample_name)