def mutect(tumor_bam,
normal_bam,
output,
germline_resource,
reference,
interval_list="",
pon="",
ip="50"):
args = defaultdict(list)
args["R"] = reference
args["I"].append(normal_bam)
args["I"].append(tumor_bam)
args["O"] = output
args["normal"] = getSampleName(normal_bam, "/tmp/normal.name")
args["-f1r2-tar-gz"] = output + ".f1r2.tar.gz"
args["bamout"] = f"{output}.bamout.bam"
args["-germline-resource"] = germline_resource
if pon:
args["pon"] = pon
if interval_list:
args["L"] = interval_list
args["ip"] = ip
check_output(["gatk Mutect2" + build_args_from_dict(args)], shell=True)
return output
def applyBQSR(input_bam, output, recalibration_table, reference):
args = defaultdict(list)
args["R"] = reference
args["I"] = input_bam
args["O"] = output
args["bqsr"] = recalibration_table
check_output(["gatk ApplyBQSR" + build_args_from_dict(args)], shell=True)
return output
def markAdapters(input_bam, output, metrics_file, tmp_dir):
args = defaultdict(list)
args["I"] = input_bam
args["O"] = output
args["M"] = metrics_file
args["TMP_DIR"] = tmp_dir
check_output(["gatk MarkIlluminaAdapters " + build_args_from_dict(args)],
shell=True)
return output
def learnReadOrientationModel(input_f1r2, output):
args = defaultdict(list)
args["I"] = input_f1r2
args["O"] = output
check_output(
["gatk LearnReadOrientationModel" + build_args_from_dict(args)],
shell=True)
return output
def getSampleName(input_bam, output):
args = defaultdict(list)
args["I"] = input_bam
args["O"] = output
check_output(["gatk GetSampleName" + build_args_from_dict(args)],
shell=True)
samplename = ""
with open(output, 'r') as f:
samplename = f.read()
return samplename
def markDuplicates(input_bam, output, metrics_file, tmp_dir):
args = defaultdict(list)
args["METRICS_FILE"] = metrics_file
args["O"] = output
args["CREATE_INDEX"] = "true"
args["OPTICAL_DUPLICATE_PIXEL_DISTANCE"] = "100"
args["TMP_DIR"] = tmp_dir
args["INPUT"] = input_bam
check_output(["gatk MarkDuplicates" + build_args_from_dict(args)],
shell=True)
def samToFastq(input_bam, output, tmp_dir):
args = defaultdict(list)
args["I"] = input_bam
args["FASTQ"] = output
args["CLIPPING_ATTRIBUTE"] = "XT"
args["CLIPPING_ACTION"] = "2"
args["INTERLEAVE"] = "true"
args["TMP_DIR"] = tmp_dir
check_output(["gatk SamToFastq " + build_args_from_dict(args)], shell=True)
return output
def GetPileupSummaries(input_bam, output, variant, interval_list, ip="50"):
args = defaultdict(list)
args["I"] = input_bam
args["O"] = output
args["V"] = variant
args["L"] = interval_list
args["ip"] = ip
check_output(["gatk GetPileupSummaries" + build_args_from_dict(args)],
shell=True)
return output
def SelectVariants(input_variants, output, reference):
args = defaultdict(list)
args["V"] = input_variants
args["O"] = output
args["R"] = reference
args["-exclude-filtered"] = "true"
args["-exclude-non-variants"] = "true"
check_output(["gatk SelectVariants" + build_args_from_dict(args)],
shell=True)
return output
def CalculateContamination(tumor_pileupsummaries, normal_pileupsummaries,
output, segments):
args = defaultdict(list)
args["I"] = tumor_pileupsummaries
args["matched"] = normal_pileupsummaries
args["O"] = output
args["segments"] = segments
check_output(["gatk CalculateContamination" + build_args_from_dict(args)],
shell=True)
return output
def FilterByOrientationBias(input_variants, output, metrics):
args = defaultdict(list)
args["V"] = input_variants
args["O"] = output
args["-artifact-modes"].append("C/A")
args["-artifact-modes"].append("T/G")
args["P"] = metrics
check_output(["gatk FilterByOrientationBias" + build_args_from_dict(args)],
shell=True)
return output
def FilterMutectCalls(input_variants, output, reference, contamination_table,
segments, ob_priors):
args = defaultdict(list)
args["V"] = input_variants
args["O"] = output
args["R"] = reference
args["-orientation-bias-artifact-priors"] = ob_priors
args["-contamination-table"] = contamination_table
args["-tumor-segmentation"] = segments
check_output(["gatk FilterMutectCalls" + build_args_from_dict(args)],
shell=True)
return output
def validateSamFile(input_bam, output, reference):
if not Path(output):
args = defaultdict(list)
args["R"] = reference
args["I"] = input_bam
args["O"] = output
args["MODE"] = "SUMMARY"
check_output(["gatk ValidateSamFile" + build_args_from_dict(args)],
shell=True)
else:
with open(output, "r") as f:
print("Validation results", f.read())
def CollectSequencingArtifactMetrics(input_bam,
output,
reference,
extension=".txt"):
args = defaultdict(list)
args["I"] = input_bam
args["O"] = output
args["R"] = reference
args["EXT"] = extension
check_output(
["gatk CollectSequencingArtifactMetrics" + build_args_from_dict(args)],
shell=True)
return output
def revertSam(input_bam, output, tmp_dir):
args = defaultdict(list)
args["I"] = input_bam
args["O"] = output
args["SANITIZE"] = "true"
args["MAX_DISCARD_FRACTION"] = "0.005"
args["ATTRIBUTE_TO_CLEAR"].append("XA")
args["ATTRIBUTE_TO_CLEAR"].append("BD")
args["ATTRIBUTE_TO_CLEAR"].append("XS")
args["ATTRIBUTE_TO_CLEAR"].append("BI")
args["SORT_ORDER"] = "queryname"
args["RESTORE_ORIGINAL_QUALITIES"] = "true"
args["REMOVE_DUPLICATE_INFORMATION"] = "true"
args["REMOVE_ALIGNMENT_INFORMATION"] = "true"
args["TMP_DIR"] = tmp_dir
check_output(["gatk RevertSam" + build_args_from_dict(args)], shell=True)
return output
def mergeBamAlignment(input_unmapped_bam, input_aligned_bam, output, reference,
tmp_dir):
args = defaultdict(list)
args["R"] = reference
args["UNMAPPED_BAM"] = input_unmapped_bam
args["ALIGNED_BAM"] = input_aligned_bam
args["O"] = output
args["CREATE_INDEX"] = "true"
args["ADD_MATE_CIGAR"] = "true"
args["CLIP_ADAPTERS"] = "true"
args["CLIP_OVERLAPPING_READS"] = "true"
args["INCLUDE_SECONDARY_ALIGNMENTS"] = "true"
args["MAX_INSERTIONS_OR_DELETIONS"] = "-1"
args["PRIMARY_ALIGNMENT_STRATEGY"] = "MostDistant"
args["ATTRIBUTES_TO_RETAIN"] = "XS"
args["TMP_DIR"] = tmp_dir
check_output(["gatk MergeBamAlignment" + build_args_from_dict(args)],
shell=True)
return output
def BaseRecalibrator(input_bam,
output,
reference,
interval_list="",
known_sites="",
interval_padding="50"):
args = defaultdict(list)
args["R"] = reference
args["I"] = input_bam
args["O"] = output
if interval_list:
args["L"] = interval_list
args["ip"] = interval_padding
if known_sites:
for known_site in known_sites.split():
args["-known-sites"].append(known_site)
check_output(["gatk BaseRecalibrator" + build_args_from_dict(args)],
shell=True)
return output
def fastqToSam(fastq1,
fastq2,
output,
read_group_name,
sample_name,
library_name="DefaultLibraryName",
platform_unit="DefaultPlatformUnit",
platform="DefaultPlatform",
sequencing_center="DefaultSequencingCenter",
run_date=datetime.now().isoformat()):
args = defaultdict(list)
args["FASTQ"] = fastq1
args["FASTQ2"] = fastq2
args["OUTPUT"] = output
args["READ_GROUP_NAME"] = read_group_name
args["SAMPLE_NAME"] = sample_name
args["LIBRARY_NAME"] = library_name
args["PLATFORM_UNIT"] = platform_unit
args["PLATFORM"] = platform
args["SEQUENCING_CENTER"] = sequencing_center
args["RUN_DATE"] = run_date
check_output(["gatk FastqToSam " + build_args_from_dict(args)], shell=True)
return output
def bwaMem(input_fastq, output, reference, threads=str(cpu_count())):
args = defaultdict(list)
args["t"] = threads
check_output(["bwa mem -M -p " + build_args_from_dict(args) + " " + reference + " " + input_fastq + " > " + output], shell=True)