def create_data_dir(args, fasta_path, bam_path):
print "++Creating data directory for bam2aln processing."
data_dir = os.path.join(args.output_dir, "data")
if not os.path.exists(data_dir):
os.makedirs(data_dir)
reference_fasta_path = os.path.join(data_dir, "reference.fasta")
if not os.path.exists(reference_fasta_path):
shutil.copy2(fasta_path, reference_fasta_path)
samtools.faidx(reference_fasta_path)
reference_bam_path = os.path.join(data_dir, "reference.bam")
if not os.path.exists(reference_bam_path):
shutil.copy2(bam_path, reference_bam_path)
samtools.index(reference_bam_path)
def do_gatk(args):
fasta_path, sorted_bam_path = pipelines.common.ssaha2_alignment(args)
#Gatk
#Step 3
step_3_dir = os.path.join(args.output_dir, "03_gatk")
step_3_file = os.path.join(step_3_dir, "gatk.done")
realigned_bam_path = ""
if not os.path.exists(step_3_file):
print "++Gatk recalibration and realignment of reference alignment file."
if not os.path.exists(step_3_dir):
os.makedirs(step_3_dir)
#Step: Picard: Mark Duplicates.
dedup_bam_path = os.path.join(step_3_dir, "dedup.bam")
dedup_metrics_path = os.path.join(step_3_dir, "dedup.metrics")
picardtools.mark_duplicates(sorted_bam_path, dedup_bam_path, dedup_metrics_path)
#Step: Samtools: Index BAM. ***Do after mark duplicates.
index_done_file = os.path.join(step_3_dir, "index.done")
if not os.path.exists(index_done_file):
samtools.index(dedup_bam_path)
open(index_done_file, 'w').close()
#Step: Gatk Realign.
#Gatk: Intervals.
intervals_path = gatk.realigner_target_creator(fasta_path, dedup_bam_path)
#Gatk: Indel Realign.
realigned_bam_path = gatk.indel_realigner(fasta_path, dedup_bam_path, intervals_path)
#Step: Gatk Recal. ***May not be able to do due to need for dbSNP file.
#CountCovariates.
#recal_csv_path = os.path.join(step_3_dir, "recal_data.csv")
#gatk.count_covariates(fasta_path, realigned_bam_path, recal_csv_path)
##TableRecalibration.
#recal_bam_path = os.path.join(step_3_dir, "recal.bam")
#gatk.table_recalibration(fasta_path, realigned_bam_path, recal_csv_path, recal_bam_path)
p.dump(realigned_bam_path, open(step_3_file, 'w'))
else:
realigned_bam_path = p.load(open(step_3_file, 'r'))
#Gatk Output
#Step 4
output_dir = os.path.join(args.output_dir, "output")
raw_vcf_path = os.path.join(output_dir, "output.vcf")
gd_path = os.path.join(output_dir, "output.gd")
print "++Filtering poor values for SNPs and INDELs in output and converting vcf files to gd."
if not os.path.exists(output_dir): os.makedirs(output_dir)
if not os.path.exists(raw_vcf_path):
gatk.unified_genotyper(fasta_path, realigned_bam_path, raw_vcf_path, args.glm_option)
breseq.command.vcf2gd(raw_vcf_path, gd_path)
#Gatk recommended filter values for SNPs and INDELs.
snp_filters = ['"QD < 2.0"',\
'"MQ < 40.0"',\
'"FS > 60.0"',\
'"HaplotypeScore > 13.0"',\
'"MQRankSum < -12.5"',\
'"ReadPosRankSum < -8.0"']
indel_filters = ['"QD < 2.0"',\
'"ReadPosRankSum < -20.0"',\
'"InbreedingCoeff < -0.8"',\
'"FS > 200.0"']
snp_gd = os.path.join(output_dir, "SNP.gd")
indels_gd = os.path.join(output_dir, "INDELS.gd")
breseq.command.genome_diff_filter(snp_gd, gd_path, ["SNP"], snp_filters)
breseq.command.genome_diff_filter(indels_gd, gd_path, ["INS", "DEL"], indel_filters)
breseq.command.genome_diff_merge([snp_gd, indels_gd] , gd_path)
breseq.command.genome_diff_filter(gd_path, gd_path, ["ALL"], ['"AF!=1.00"'])
pipelines.common.create_data_dir(args, fasta_path, realigned_bam_path)
