74 lines
3.8 KiB
Python
74 lines
3.8 KiB
Python
from java.lang import *
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from java.io import File,FileReader
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from net.sf.picard.metrics import MetricsFile
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from org.broadinstitute.sting.datasources.pipeline import Pipeline
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from org.broadinstitute.sting.utils.yaml import YamlUtils
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import os,string,sys
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def median(l):
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return sorted(l)[(len(l)+1)/2]
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def mean(l):
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return float(sum(l))/len(l)
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def get_metrics(filename):
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if not os.path.exists(filename):
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return None
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file_reader = FileReader(filename)
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metrics_file = MetricsFile()
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metrics_file.read(file_reader)
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metrics = metrics_file.getMetrics()
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file_reader.close()
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return metrics
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if len(sys.argv) != 2:
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print 'USAGE: %s <pipeline_file.yaml>'
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sys.exit(1)
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if not os.path.exists(sys.argv[1]):
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print 'Pipeline file %s not found' % sys.argv[1]
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sys.exit(1)
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pipeline_file = sys.argv[1]
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pipeline = YamlUtils.load(Pipeline,File(pipeline_file))
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header = ['SAMPLE','HAPLOTYPES_CONFIDENTLY_MATCHING.MIN','HAPLOTYPES_CONFIDENTLY_MATCHING.MAX','HAPLOTYPES_CONFIDENTLY_MATCHING.MEDIAN',
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'BAIT_SET','GENOME_SIZE','PCT_SELECTED_BASES','MEAN_TARGET_COVERAGE','ZERO_CVG_TARGETS_PCT','FOLD_80_BASE_PENALTY','HS_LIBRARY_SIZE',
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'PCT_PF_READS_ALIGNED','PF_HQ_ERROR_RATE','MEAN_READ_LENGTH','BAD_CYCLES','STRAND_BALANCE','PCT_CHIMERAS','PCT_ADAPTER','MEDIAN_INSERT_SIZE',
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'TOTAL_SNPS']
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data = ['%s'] * len(header)
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print string.join(header,'\t')
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# get a representative BAM file for each sample, to use as a base path. Note that this assumes every sample corresponds to the same base path.
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samples = dict()
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for sample in pipeline.getSamples():
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if sample.getBamFiles().size() > 0:
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samples[sample.getId()] = sample.getBamFiles().values().iterator().next()
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for sample_id,filename in samples.items():
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basepath = filename.getAbsolutePath()[0:filename.getAbsolutePath().rindex('.bam')]
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fingerprinting_summary_metrics = get_metrics('%s.%s' % (basepath,'fingerprinting_summary_metrics'))
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if fingerprinting_summary_metrics != None:
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haplotypes_confidently_matching = [metric.HAPLOTYPES_CONFIDENTLY_MATCHING for metric in fingerprinting_summary_metrics]
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min_haplotypes_confidently_matching = str(min(haplotypes_confidently_matching))
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max_haplotypes_confidently_matching = str(max(haplotypes_confidently_matching))
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median_haplotypes_confidently_matching = str(median(haplotypes_confidently_matching))
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else:
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min_haplotypes_confidently_matching = 'NA'
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max_haplotypes_confidently_matching = 'NA'
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median_haplotypes_confidently_matching = 'NA'
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hybrid_selection_metrics = get_metrics('%s.%s' % (basepath,'hybrid_selection_metrics'))[0]
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alignment_summary_metrics = get_metrics('%s.%s' % (basepath,'alignment_summary_metrics'))[0]
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insert_size_metrics = get_metrics('%s.%s' % (basepath,'insert_size_metrics'))[0]
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dbsnp_matches = get_metrics('%s.%s' % (basepath,'dbsnp_matches'))[0]
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print string.join(data,'\t')%(sample_id,min_haplotypes_confidently_matching,max_haplotypes_confidently_matching,median_haplotypes_confidently_matching,
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hybrid_selection_metrics.BAIT_SET,hybrid_selection_metrics.GENOME_SIZE,hybrid_selection_metrics.PCT_SELECTED_BASES,
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hybrid_selection_metrics.MEAN_TARGET_COVERAGE,hybrid_selection_metrics.ZERO_CVG_TARGETS_PCT,hybrid_selection_metrics.FOLD_80_BASE_PENALTY,
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hybrid_selection_metrics.HS_LIBRARY_SIZE,alignment_summary_metrics.PCT_PF_READS_ALIGNED,alignment_summary_metrics.PF_HQ_ERROR_RATE,
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alignment_summary_metrics.MEAN_READ_LENGTH,alignment_summary_metrics.BAD_CYCLES,alignment_summary_metrics.STRAND_BALANCE,
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alignment_summary_metrics.PCT_CHIMERAS,alignment_summary_metrics.PCT_ADAPTER,insert_size_metrics.MEDIAN_INSERT_SIZE,dbsnp_matches.TOTAL_SNPS)
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