Now supports resume and dry runningRecalQual.py
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@996 348d0f76-0448-11de-a6fe-93d51630548a
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depristo
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4eda040e0f
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@ -15,13 +15,25 @@ def geli2dbsnpFile(geli):
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root, flowcellDotlane, ext = picard_utils.splitPath(geli)
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return os.path.join(root, flowcellDotlane) + '.dbsnp_matches'
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def SingleSampleGenotyperCmd(bam, geli, use2bp):
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naid = bam.split(".")[0]
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metrics = geli + '.metrics'
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gatkPath = '/humgen/gsa-scr1/kiran/repositories/Sting/trunk/dist/GenomeAnalysisTK.jar'
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hapmapChip = '/home/radon01/andrewk/hapmap_1kg/gffs/' + naid + '.gff'
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targetList = '/home/radon01/depristo/work/1kg_pilot_evaluation/data/thousand_genomes_alpha_redesign.targets.interval_list'
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cmd = "java -ea -jar " + gatkPath + ' ' + ' '.join(['-T SingleSampleGenotyper', '-I', bam, '-L', targetList, '-R', ref, '-D', '/humgen/gsa-scr1/GATK_Data/dbsnp.rod.out', '--hapmap_chip', hapmapChip, '-calls', geli, '-met', metrics, '-geli -fb -l INFO'])
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return cmd
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def bams2geli(bams):
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def call1(bam):
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geli = os.path.splitext(bam)[0] + '.geli'
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jobid = 0
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if not os.path.exists(geli):
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cmd = picard_utils.callGenotypesCmd( bam, geli, options = picard_utils.hybridSelectionExtraArgsForCalling())
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if OPTIONS.useSSG:
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if not os.path.exists(geli + '.calls'):
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cmd = SingleSampleGenotyperCmd(bam, geli + '.calls', OPTIONS.useSSG2b)
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else:
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if not os.path.exists(geli):
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cmd = picard_utils.callGenotypesCmd( bam, geli, options = picard_utils.hybridSelectionExtraArgsForCalling())
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jobid = farm_commands.cmd(cmd, OPTIONS.farmQueue, just_print_commands = OPTIONS.dry )
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return geli, jobid
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calls = map(call1, bams)
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@ -44,6 +56,12 @@ def main():
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parser.add_option("", "--dry", dest="dry",
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action='store_true', default=False,
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help="If provided, nothing actually gets run, just a dry run")
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parser.add_option("", "--ssg", dest="useSSG",
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action='store_true', default=False,
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help="If provided, we'll use the GATK SSG for genotyping")
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parser.add_option("", "--ssg2b", dest="useSSG2b",
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action='store_true', default=False,
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help="If provided, we'll use 2bp enabled GATK SSG ")
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parser.add_option("-o", "--output", dest="output",
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type="string", default='/dev/stdout',
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help="x")
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@ -18,6 +18,7 @@ output_root = './'
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# Location of the resource files distributed with the recalibration tool.
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# If editing, please end this variable with a trailing slash.
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resources='resources/'
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#resources='/broad/1KG/DCC_recal/ReadQualityRecalibrator/resources/'
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import getopt,glob,os,sys
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import LogisticRegressionByReadGroup
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@ -39,12 +40,29 @@ dbsnp = resources + 'dbsnp.1kg.rod.out'
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# Where are the application files required to run the recalibration?
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gatk = resources + 'gatk/GenomeAnalysisTK.jar'
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#gatk = '/home/radon01/depristo/dev/GenomeAnalysisTK/trunk/dist/GenomeAnalysisTK.jar'
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logistic_regression_script = resources + 'logistic_regression.R'
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empirical_vs_reported_grapher = resources + 'plot_q_emp_stated_hst.R'
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# Assemble the platform list into command-line arguments.
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platform_args = ' '.join(['-pl %s' % platform for platform in platforms])
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def output_file_needs_update(output_file, input_files = []):
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if reprocessFiles: # we are always reprocessing files
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return True
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if not os.path.exists(output_file): # the file doesn't exist
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return True
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else:
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return False
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def executeCommand(cmd):
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if not dryRun:
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return os.system(cmd)
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else:
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print ' => Would execute', cmd
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return 0
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def exit(msg,errorcode):
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print msg
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sys.exit(errorcode)
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@ -57,7 +75,7 @@ def graph_file(graph_script,graph_data):
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'Graph the given data using the given script. Leave the data in the output directory.'
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check_input_file_available(graph_script,'%s R graphing script' % graph_script)
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check_input_file_available(graph_data,'%s graphing data' % graph_data)
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result = os.system(' '.join((R_exe,graph_script,graph_data)))
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result = executeCommand(' '.join((R_exe,graph_script,graph_data)))
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if result != 0:
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exit('Unable to graph data: %s' % graph_data,1)
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@ -65,54 +83,70 @@ def recalibrate():
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'Recalibrate the given bam file'
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# generate the covariates
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print 'generating covariates'
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generate_covariates = ' '.join((gatk_base_cmdline,'-T CountCovariates','-I',bam,'-mqs 40','--OUTPUT_FILEROOT',output_dir+'initial','--CREATE_TRAINING_DATA','--MIN_MAPPING_QUALITY 1',platform_args))
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returncode = os.system(generate_covariates)
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if returncode != 0:
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exit('Unable to generate covariates',1)
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# compute the logistic regression
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print 'computing the logistic regression'
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LogisticRegressionByReadGroup.compute_logistic_regression(output_dir + 'initial.covariate_counts.csv',output_dir + 'linear_regression_results.out',R_exe,logistic_regression_script)
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# apply the logistic regression, writing the output data to calibrated_bam
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print 'applying the correction to the reads'
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apply_logistic_regression = ' '.join((gatk_base_cmdline,'-T LogisticRecalibration','-I',bam,'-logisticParams',output_dir+'linear_regression_results.out','-outputBAM',calibrated_bam))
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returncode = os.system(apply_logistic_regression)
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if returncode != 0:
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exit('Unable to apply logistic regression',1)
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linear_regression_results = output_dir + 'linear_regression_results.out'
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if output_file_needs_update(linear_regression_results):
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generate_covariates = ' '.join((gatk_base_cmdline,'-T CountCovariates','-I',bam,'-mqs 40','--OUTPUT_FILEROOT',output_dir+'initial','--CREATE_TRAINING_DATA','--MIN_MAPPING_QUALITY 1',platform_args))
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returncode = executeCommand(generate_covariates)
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if returncode != 0:
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exit('Unable to generate covariates',1)
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# compute the logistic regression
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if not dryRun:
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print 'computing the logistic regression'
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LogisticRegressionByReadGroup.compute_logistic_regression(output_dir + 'initial.covariate_counts.csv',linear_regression_results,R_exe,logistic_regression_script)
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else:
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print 'Logistic recalibration data files already generated', linear_regression_results
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if output_file_needs_update(calibrated_bam):
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# apply the logistic regression, writing the output data to calibrated_bam
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print 'applying the correction to the reads'
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apply_logistic_regression = ' '.join((gatk_base_cmdline,'-T LogisticRecalibration','-I',bam,'-logisticParams',output_dir+'linear_regression_results.out','-outputBAM',calibrated_bam))
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returncode = executeCommand(apply_logistic_regression)
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if returncode != 0:
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exit('Unable to apply logistic regression',1)
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else:
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print 'Recalibrated BAM file already generated', calibrated_bam
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# index the calibrated bam
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print 'indexing the calibrated bam'
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index_calibrated_bamfile = ' '.join((samtools_exe,'index',calibrated_bam))
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returncode = os.system(index_calibrated_bamfile)
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if returncode != 0:
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exit('Unable to index calibrated bamfile',1)
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if output_file_needs_update(calibrated_bam + '.bai'):
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print 'indexing the calibrated bam'
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index_calibrated_bamfile = ' '.join((samtools_exe,'index',calibrated_bam))
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returncode = executeCommand(index_calibrated_bamfile)
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if returncode != 0:
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exit('Unable to index calibrated bamfile',1)
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else:
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print 'Recalibrated BAM index file already generated', calibrated_bam + '.bai'
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print 'Recalibration complete! Calibrated bam is available here: ' + calibrated_bam
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def evaluate():
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'Evaluate recalibration results.'
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print 'Evaluating recalibration results'
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# regenerate the covariates
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regenerate_covariates = ' '.join((gatk_base_cmdline,'-T CountCovariates','-I',calibrated_bam,'-mqs 40','--OUTPUT_FILEROOT',output_dir+'recalibrated','--CREATE_TRAINING_DATA','--MIN_MAPPING_QUALITY 1',platform_args))
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print 'regenerating covariates'
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returncode = os.system(regenerate_covariates)
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if returncode != 0:
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exit('Unable to regenerate covariates',1)
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print 'graphing initial results'
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for filename in glob.glob(output_dir+'initial.*.empirical_v_reported_quality.csv'):
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graph_file(empirical_vs_reported_grapher,filename)
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print 'graphing final results'
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for filename in glob.glob(output_dir+'recalibrated.*.empirical_v_reported_quality.csv'):
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graph_file(empirical_vs_reported_grapher,filename)
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recalibrated_regression_results = output_dir + 'recalibrated' + 'linear_regression_results.out'
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if output_file_needs_update(recalibrated_regression_results):
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# regenerate the covariates
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regenerate_covariates = ' '.join((gatk_base_cmdline,'-T CountCovariates','-I',calibrated_bam,'-mqs 40','--OUTPUT_FILEROOT',output_dir+'recalibrated','--CREATE_TRAINING_DATA','--MIN_MAPPING_QUALITY 1',platform_args))
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print 'regenerating covariates'
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returncode = executeCommand(regenerate_covariates)
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if returncode != 0:
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exit('Unable to regenerate covariates',1)
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print 'graphing initial results'
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for filename in glob.glob(output_dir+'initial.*.empirical_v_reported_quality.csv'):
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graph_file(empirical_vs_reported_grapher,filename)
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print 'graphing final results'
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for filename in glob.glob(output_dir+'recalibrated.*.empirical_v_reported_quality.csv'):
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graph_file(empirical_vs_reported_grapher,filename)
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else:
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print 'Evaluated files already generated', recalibrated_regression_results
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def usage():
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exit('Usage: python RecalQual.py [--recalibrate] [--evaluate] <input bam file> <calibrated output bam file>',1)
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# Try to parse the given command-line arguments.
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try:
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opts, args = getopt.gnu_getopt(sys.argv[1:],'',['recalibrate','evaluate'])
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opts, args = getopt.gnu_getopt(sys.argv[1:],'',['recalibrate','evaluate', 'reprocess', 'dry'])
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except getopt.GetoptError, err:
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usage()
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@ -123,12 +157,18 @@ if len(args) < 2:
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# Determine whether to evaluate / recalibrate.
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recalibrate_requested = False
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evaluate_requested = False
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reprocessFiles = False
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dryRun = False
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for opt,arg in opts:
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if opt == '--recalibrate':
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recalibrate_requested = True
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if opt == '--evaluate':
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evaluate_requested = True
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if opt == '--reprocess':
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reprocessFiles = True
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if opt == '--dry':
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dryRun = True
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# Default to 'recalibrate' unless the user specified only evaluate.
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do_recalibration = not (evaluate_requested and not recalibrate_requested)
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# Only evaluate if the user specifically requested evaluation.
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