def run(self, taxonomyStr, ubiquityThreshold, singleCopyThreshold, percentCompletion, numReplicates, numGenomes, contigLen): img = IMG() genomeIds = img.genomeIdsByTaxonomy(taxonomyStr, 'Final') print('\nLineage ' + taxonomyStr + ' contains ' + str(len(genomeIds)) + ' genomes.') # build marker genes and colocated marker sets countTable = img.countTable(genomeIds) markerGenes = img.markerGenes(genomeIds, countTable, ubiquityThreshold * len(genomeIds), singleCopyThreshold * len(genomeIds)) print(' Marker genes: ' + str(len(markerGenes))) geneDistTable = img.geneDistTable(genomeIds, markerGenes, spacingBetweenContigs=1e6) colocatedGenes = img.colocatedGenes(geneDistTable) colocatedSets = img.colocatedSets(colocatedGenes, markerGenes) print(' Co-located gene sets: ' + str(len(colocatedSets))) # random sample genomes if numGenomes == -1: rndGenomeIds = genomeIds else: rndGenomeIds = random.sample(genomeIds, numGenomes) # estimate completion for each genome using both the marker genes and marker sets metadata = img.genomeMetadata('Final') plotLabels = [] plotData = [] for genomeId in rndGenomeIds: mgCompletion = [] msCompletion = [] for _ in range(0, numReplicates): startPartialGenomeContigs = img.sampleGenome( metadata[genomeId]['genome size'], percentCompletion, contigLen) # calculate completion with marker genes containedMarkerGenes = img.containedMarkerGenes( markerGenes, geneDistTable[genomeId], startPartialGenomeContigs, contigLen) mgCompletion.append( float(len(containedMarkerGenes)) / len(markerGenes) - percentCompletion) # calculate completion with marker set comp = 0.0 for cs in colocatedSets: present = 0 for contigId in cs: if contigId in containedMarkerGenes: present += 1 comp += float(present) / len(cs) msCompletion.append(comp / len(colocatedSets) - percentCompletion) plotData.append(mgCompletion) plotData.append(msCompletion) species = ' '.join( metadata[genomeId]['taxonomy'][ranksByLabel['Genus']:]) plotLabels.append(species + ' (' + genomeId + ')') plotLabels.append('') # plot data boxPlot = BoxPlot() plotFilename = './images/sim.MGvsMS.' + taxonomyStr.replace( ';', '_') + '.' + str(percentCompletion) + '.errorbar.png' title = taxonomyStr.replace( ';', '; ') + '\n' + 'Percent completion = %.2f' % percentCompletion boxPlot.plot(plotFilename, plotData, plotLabels, r'$\Delta$' + ' Percent Completion', '', False, title)
def run(self, taxonomyStr, ubiquityThreshold, singleCopyThreshold, percentCompletion, numReplicates, numGenomes, contigLen): img = IMG() genomeIds = img.genomeIdsByTaxonomy(taxonomyStr, 'Final') print '\nLineage ' + taxonomyStr + ' contains ' + str(len(genomeIds)) + ' genomes.' # build marker genes and colocated marker sets countTable = img.countTable(genomeIds) markerGenes = img.markerGenes(genomeIds, countTable, ubiquityThreshold*len(genomeIds), singleCopyThreshold*len(genomeIds)) print ' Marker genes: ' + str(len(markerGenes)) geneDistTable = img.geneDistTable(genomeIds, markerGenes) colocatedGenes = img.colocatedGenes(geneDistTable) colocatedSets = img.colocatedSets(colocatedGenes, markerGenes) print ' Co-located gene sets: ' + str(len(colocatedSets)) # random sample genomes if numGenomes == -1: rndGenomeIds = genomeIds else: rndGenomeIds = random.sample(genomeIds, numGenomes) # estimate completion for each genome using both the marker genes and marker sets metadata = img.genomeMetadata('Final') plotLabels = [] plotData = [] for genomeId in rndGenomeIds: mgCompletion = [] msCompletion = [] for _ in xrange(0, numReplicates): startPartialGenomeContigs = img.sampleGenome(metadata[genomeId]['genome size'], percentCompletion, contigLen) # calculate completion with marker genes containedMarkerGenes = img.containedMarkerGenes(markerGenes, geneDistTable[genomeId], startPartialGenomeContigs, contigLen) mgCompletion.append(float(len(containedMarkerGenes))/len(markerGenes) - percentCompletion) # calculate completion with marker set comp = 0.0 for cs in colocatedSets: present = 0 for contigId in cs: if contigId in containedMarkerGenes: present += 1 comp += float(present) / len(cs) msCompletion.append(comp / len(colocatedSets) - percentCompletion) plotData.append(mgCompletion) plotData.append(msCompletion) species = ' '.join(metadata[genomeId]['taxonomy'][ranksByLabel['Genus']:]) plotLabels.append(species + ' (' + genomeId + ')') plotLabels.append('') # plot data boxPlot = BoxPlot() plotFilename = './images/sim.MGvsMS.' + taxonomyStr.replace(';','_') + '.' + str(percentCompletion) + '.errorbar.png' title = taxonomyStr.replace(';', '; ') + '\n' + 'Percent completion = %.2f' % percentCompletion boxPlot.plot(plotFilename, plotData, plotLabels, r'$\Delta$' + ' Percent Completion', '', False, title)
def run(self, taxonomyStr, mostSpecificRank, minGenomes, ubiquityThreshold, singleCopyThreshold, percentCompletion, numReplicates, numGenomes, contigLen): img = IMG() lineages = [] taxon = taxonomyStr.split(';') for r in range(0, len(taxon)): lineages.append(';'.join(taxon[0:r + 1])) # get all marker sets markerGenes = [] geneDistTable = [] colocatedSets = [] for lineage in lineages: genomeIds = img.genomeIdsByTaxonomy(lineage, 'Final') print('\nLineage ' + lineage + ' contains ' + str(len(genomeIds)) + ' genomes.') # build marker genes and colocated marker sets countTable = img.countTable(genomeIds) mg = img.markerGenes(genomeIds, countTable, ubiquityThreshold * len(genomeIds), singleCopyThreshold * len(genomeIds)) print(' Marker genes: ' + str(len(mg))) mdt = img.geneDistTable(genomeIds, mg, spacingBetweenContigs=1e6) colocatedGenes = img.colocatedGenes(mdt) cs = img.colocatedSets(colocatedGenes, mg) print(' Co-located gene sets: ' + str(len(cs))) markerGenes.append(mg) geneDistTable.append(mdt) colocatedSets.append(cs) # random sample genomes if numGenomes == -1: rndGenomeIds = genomeIds else: rndGenomeIds = random.sample(genomeIds, numGenomes) # estimate completion for each genome using both the marker genes and marker sets metadata = img.genomeMetadata('Final') plotLabels = [] plotData = [] for genomeId in rndGenomeIds: completion = [[] for _ in range(len(lineages))] for _ in range(0, numReplicates): startPartialGenomeContigs = img.sampleGenome( metadata[genomeId]['genome size'], percentCompletion, contigLen) # calculate completion with marker set for i in range(len(lineages)): containedMarkerGenes = img.containedMarkerGenes( markerGenes[i], geneDistTable[i][genomeId], startPartialGenomeContigs, contigLen) comp = 0.0 for cs in colocatedSets[i]: present = 0 for contigId in cs: if contigId in containedMarkerGenes: present += 1 comp += float(present) / len(cs) completion[i].append(comp / len(colocatedSets[i]) - percentCompletion) plotLabels.append(genomeId + ' - ' + lineages[i]) for d in completion: plotData.append(d) # plot data boxPlot = BoxPlot() plotFilename = './images/sim.lineages.' + taxonomyStr.replace( ';', '_') + '.' + str(percentCompletion) + '.errorbar.png' title = taxonomyStr.replace( ';', '; ') + '\n' + 'Percent completion = %.2f' % percentCompletion boxPlot.plot(plotFilename, plotData, plotLabels, r'$\Delta$' + ' Percent Completion', '', False, title)
def run(self, taxonomyStr, mostSpecificRank, minGenomes, ubiquityThreshold, singleCopyThreshold, percentCompletion, numReplicates, numGenomes, contigLen): img = IMG() lineages = [] taxon = taxonomyStr.split(';') for r in xrange(0, len(taxon)): lineages.append(';'.join(taxon[0:r+1])) # get all marker sets markerGenes = [] geneDistTable = [] colocatedSets = [] for lineage in lineages: genomeIds = img.genomeIdsByTaxonomy(lineage, 'Final') print '\nLineage ' + lineage + ' contains ' + str(len(genomeIds)) + ' genomes.' # build marker genes and colocated marker sets countTable = img.countTable(genomeIds) mg = img.markerGenes(genomeIds, countTable, ubiquityThreshold*len(genomeIds), singleCopyThreshold*len(genomeIds)) print ' Marker genes: ' + str(len(mg)) mdt = img.geneDistTable(genomeIds, mg, spacingBetweenContigs=1e6) colocatedGenes = img.colocatedGenes(mdt) cs = img.colocatedSets(colocatedGenes, mg) print ' Co-located gene sets: ' + str(len(cs)) markerGenes.append(mg) geneDistTable.append(mdt) colocatedSets.append(cs) # random sample genomes if numGenomes == -1: rndGenomeIds = genomeIds else: rndGenomeIds = random.sample(genomeIds, numGenomes) # estimate completion for each genome using both the marker genes and marker sets metadata = img.genomeMetadata('Final') plotLabels = [] plotData = [] for genomeId in rndGenomeIds: completion = [[] for _ in xrange(len(lineages))] for _ in xrange(0, numReplicates): startPartialGenomeContigs = img.sampleGenome(metadata[genomeId]['genome size'], percentCompletion, contigLen) # calculate completion with marker set for i in xrange(len(lineages)): containedMarkerGenes = img.containedMarkerGenes(markerGenes[i], geneDistTable[i][genomeId], startPartialGenomeContigs, contigLen) comp = 0.0 for cs in colocatedSets[i]: present = 0 for contigId in cs: if contigId in containedMarkerGenes: present += 1 comp += float(present) / len(cs) completion[i].append(comp / len(colocatedSets[i]) - percentCompletion) plotLabels.append(genomeId + ' - ' + lineages[i]) for d in completion: plotData.append(d) # plot data boxPlot = BoxPlot() plotFilename = './images/sim.lineages.' + taxonomyStr.replace(';','_') + '.' + str(percentCompletion) + '.errorbar.png' title = taxonomyStr.replace(';', '; ') + '\n' + 'Percent completion = %.2f' % percentCompletion boxPlot.plot(plotFilename, plotData, plotLabels, r'$\Delta$' + ' Percent Completion', '', False, title)