def run(self, outputFile):
img = IMG()
print 'Identifying all IMG prokaryotic genomes with valid data.'
metadata = img.genomeMetadata()
genomeIds = img.genomeIdsByTaxonomy('prokaryotes', metadata)
genomeMissingData = img.genomesWithMissingData(genomeIds)
genomeIds -= genomeMissingData
print ' Identified %d valid genomes.' % (len(genomeIds))
print 'Calculating gene copy number for each genome.'
countTable = img.geneCountTable(genomeIds)
counts = []
for _, count in countTable['pfam00318'].iteritems():
counts.append(count)
print len(genomeIds)
print len(counts)
print mean(counts)
fout = open(outputFile, 'w')
fout.write(str(countTable))
fout.close()
print 'Gene count dictionary to: ' + outputFile
def run(self):
img = IMG()
fout = open('./data/evaluate_hmms_with_prodigal.txt', 'w', 1)
# get list of all marker genes
markerset = MarkerSet()
pfamMarkers, tigrMarkers = markerset.getCalculatedMarkerGenes()
print 'PFAM marker genes: ' + str(len(tigrMarkers))
print 'TIGR marker genes: ' + str(len(pfamMarkers))
print ''
# run HMMs on each of the finished genomes
genomeIds = img.genomeIds('Finished')
for genomeId in genomeIds:
print genomeId + ':'
fout.write(genomeId + ':\n')
self.runPFAM(genomeId)
self.runTIGRFAM(genomeId)
fout.write(' ORF results:\n')
self.compareResults(genomeId, pfamMarkers, tigrMarkers, fout)
#self.translateSixFrames(genomeId)
#self.runPFAM_SixFrames(genomeId)
#self.runTIGRFAM_SixFrames(genomeId)
#fout.write(' Six-frame translation results:\n')
#self.compareSixFrameResults(genomeId, pfamMarkers, tigrMarkers, fout)
fout.close()
def run(self):
img = IMG()
fout = open('./data/evaluate_hmms_with_prodigal.txt', 'w', 1)
# get list of all marker genes
markerset = MarkerSet()
pfamMarkers, tigrMarkers = markerset.getCalculatedMarkerGenes()
print('PFAM marker genes: ' + str(len(tigrMarkers)))
print('TIGR marker genes: ' + str(len(pfamMarkers)))
print('')
# run HMMs on each of the finished genomes
genomeIds = img.genomeIds('Finished')
for genomeId in genomeIds:
print(genomeId + ':')
fout.write(genomeId + ':\n')
self.runPFAM(genomeId)
self.runTIGRFAM(genomeId)
fout.write(' ORF results:\n')
self.compareResults(genomeId, pfamMarkers, tigrMarkers, fout)
#self.translateSixFrames(genomeId)
#self.runPFAM_SixFrames(genomeId)
#self.runTIGRFAM_SixFrames(genomeId)
#fout.write(' Six-frame translation results:\n')
#self.compareSixFrameResults(genomeId, pfamMarkers, tigrMarkers, fout)
fout.close()
def __init__(self, outputDir):
self.img = IMG()
self.markerSetBuilder = MarkerSetBuilder()
if os.path.exists(outputDir):
print '[Error] Output directory already exists: ' + outputDir
sys.exit(0)
else:
os.makedirs(outputDir)
self.__checkForHMMER()
self.__checkForFastTree()
self.hmmDir = os.path.join(outputDir, 'phylo_hmms')
self.alignmentDir = os.path.join(outputDir, 'gene_alignments')
self.geneTreeDir = os.path.join(outputDir, 'gene_trees')
self.conspecificGeneTreeDir = os.path.join(outputDir,
'gene_trees_conspecific')
self.finalGeneTreeDir = os.path.join(outputDir, 'gene_trees_final')
self.consistencyOut = os.path.join(outputDir,
'genome_tree.consistency.tsv')
self.concatenatedAlignFile = os.path.join(
outputDir, 'genome_tree.concatenated.faa')
self.derepConcatenatedAlignFile = os.path.join(
outputDir, 'genome_tree.concatenated.derep.fasta')
self.treeOut = os.path.join(outputDir, 'genome_tree.tre')
self.treeOutAce = os.path.join(outputDir, 'genome_tree.ace_ids.tre')
self.treeRootedOut = os.path.join(outputDir, 'genome_tree.rooted.tre')
self.treeTaxonomyOut = os.path.join(outputDir,
'genome_tree.taxonomy.tre')
self.treeDerepOut = os.path.join(outputDir, 'genome_tree.derep.tre')
self.treeDerepRootedOut = os.path.join(outputDir,
'genome_tree.derep.rooted.tre')
self.treeDerepBootstrapOut = os.path.join(outputDir,
'genome_tree.derep.bs.tre')
self.treeDerepFinalOut = os.path.join(outputDir,
'genome_tree.final.tre')
self.taxonomyOut = os.path.join(outputDir, 'genome_tree.taxonomy.tsv')
self.treeMetadata = os.path.join(outputDir, 'genome_tree.metadata.tsv')
self.phyloHMMsOut = os.path.join(outputDir, 'phylo.hmm')
self.derepSeqFile = os.path.join(outputDir, 'genome_tree.derep.txt')
self.phyloUbiquity = 0.90
self.phyloSingleCopy = 0.90
self.paralogAcceptPer = 0.01
# self.consistencyAcceptPer = 0.95 # for trees at the class-level
self.consistencyAcceptPer = 0.906 # for trees at the phylum-level
self.consistencyMinTaxa = 20
# create output directories
os.makedirs(self.hmmDir)
os.makedirs(self.alignmentDir)
os.makedirs(self.geneTreeDir)
os.makedirs(self.conspecificGeneTreeDir)
os.makedirs(self.finalGeneTreeDir)
class ClassTree(object):
def __init__(self):
self.img = IMG()
def run(self):
tree = dendropy.Tree.get_from_path('../data/genome_tree/genome_tree_prok.refpkg/genome_tree.final.tre', schema='newick', as_rooted=True, preserve_underscores=True)
metadata = self.img.genomeMetadata()
# relabel taxa
for leaf in tree.leaf_nodes():
genomeId = leaf.taxon.label.replace('IMG_', '')
classT = metadata[genomeId]['taxonomy'][2]
newLeafLabel = classT + '_' + genomeId
leaf.taxon.label = newLeafLabel
# relabel internal nodes
for node in tree.internal_nodes():
uid, taxaStr, bootstrap = node.label.split('|')
if bootstrap:
node.label = uid + ':' + str(node.edge_length) + '[' + str(int(float(bootstrap)*100 + 0.5)) + ']'
else:
node.label = uid + ':' + str(node.edge_length)
# write out reduced tree
tree.write_to_path('./experiments/classTree.tre', schema='newick', suppress_rooting=True, suppress_edge_lengths=True, unquoted_underscores=True)
tree.write_to_path('./experiments/classTree_no_internal.tre', schema='newick', suppress_rooting=True, suppress_edge_lengths=True, unquoted_underscores=True, suppress_internal_node_labels=True)
class ClassTree(object):
def __init__(self):
self.img = IMG()
def run(self):
tree = dendropy.Tree.get_from_path('../data/genome_tree/genome_tree_prok.refpkg/genome_tree.final.tre', schema='newick', as_rooted=True, preserve_underscores=True)
metadata = self.img.genomeMetadata()
# relabel taxa
for leaf in tree.leaf_nodes():
genomeId = leaf.taxon.label.replace('IMG_', '')
classT = metadata[genomeId]['taxonomy'][2]
newLeafLabel = classT + '_' + genomeId
leaf.taxon.label = newLeafLabel
# relabel internal nodes
for node in tree.internal_nodes():
uid, taxaStr, bootstrap = node.label.split('|')
if bootstrap:
node.label = uid + ':' + str(node.edge_length) + '[' + str(int(float(bootstrap)*100 + 0.5)) + ']'
else:
node.label = uid + ':' + str(node.edge_length)
# write out reduced tree
tree.write_to_path('./experiments/classTree.tre', schema='newick', suppress_rooting=True, suppress_edge_lengths=True, unquoted_underscores=True)
tree.write_to_path('./experiments/classTree_no_internal.tre', schema='newick', suppress_rooting=True, suppress_edge_lengths=True, unquoted_underscores=True, suppress_internal_node_labels=True)
def run(self, outputFile):
img = IMG()
print('Identifying all IMG prokaryotic genomes with valid data.')
metadata = img.genomeMetadata()
genomeIds = img.genomeIdsByTaxonomy('prokaryotes', metadata)
genomeMissingData = img.genomesWithMissingData(genomeIds)
genomeIds -= genomeMissingData
print(' Identified %d valid genomes.' % (len(genomeIds)))
print('Calculating gene copy number for each genome.')
countTable = img.geneCountTable(genomeIds)
counts = []
for _, count in countTable['pfam00318'].iteritems():
counts.append(count)
print(len(genomeIds))
print(len(counts))
print(mean(counts))
fout = open(outputFile, 'w')
fout.write(str(countTable))
fout.close()
print('Gene count dictionary to: ' + outputFile)
def __init__(self, outputDir):
self.img = IMG()
self.markerSetBuilder = MarkerSetBuilder()
if os.path.exists(outputDir):
print '[Error] Output directory already exists: ' + outputDir
sys.exit(0)
else:
os.makedirs(outputDir)
self.__checkForHMMER()
self.__checkForFastTree()
self.hmmDir = os.path.join(outputDir, 'phylo_hmms')
self.alignmentDir = os.path.join(outputDir, 'gene_alignments')
self.geneTreeDir = os.path.join(outputDir, 'gene_trees')
self.conspecificGeneTreeDir = os.path.join(outputDir, 'gene_trees_conspecific')
self.finalGeneTreeDir = os.path.join(outputDir, 'gene_trees_final')
self.consistencyOut = os.path.join(outputDir, 'genome_tree.consistency.tsv')
self.concatenatedAlignFile = os.path.join(outputDir, 'genome_tree.concatenated.faa')
self.derepConcatenatedAlignFile = os.path.join(outputDir, 'genome_tree.concatenated.derep.fasta')
self.treeOut = os.path.join(outputDir, 'genome_tree.tre')
self.treeOutAce = os.path.join(outputDir, 'genome_tree.ace_ids.tre')
self.treeRootedOut = os.path.join(outputDir, 'genome_tree.rooted.tre')
self.treeTaxonomyOut = os.path.join(outputDir, 'genome_tree.taxonomy.tre')
self.treeDerepOut = os.path.join(outputDir, 'genome_tree.derep.tre')
self.treeDerepRootedOut = os.path.join(outputDir, 'genome_tree.derep.rooted.tre')
self.treeDerepBootstrapOut = os.path.join(outputDir, 'genome_tree.derep.bs.tre')
self.treeDerepFinalOut = os.path.join(outputDir, 'genome_tree.final.tre')
self.taxonomyOut = os.path.join(outputDir, 'genome_tree.taxonomy.tsv')
self.treeMetadata = os.path.join(outputDir, 'genome_tree.metadata.tsv')
self.phyloHMMsOut = os.path.join(outputDir, 'phylo.hmm')
self.derepSeqFile = os.path.join(outputDir, 'genome_tree.derep.txt')
self.phyloUbiquity = 0.90
self.phyloSingleCopy = 0.90
self.paralogAcceptPer = 0.01
#self.consistencyAcceptPer = 0.95 # for trees at the class-level
self.consistencyAcceptPer = 0.906 # for trees at the phylum-level
self.consistencyMinTaxa = 20
# create output directories
os.makedirs(self.hmmDir)
os.makedirs(self.alignmentDir)
os.makedirs(self.geneTreeDir)
os.makedirs(self.conspecificGeneTreeDir)
os.makedirs(self.finalGeneTreeDir)
class MarkerSetStabilityTest(object):
def __init__(self):
self.img = IMG()
self.markerset = MarkerSet()
def __processLineage(self, metadata, ubiquityThreshold,
singleCopyThreshold, minGenomes, queueIn, queueOut):
"""Assess stability of marker set for a specific named taxonomic group."""
while True:
lineage = queueIn.get(block=True, timeout=None)
if lineage == None:
break
genomeIds = self.img.genomeIdsByTaxonomy(lineage, metadata,
'trusted')
markerGenes = []
perChange = []
numGenomesToSelect = int(0.9 * len(genomeIds))
if len(genomeIds) >= minGenomes:
# calculate marker set for all genomes in lineage
geneCountTable = self.img.geneCountTable(genomeIds)
markerGenes = self.markerset.markerGenes(
genomeIds, geneCountTable,
ubiquityThreshold * len(genomeIds),
singleCopyThreshold * len(genomeIds))
tigrToRemove = self.img.identifyRedundantTIGRFAMs(markerGenes)
markerGenes = markerGenes - tigrToRemove
for _ in range(0, 100):
# calculate marker set for subset of genomes
subsetGenomeIds = random.sample(genomeIds,
numGenomesToSelect)
geneCountTable = self.img.geneCountTable(subsetGenomeIds)
subsetMarkerGenes = self.markerset.markerGenes(
subsetGenomeIds, geneCountTable,
ubiquityThreshold * numGenomesToSelect,
singleCopyThreshold * numGenomesToSelect)
tigrToRemove = self.img.identifyRedundantTIGRFAMs(
subsetMarkerGenes)
subsetMarkerGenes = subsetMarkerGenes - tigrToRemove
perChange.append(
float(
len(
markerGenes.symmetric_difference(
subsetMarkerGenes))) * 100.0 /
len(markerGenes))
if perChange != []:
queueOut.put(
(lineage, len(genomeIds), len(markerGenes),
numGenomesToSelect, mean(perChange), std(perChange)))
else:
queueOut.put((lineage, len(genomeIds), len(markerGenes),
numGenomesToSelect, -1, -1))
def __storeResults(self, outputFile, totalLineages, writerQueue):
"""Store results to file."""
fout = open(outputFile, 'w')
fout.write(
'Lineage\t# genomes\t# markers\t# sampled genomes\tmean % change\tstd % change\n'
)
numProcessedLineages = 0
while True:
lineage, numGenomes, numMarkerGenes, numSampledGenomes, meanPerChange, stdPerChange = writerQueue.get(
block=True, timeout=None)
if lineage == None:
break
numProcessedLineages += 1
statusStr = ' Finished processing %d of %d (%.2f%%) lineages.' % (
numProcessedLineages, totalLineages,
float(numProcessedLineages) * 100 / totalLineages)
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
fout.write('%s\t%d\t%d\t%d\t%f\t%f\n' %
(lineage, numGenomes, numMarkerGenes, numSampledGenomes,
meanPerChange, stdPerChange))
sys.stdout.write('\n')
fout.close()
def run(self, outputFile, ubiquityThreshold, singleCopyThreshold,
minGenomes, mostSpecificRank, numThreads):
"""Calculate stability of marker sets for named taxonomic groups."""
print(' Testing stability of marker sets:')
random.seed(1)
# process each sequence in parallel
workerQueue = mp.Queue()
writerQueue = mp.Queue()
metadata = self.img.genomeMetadata()
lineages = self.img.lineagesByCriteria(metadata, minGenomes,
mostSpecificRank)
for lineage in lineages:
workerQueue.put(lineage)
for _ in range(numThreads):
workerQueue.put(None)
calcProc = [
mp.Process(target=self.__processLineage,
args=(metadata, ubiquityThreshold, singleCopyThreshold,
minGenomes, workerQueue, writerQueue))
for _ in range(numThreads)
]
writeProc = mp.Process(target=self.__storeResults,
args=(outputFile, len(lineages), writerQueue))
writeProc.start()
for p in calcProc:
p.start()
for p in calcProc:
p.join()
writerQueue.put((None, None, None, None, None, None))
writeProc.join()
def __init__(self):
self.img = IMG()
def __init__(self):
self.markerSetBuilder = MarkerSetBuilder()
self.img = IMG()
class Simulation(object):
def __init__(self):
self.markerSetBuilder = MarkerSetBuilder()
self.img = IMG()
self.simContigLen = 10000
def __selectMarkerSet(self, tree, internalNode, metadata, ubiquityThreshold, singleCopyThreshold, queueOut):
"""Select marker set for parent edge of specified internal node."""
# get genomes descendant from each child of the specified internal node
leaves = []
for child in internalNode.child_nodes():
genomeIds = set()
for leaf in child.leaf_nodes():
genomeId = leaf.taxon.label.replace('IMG_', '')
genomeIds.add(genomeId)
duplicateGenomes = self.markerSetBuilder.duplicateSeqs.get(leaf.taxon.label, [])
for dup in duplicateGenomes:
dupId = dup.replace('IMG_', '')
genomeIds.add(dupId)
leaves.append(genomeIds)
# make sure each set of leaves contains at least a minimum number of genomes
orderedLeaves = sorted(leaves, key=len)
if len(orderedLeaves[0]) < 5:
queueOut.put(('NA', -1, -1, -1, -1, -1))
return
# calculate marker genes with all genomes in lineage with the fewest genomes removed
binMarkerGenes, _ = self.markerSetBuilder.buildBinMarkerSet(tree, internalNode, ubiquityThreshold, singleCopyThreshold, bMarkerSet = False, genomeIdsToRemove = orderedLeaves[0])
# evaluate accuracy of completeness and contamination estimations on different partial genomes from lineage with fewest genomes
testGenomeIds = random.sample(orderedLeaves[0], min(len(orderedLeaves[0]), 100))
deltaComp = defaultdict(list)
deltaCont = defaultdict(list)
for testGenomeId in testGenomeIds:
geneDistTable = self.img.geneDistTable([testGenomeId], binMarkerGenes.getMarkerGenes(), spacingBetweenContigs=0)
genomeSize = readFastaBases(os.path.join(self.img.genomeDir, testGenomeId, testGenomeId + '.fna'))
repsPerGenome = 100
for _ in xrange(0, repsPerGenome):
testComp = random.uniform(0.5, 1.0)
testCont = random.uniform(0, 0.2)
trueComp, trueCont, startPartialGenomeContigs = self.markerSetBuilder.sampleGenome(genomeSize, testComp, testCont, self.simContigLen)
for ms in binMarkerGenes.markerSetIter():
containedMarkerGenes = self.markerSetBuilder.containedMarkerGenes(ms.getMarkerGenes(), geneDistTable[testGenomeId], startPartialGenomeContigs, self.simContigLen)
completeness, contamination = ms.genomeCheck(containedMarkerGenes, bIndividualMarkers=True)
if completeness == 0.0:
print ms.getMarkerGenes()
print geneDistTable[testGenomeId]
print startPartialGenomeContigs
print genomeSize
print '*****************' + testGenomeId
sys.exit()
deltaComp[ms.lineageStr].append(completeness - trueComp)
deltaCont[ms.lineageStr].append(contamination - trueCont)
# determine lineage-specific marker set with best average performance
curBest = 1000
bestUID = None
dCompBest = 0
dContBest = 0
for lineageStr in deltaComp:
dComp, dCont = mean(abs(array(deltaComp[lineageStr]))), mean(abs(array(deltaCont[lineageStr])))
if (dComp + dCont) < curBest:
dCompBest = dComp
dContBest = dCont
dCompStdBest = std(abs(array(deltaComp[lineageStr])))
dContStdBest = std(abs(array(deltaCont[lineageStr])))
bestUID = lineageStr.split('|')[0]
curBest = dComp + dCont
queueOut.put((internalNode, bestUID, dCompBest, dCompStdBest, dContBest, dContStdBest))
def __workerThread(self, tree, metadata, ubiquityThreshold, singleCopyThreshold, queueIn, queueOut):
"""Process each data item in parallel."""
while True:
internalNode = queueIn.get(block=True, timeout=None)
if internalNode == None:
break
self.__selectMarkerSet(tree, internalNode, metadata, ubiquityThreshold, singleCopyThreshold, queueOut)
def __writerThread(self, numInternalNodes, writerQueue):
"""Store or write results of worker threads in a single thread."""
fout = open('/tmp/simInferBestMarkerSet.tsv', 'w')
fout.write('Internal node ID\tMarker set ID\tmean % delta comp\tstd % delta comp\tmean % delta cont\tstd % delta cont\n')
itemsProcessed = 0
while True:
internalNode, bestUID, dCompBest, dCompStdBest, dContBest, dContStdBest = writerQueue.get(block=True, timeout=None)
if internalNode == None:
break
itemsProcessed += 1
statusStr = ' Finished processing %d of %d (%.2f%%) internal branches.' % (itemsProcessed, numInternalNodes, float(itemsProcessed)*100/(numInternalNodes))
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
if internalNode != 'NA':
fout.write(internalNode.label + '\t%s\t%.2f\t%.2f\t%.2f\t%.2f\n' % (bestUID, dCompBest, dCompStdBest, dContBest, dContStdBest))
fout.close()
sys.stdout.write('\n')
def run(self, ubiquityThreshold, singleCopyThreshold, numThreads):
random.seed(0)
print '\n Calculating global gene count table.'
metadata = self.img.genomeMetadata()
self.markerSetBuilder.globalGeneCountTable = self.img.geneCountTable(metadata.keys())
print '\n Reading reference genome tree.'
treeFile = os.path.join(os.path.dirname(sys.argv[0]), '..', 'data', 'genome_tree', 'genome_tree_prok.refpkg', 'genome_tree.final.tre')
tree = dendropy.Tree.get_from_path(treeFile, schema='newick', as_rooted=True, preserve_underscores=True)
print ' Evaluating %d internal nodes.' % len(tree.internal_nodes())
workerQueue = mp.Queue()
writerQueue = mp.Queue()
for internalNode in tree.internal_nodes():
if internalNode.parent_node != None:
workerQueue.put(internalNode)
for _ in range(numThreads):
workerQueue.put(None)
metadata = self.img.genomeMetadata()
workerProc = [mp.Process(target = self.__workerThread, args = (tree, metadata, ubiquityThreshold, singleCopyThreshold, workerQueue, writerQueue)) for _ in range(numThreads)]
writeProc = mp.Process(target = self.__writerThread, args = (len(tree.internal_nodes())-1, writerQueue))
writeProc.start()
for p in workerProc:
p.start()
for p in workerProc:
p.join()
writerQueue.put((None, None, None, None, None, None))
writeProc.join()
def run(self, geneTreeDir, treeExtension, consistencyThreshold, minTaxaForAverage, outputFile, outputDir):
# make sure output directory is empty
if not os.path.exists(outputDir):
os.makedirs(outputDir)
files = os.listdir(outputDir)
for f in files:
if os.path.isfile(os.path.join(outputDir, f)):
os.remove(os.path.join(outputDir, f))
# get TIGRFam info
descDict = {}
files = os.listdir('/srv/db/tigrfam/13.0/TIGRFAMs_13.0_INFO')
for f in files:
shortDesc = longDesc = ''
for line in open('/srv/db/tigrfam/13.0/TIGRFAMs_13.0_INFO/' + f):
lineSplit = line.split(' ')
if lineSplit[0] == 'AC':
acc = lineSplit[1].strip()
elif lineSplit[0] == 'DE':
shortDesc = lineSplit[1].strip()
elif lineSplit[0] == 'CC':
longDesc = lineSplit[1].strip()
descDict[acc] = [shortDesc, longDesc]
# get PFam info
for line in open('/srv/db/pfam/27/Pfam-A.clans.tsv'):
lineSplit = line.split('\t')
acc = lineSplit[0]
shortDesc = lineSplit[3]
longDesc = lineSplit[4].strip()
descDict[acc] = [shortDesc, longDesc]
# get IMG taxonomy
img = IMG()
metadata = img.genomeMetadata()
genomeIdToTaxonomy = {}
for genomeId, m in metadata.iteritems():
genomeIdToTaxonomy[genomeId] = m['taxonomy']
# perform analysis for each tree
treeFiles = os.listdir(geneTreeDir)
allResults = {}
allTaxa = [set([]), set([]), set([])]
taxaCounts = {}
avgConsistency = {}
for treeFile in treeFiles:
if not treeFile.endswith(treeExtension):
continue
print treeFile
tree = dendropy.Tree.get_from_path(os.path.join(geneTreeDir, treeFile), schema='newick', as_rooted=True, preserve_underscores=True)
domainConsistency = {}
phylaConsistency = {}
classConsistency = {}
consistencyDict = [domainConsistency, phylaConsistency, classConsistency]
# get abundance of taxa at different taxonomic ranks
totals = [{}, {}, {}]
leaves = tree.leaf_nodes()
print ' Number of leaves: ' + str(len(leaves))
totalValidLeaves = 0
for leaf in leaves:
genomeId = self.__genomeId(leaf.taxon.label)
if genomeId not in metadata:
print '[Error] Genome is missing metadata: ' + genomeId
sys.exit()
totalValidLeaves += 1
taxonomy = genomeIdToTaxonomy[genomeId]
for r in xrange(0, 3):
totals[r][taxonomy[r]] = totals[r].get(taxonomy[r], 0) + 1
consistencyDict[r][taxonomy[r]] = 0
allTaxa[r].add(taxonomy[r])
taxaCounts[treeFile] = [totalValidLeaves, totals[0].get('Bacteria', 0), totals[0].get('Archaea', 0)]
# find highest consistency nodes (congruent descendant taxa / (total taxa + incongruent descendant taxa))
internalNodes = tree.internal_nodes()
for node in internalNodes:
leaves = node.leaf_nodes()
for r in xrange(0, 3):
leafCounts = {}
for leaf in leaves:
genomeId = self.__genomeId(leaf.taxon.label)
taxonomy = genomeIdToTaxonomy[genomeId]
leafCounts[taxonomy[r]] = leafCounts.get(taxonomy[r], 0) + 1
# calculate consistency for node
for taxa in consistencyDict[r]:
totalTaxaCount = totals[r][taxa]
if totalTaxaCount <= 1 or taxa == 'unclassified':
consistencyDict[r][taxa] = 'N/A'
continue
taxaCount = leafCounts.get(taxa, 0)
incongruentTaxa = len(leaves) - taxaCount
c = float(taxaCount) / (totalTaxaCount + incongruentTaxa)
if c > consistencyDict[r][taxa]:
consistencyDict[r][taxa] = c
# consider clan in other direction since the trees are unrooted
taxaCount = totalTaxaCount - leafCounts.get(taxa, 0)
incongruentTaxa = totalValidLeaves - len(leaves) - taxaCount
c = float(taxaCount) / (totalTaxaCount + incongruentTaxa)
if c > consistencyDict[r][taxa]:
consistencyDict[r][taxa] = c
# write results
consistencyDir = os.path.join(outputDir, 'consistency')
if not os.path.exists(consistencyDir):
os.makedirs(consistencyDir)
fout = open(os.path.join(consistencyDir, treeFile + '.results.tsv'), 'w')
fout.write('Tree')
for r in xrange(0, 3):
for taxa in sorted(consistencyDict[r].keys()):
fout.write('\t' + taxa)
fout.write('\n')
fout.write(treeFile)
for r in xrange(0, 3):
for taxa in sorted(consistencyDict[r].keys()):
if consistencyDict[r][taxa] != 'N/A':
fout.write('\t%.2f' % (consistencyDict[r][taxa]*100))
else:
fout.write('\tN/A')
fout.close()
# calculate average consistency at each taxonomic rank
average = []
for r in xrange(0, 3):
sumConsistency = []
for taxa in consistencyDict[r]:
if totals[r][taxa] > minTaxaForAverage and consistencyDict[r][taxa] != 'N/A':
sumConsistency.append(consistencyDict[r][taxa])
if len(sumConsistency) > 0:
average.append(sum(sumConsistency) / len(sumConsistency))
else:
average.append(0)
avgConsistency[treeFile] = average
allResults[treeFile] = consistencyDict
print ' Average consistency: ' + str(average) + ', mean = %.2f' % (sum(average)/len(average))
print ''
# print out combined results
fout = open(outputFile, 'w')
fout.write('Tree\tShort Desc.\tLong Desc.\tAlignment Length\t# Taxa\t# Bacteria\t# Archaea\tAvg. Consistency\tAvg. Domain Consistency\tAvg. Phylum Consistency\tAvg. Class Consistency')
for r in xrange(0, 3):
for t in sorted(allTaxa[r]):
fout.write('\t' + t)
fout.write('\n')
filteredGeneTrees = 0
retainedGeneTrees = 0
for treeFile in sorted(allResults.keys()):
consistencyDict = allResults[treeFile]
treeId = treeFile[0:treeFile.find('.')].replace('pfam', 'PF')
fout.write(treeId + '\t' + descDict[treeId][0] + '\t' + descDict[treeId][1])
# Taxa count
fout.write('\t' + str(taxaCounts[treeFile][0]) + '\t' + str(taxaCounts[treeFile][1]) + '\t' + str(taxaCounts[treeFile][2]))
avgCon = 0
for r in xrange(0, 3):
avgCon += avgConsistency[treeFile][r]
avgCon /= 3
fout.write('\t' + str(avgCon))
if avgCon >= consistencyThreshold:
retainedGeneTrees += 1
os.system('cp ' + os.path.join(geneTreeDir, treeFile) + ' ' + os.path.join(outputDir, treeFile))
else:
filteredGeneTrees += 1
print 'Filtered % s with an average consistency of %.4f.' % (treeFile, avgCon)
for r in xrange(0, 3):
fout.write('\t' + str(avgConsistency[treeFile][r]))
for r in xrange(0, 3):
for t in sorted(allTaxa[r]):
if t in consistencyDict[r]:
if consistencyDict[r][t] != 'N/A':
fout.write('\t%.2f' % (consistencyDict[r][t]*100))
else:
fout.write('\tN/A')
else:
fout.write('\tN/A')
fout.write('\n')
fout.close()
print 'Retained gene trees: ' + str(retainedGeneTrees)
print 'Filtered gene trees: ' + str(filteredGeneTrees)
def __init__(self):
self.img = IMG()
self.markerset = MarkerSet()
class DecorateTree(object):
def __init__(self):
self.img = IMG()
self.markerSetBuilder = MarkerSetBuilder()
def __meanStd(self, metadata, genomeIds, category):
values = []
for genomeId in genomeIds:
genomeId = genomeId.replace('IMG_', '')
v = metadata[genomeId][category]
if v != 'NA':
values.append(v)
return mean(values), std(values)
def __calculateMarkerSet(self, genomeLabels, ubiquityThreshold = 0.97, singleCopyThreshold = 0.97):
"""Calculate marker set for a set of genomes."""
# get genome IDs from genome labels
genomeIds = set()
for genomeLabel in genomeLabels:
genomeIds.add(genomeLabel.replace('IMG_', ''))
markerSet = self.markerSetBuilder.buildMarkerSet(genomeIds, ubiquityThreshold, singleCopyThreshold)
return markerSet.markerSet
def __pfamIdToPfamAcc(self, img):
pfamIdToPfamAcc = {}
for line in open(img.pfamHMMs):
if 'ACC' in line:
acc = line.split()[1].strip()
pfamId = acc.split('.')[0]
pfamIdToPfamAcc[pfamId] = acc
return pfamIdToPfamAcc
def decorate(self, taxaTreeFile, derepFile, inputTreeFile, metadataOut, numThreads):
# read genome metadata
print ' Reading metadata.'
metadata = self.img.genomeMetadata()
# read list of taxa with duplicate sequences
print ' Read list of taxa with duplicate sequences.'
duplicateTaxa = {}
for line in open(derepFile):
lineSplit = line.rstrip().split()
if len(lineSplit) > 1:
duplicateTaxa[lineSplit[0]] = lineSplit[1:]
# build gene count table
print ' Building gene count table.'
genomeIds = self.img.genomeMetadata().keys()
print ' # trusted genomes = ' + str(len(genomeIds))
# calculate statistics for each internal node using multiple threads
print ' Calculating statistics for each internal node.'
self.__internalNodeStatistics(taxaTreeFile, inputTreeFile, duplicateTaxa, metadata, metadataOut, numThreads)
def __internalNodeStatistics(self, taxaTreeFile, inputTreeFile, duplicateTaxa, metadata, metadataOut, numThreads):
# determine HMM model accession numbers
pfamIdToPfamAcc = self.__pfamIdToPfamAcc(self.img)
taxaTree = dendropy.Tree.get_from_path(taxaTreeFile, schema='newick', as_rooted=True, preserve_underscores=True)
inputTree = dendropy.Tree.get_from_path(inputTreeFile, schema='newick', as_rooted=True, preserve_underscores=True)
workerQueue = mp.Queue()
writerQueue = mp.Queue()
uniqueId = 0
for node in inputTree.internal_nodes():
uniqueId += 1
workerQueue.put((uniqueId, node))
for _ in range(numThreads):
workerQueue.put((None, None))
calcProc = [mp.Process(target = self.__processInternalNode, args = (taxaTree, duplicateTaxa, workerQueue, writerQueue)) for _ in range(numThreads)]
writeProc = mp.Process(target = self.__reportStatistics, args = (metadata, metadataOut, inputTree, inputTreeFile, pfamIdToPfamAcc, writerQueue))
writeProc.start()
for p in calcProc:
p.start()
for p in calcProc:
p.join()
writerQueue.put((None, None, None, None, None, None, None))
writeProc.join()
def __processInternalNode(self, taxaTree, duplicateTaxa, queueIn, queueOut):
"""Run each marker gene in a separate thread."""
while True:
uniqueId, node = queueIn.get(block=True, timeout=None)
if uniqueId == None:
break
# find corresponding internal node in taxa tree
labels = []
for leaf in node.leaf_nodes():
labels.append(leaf.taxon.label)
if leaf.taxon.label in duplicateTaxa:
for genomeId in duplicateTaxa[leaf.taxon.label]:
labels.append(genomeId)
# check if there is a taxonomic label
mrca = taxaTree.mrca(taxon_labels = labels)
taxaStr = ''
if mrca.label:
taxaStr = mrca.label.replace(' ', '')
# give node a unique Id while retraining bootstrap value
bootstrap = ''
if node.label:
bootstrap = node.label
nodeLabel = 'UID' + str(uniqueId) + '|' + taxaStr + '|' + bootstrap
# calculate marker set
markerSet = self.__calculateMarkerSet(labels)
queueOut.put((uniqueId, labels, markerSet, taxaStr, bootstrap, node.oid, nodeLabel))
def __reportStatistics(self, metadata, metadataOut, inputTree, inputTreeFile, pfamIdToPfamAcc, writerQueue):
"""Store statistics for internal node."""
fout = open(metadataOut, 'w')
fout.write('UID\t# genomes\tTaxonomy\tBootstrap')
fout.write('\tGC mean\tGC std')
fout.write('\tGenome size mean\tGenome size std')
fout.write('\tGene count mean\tGene count std')
fout.write('\tMarker set')
fout.write('\n')
numProcessedNodes = 0
numInternalNodes = len(inputTree.internal_nodes())
while True:
uniqueId, labels, markerSet, taxaStr, bootstrap, nodeID, nodeLabel = writerQueue.get(block=True, timeout=None)
if uniqueId == None:
break
numProcessedNodes += 1
statusStr = ' Finished processing %d of %d (%.2f%%) internal nodes.' % (numProcessedNodes, numInternalNodes, float(numProcessedNodes)*100/numInternalNodes)
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
fout.write('UID' + str(uniqueId) + '\t' + str(len(labels)) + '\t' + taxaStr + '\t' + bootstrap)
m, s = self.__meanStd(metadata, labels, 'GC %')
fout.write('\t' + str(m*100) + '\t' + str(s*100))
m, s = self.__meanStd(metadata, labels, 'genome size')
fout.write('\t' + str(m) + '\t' + str(s))
m, s = self.__meanStd(metadata, labels, 'gene count')
fout.write('\t' + str(m) + '\t' + str(s))
# change model names to accession numbers, and make
# sure there is an HMM model for each PFAM
mungedMarkerSets = []
for geneSet in markerSet:
s = set()
for geneId in geneSet:
if 'pfam' in geneId:
pfamId = geneId.replace('pfam', 'PF')
if pfamId in pfamIdToPfamAcc:
s.add(pfamIdToPfamAcc[pfamId])
else:
s.add(geneId)
mungedMarkerSets.append(s)
fout.write('\t' + str(mungedMarkerSets))
fout.write('\n')
node = inputTree.find_node(filter_fn=lambda n: hasattr(n, 'oid') and n.oid == nodeID)
node.label = nodeLabel
sys.stdout.write('\n')
fout.close()
inputTree.write_to_path(inputTreeFile, schema='newick', suppress_rooting=True, unquoted_underscores=True)
def run(self, inputMetadataFile, outputMetadataFile, outputDir, ubiquityThreshold, singleCopyThreshold, trustedCompleteness, trustedContamination):
img = IMG()
markerSetBuilder = MarkerSetBuilder()
allOut = open(os.path.join(outputDir, 'genomes_all.tsv'), 'w')
allOut.write('Genome Id\tLineage\tGenome size (Mbps)\tScaffold count\tGene count\tCoding base count\tN50\tBiotic Relationship\tStatus\tCompleteness\tContamination\tMissing markers\tDuplicate markers\n')
trustedOut = open(os.path.join(outputDir, 'genomes_trusted.tsv'), 'w')
trustedOut.write('Genome Id\tLineage\tGenome size (Mbps)\tScaffold count\tGene count\tCoding base count\tN50\tBiotic Relationship\tStatus\tCompleteness\tContamination\tMissing markers\tDuplicate markers\n')
filteredOut = open(os.path.join(outputDir, 'genomes_filtered.tsv'), 'w')
filteredOut.write('Genome Id\tLineage\tGenome size (Mbps)\tScaffold count\tGene count\tCoding base count\tN50\tBiotic Relationship\tStatus\tCompleteness\tContamination\tMissing markers\tDuplicate markers\n')
metadataOut = open(outputMetadataFile, 'w')
# read input metadata file
metadata = img.genomeMetadataFromFile(inputMetadataFile)
finishedGenomes = defaultdict(set)
allGenomes = defaultdict(set)
metadataLine = {}
bHeader = True
for line in open(inputMetadataFile):
if bHeader:
metadataOut.write(line)
bHeader = False
continue
lineSplit = line.split('\t')
genomeId = lineSplit[0]
domain = lineSplit[1]
status = lineSplit[2]
if status == 'Finished':
finishedGenomes[domain].add(genomeId)
allGenomes[domain].add(genomeId)
metadataLine[genomeId] = line
allTrustedGenomeIds = set()
for lineage, allLineageGenomeIds in allGenomes.iteritems():
print '[' + lineage + ']'
print ' Number of genomes: %d' % len(allLineageGenomeIds)
# tabulate genomes from each phylum
allPhylumCounts = {}
for genomeId in allLineageGenomeIds:
taxon = metadata[genomeId]['taxonomy'][1]
allPhylumCounts[taxon] = allPhylumCounts.get(taxon, 0) + 1
# identify marker genes for finished genomes
print '\nDetermining initial marker gene sets for genome filtering.'
markerSet = markerSetBuilder.buildMarkerSet(finishedGenomes[lineage], ubiquityThreshold, singleCopyThreshold)
print ' Marker set consists of %s marker genes organized into %d sets.' % (markerSet.numMarkers(), markerSet.numSets())
fout = open(os.path.join(outputDir, 'trusted_marker_sets_' + lineage + '.txt'), 'w')
fout.write(str(markerSet.markerSet))
fout.close()
# identifying trusted genomes (highly complete, low contamination genomes)
print '\nIdentifying highly complete, low contamination genomes.'
trustedGenomeIds = set()
filteredGenomes = set()
retainedStatus = {}
filteredStatus = {}
geneCountTable = img.geneCountTable(allLineageGenomeIds)
for genomeId in allLineageGenomeIds:
completeness, contamination, missingMarkers, duplicateMarkers = markerSetBuilder.genomeCheck(markerSet.markerSet, genomeId, geneCountTable)
genomeStr = self.__genomeString(genomeId, metadata, completeness, contamination, missingMarkers, duplicateMarkers)
if completeness >= trustedCompleteness and contamination <= trustedContamination:
trustedGenomeIds.add(genomeId)
allTrustedGenomeIds.add(genomeId)
retainedStatus[metadata[genomeId]['status']] = retainedStatus.get(metadata[genomeId]['status'], 0) + 1
trustedOut.write(genomeStr)
allOut.write(genomeStr)
metadataOut.write(metadataLine[genomeId])
else:
filteredGenomes.add(genomeId)
filteredStatus[metadata[genomeId]['status']] = filteredStatus.get(metadata[genomeId]['status'], 0) + 1
filteredOut.write(genomeStr)
allOut.write(genomeStr)
print ' Filtered genomes: %d (%.2f%%)' % (len(filteredGenomes), len(filteredGenomes)*100.0 / len(allLineageGenomeIds))
print ' ' + str(filteredStatus)
print ' \nTrusted genomes: %d (%.2f%%)' % (len(trustedGenomeIds), len(trustedGenomeIds)*100.0 / len(allLineageGenomeIds))
print ' ' + str(retainedStatus)
# determine status of retained genomes
print '\nTrusted genomes by phylum:'
trustedPhylumCounts = {}
for genomeId in trustedGenomeIds:
taxon = metadata[genomeId]['taxonomy'][1]
trustedPhylumCounts[taxon] = trustedPhylumCounts.get(taxon, 0) + 1
for phylum, count in allPhylumCounts.iteritems():
print ' ' + phylum + ': %d of %d' % (trustedPhylumCounts.get(phylum, 0), count)
print ''
allOut.close()
trustedOut.close()
filteredOut.close()
metadataOut.close()
# write out lineage statistics for genome distribution
allStats = {}
trustedStats = {}
for r in xrange(0, 6): # Domain to Genus
for genomeId, data in metadata.iteritems():
taxaStr = ';'.join(data['taxonomy'][0:r+1])
allStats[taxaStr] = allStats.get(taxaStr, 0) + 1
if genomeId in allTrustedGenomeIds:
trustedStats[taxaStr] = trustedStats.get(taxaStr, 0) + 1
sortedLineages = img.lineagesSorted(metadata)
fout = open(os.path.join(outputDir, 'lineage_stats.tsv'), 'w')
fout.write('Lineage\tGenomes with metadata\tTrusted genomes\n')
for lineage in sortedLineages:
fout.write(lineage + '\t' + str(allStats.get(lineage, 0))+ '\t' + str(trustedStats.get(lineage, 0))+ '\n')
fout.close()
def __init__(self):
img = IMG()
self.metadata = img.genomeMetadata()
class GenomeTreeWorkflow(object):
def __init__(self, outputDir):
self.img = IMG()
self.markerSetBuilder = MarkerSetBuilder()
if os.path.exists(outputDir):
print '[Error] Output directory already exists: ' + outputDir
sys.exit(0)
else:
os.makedirs(outputDir)
self.__checkForHMMER()
self.__checkForFastTree()
self.hmmDir = os.path.join(outputDir, 'phylo_hmms')
self.alignmentDir = os.path.join(outputDir, 'gene_alignments')
self.geneTreeDir = os.path.join(outputDir, 'gene_trees')
self.conspecificGeneTreeDir = os.path.join(outputDir, 'gene_trees_conspecific')
self.finalGeneTreeDir = os.path.join(outputDir, 'gene_trees_final')
self.consistencyOut = os.path.join(outputDir, 'genome_tree.consistency.tsv')
self.concatenatedAlignFile = os.path.join(outputDir, 'genome_tree.concatenated.faa')
self.derepConcatenatedAlignFile = os.path.join(outputDir, 'genome_tree.concatenated.derep.fasta')
self.treeOut = os.path.join(outputDir, 'genome_tree.tre')
self.treeOutAce = os.path.join(outputDir, 'genome_tree.ace_ids.tre')
self.treeRootedOut = os.path.join(outputDir, 'genome_tree.rooted.tre')
self.treeTaxonomyOut = os.path.join(outputDir, 'genome_tree.taxonomy.tre')
self.treeDerepOut = os.path.join(outputDir, 'genome_tree.derep.tre')
self.treeDerepRootedOut = os.path.join(outputDir, 'genome_tree.derep.rooted.tre')
self.treeDerepBootstrapOut = os.path.join(outputDir, 'genome_tree.derep.bs.tre')
self.treeDerepFinalOut = os.path.join(outputDir, 'genome_tree.final.tre')
self.taxonomyOut = os.path.join(outputDir, 'genome_tree.taxonomy.tsv')
self.treeMetadata = os.path.join(outputDir, 'genome_tree.metadata.tsv')
self.phyloHMMsOut = os.path.join(outputDir, 'phylo.hmm')
self.derepSeqFile = os.path.join(outputDir, 'genome_tree.derep.txt')
self.phyloUbiquity = 0.90
self.phyloSingleCopy = 0.90
self.paralogAcceptPer = 0.01
#self.consistencyAcceptPer = 0.95 # for trees at the class-level
self.consistencyAcceptPer = 0.906 # for trees at the phylum-level
self.consistencyMinTaxa = 20
# create output directories
os.makedirs(self.hmmDir)
os.makedirs(self.alignmentDir)
os.makedirs(self.geneTreeDir)
os.makedirs(self.conspecificGeneTreeDir)
os.makedirs(self.finalGeneTreeDir)
def __checkForHMMER(self):
"""Check to see if HMMER is on the system path."""
try:
exit_status = os.system('hmmfetch -h > /dev/null')
except:
print "Unexpected error!", sys.exc_info()[0]
raise
if exit_status != 0:
print "[Error] hmmfetch is not on the system path"
sys.exit()
def __checkForFastTree(self):
"""Check to see if FastTree is on the system path."""
try:
exit_status = os.system('FastTree 2> /dev/null')
except:
print "Unexpected error!", sys.exc_info()[0]
raise
if exit_status != 0:
print "[Error] FastTree is not on the system path"
sys.exit()
def __genesInGenomes(self, genomeIds):
genesInGenomes = {}
for genomeId in genomeIds:
markerIdToGeneIds = defaultdict(set)
for line in open(os.path.join(IMG.genomeDir, genomeId, genomeId + IMG.pfamExtension)):
lineSplit = line.split('\t')
markerIdToGeneIds[lineSplit[8]].add(lineSplit[0])
for line in open(os.path.join(IMG.genomeDir, genomeId, genomeId + IMG.tigrExtension)):
lineSplit = line.split('\t')
markerIdToGeneIds[lineSplit[6]].add(lineSplit[0])
genesInGenomes[genomeId] = markerIdToGeneIds
return genesInGenomes
def __fetchMarkerModels(self, universalMarkerGenes, outputModelDir):
markerIdToName = {}
for line in open('/srv/whitlam/bio/db/pfam/27/Pfam-A.hmm'):
if 'NAME' in line:
name = line.split()[1].rstrip()
elif 'ACC' in line:
acc = line.split()[1].rstrip()
markerId = acc.replace('PF', 'pfam')
markerId = markerId[0:markerId.rfind('.')]
markerIdToName[markerId] = name
for markerId in universalMarkerGenes:
if 'pfam' in markerId:
os.system('hmmfetch /srv/whitlam/bio/db/pfam/27/Pfam-A.hmm ' + markerIdToName[markerId] + ' > ' + os.path.join(outputModelDir, markerId.replace('pfam', 'PF') + '.hmm'))
else:
os.system('hmmfetch /srv/whitlam/bio/db/tigrfam/13.0/' + markerId + '.HMM ' + markerId + ' > ' + os.path.join(outputModelDir, markerId + '.hmm'))
def __alignMarkers(self, genomeIds, markerGenes, genesInGenomes, numThreads, outputGeneDir, outputModelDir):
"""Perform multithreaded alignment of marker genes using HMM align."""
workerQueue = mp.Queue()
writerQueue = mp.Queue()
for _, markerId in enumerate(markerGenes):
workerQueue.put(markerId)
for _ in range(numThreads):
workerQueue.put(None)
calcProc = [mp.Process(target = self.__runHmmAlign, args = (genomeIds, genesInGenomes, outputGeneDir, outputModelDir, workerQueue, writerQueue)) for _ in range(numThreads)]
writeProc = mp.Process(target = self.__reportThreads, args = (len(markerGenes), writerQueue))
writeProc.start()
for p in calcProc:
p.start()
for p in calcProc:
p.join()
writerQueue.put(None)
writeProc.join()
def __runHmmAlign(self, genomeIds, genesInGenomes, outputGeneDir, outputModelDir, queueIn, queueOut):
"""Run each marker gene in a separate thread."""
while True:
markerId = queueIn.get(block=True, timeout=None)
if markerId == None:
break
modelName = markerId
if modelName.startswith('pfam'):
modelName = modelName.replace('pfam', 'PF')
markerSeqFile = os.path.join(outputGeneDir, modelName + '.faa')
fout = open(markerSeqFile, 'w')
for genomeId in genomeIds:
seqs = readFasta(IMG.genomeDir + '/' + genomeId + '/' + genomeId + '.genes.faa')
for geneId in genesInGenomes[genomeId].get(markerId, []):
if geneId not in seqs:
# this shouldn't be necessary, but the IMG metadata isn't always
# perfectly in sync with the sequence data
continue
fout.write('>' + genomeId + '|' + geneId + '\n')
fout.write(seqs[geneId] + '\n')
fout.close()
hmmer = HMMERRunner('align')
hmmer.align(os.path.join(outputModelDir, modelName + '.hmm'), markerSeqFile, os.path.join(outputGeneDir, modelName + '.aln.faa'), trim=False, outputFormat='Pfam')
self.__maskAlignment(os.path.join(outputGeneDir, modelName + '.aln.faa'), os.path.join(outputGeneDir, modelName + '.aln.masked.faa'))
queueOut.put(modelName)
def __reportThreads(self, numGenes, writerQueue):
"""Store confidence intervals (i.e., to shared memory)."""
numProcessedGenes = 0
while True:
markerId = writerQueue.get(block=True, timeout=None)
if markerId == None:
break
numProcessedGenes += 1
statusStr = ' Finished processing %d of %d (%.2f%%) marker genes.' % (numProcessedGenes, numGenes, float(numProcessedGenes)*100/numGenes)
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
sys.stdout.write('\n')
def __maskAlignment(self, inputFile, outputFile):
"""Read HMMER alignment in STOCKHOLM format and output masked alignment in FASTA format."""
# read STOCKHOLM alignment
seqs = {}
for line in open(inputFile):
line = line.rstrip()
if line == '' or line[0] == '#' or line == '//':
if 'GC RF' in line:
mask = line.split('GC RF')[1].strip()
continue
else:
lineSplit = line.split()
seqs[lineSplit[0]] = lineSplit[1].upper().replace('.', '-').strip()
# output masked sequences in FASTA format
fout = open(outputFile, 'w')
for seqId, seq in seqs.iteritems():
fout.write('>' + seqId + '\n')
maskedSeq = ''.join([seq[i] for i in xrange(0, len(seq)) if mask[i] == 'x'])
fout.write(maskedSeq + '\n')
fout.close()
def __taxonomicMarkers(self, taxaStr, metadata, ubiquityThreshold, singleCopyThreshold):
"""Get genomes and marker genes for a specific lineage."""
genomeIds = self.img.genomeIdsByTaxonomy(taxaStr, metadata)
# hack to add in other genomes with incorrect taxonomy
aceIds = set()
for line in open('./taxonomicTrees/firmicutes.nds'):
aceIds.add(line.strip())
aceIdsToImgIds = self.aceIdsToImgIds()
for aceId in aceIds:
if aceId in aceIdsToImgIds:
genomeId = aceIdsToImgIds[aceId]
if genomeId in metadata:
genomeIds.add(genomeId)
markerGenes = self.markerSetBuilder.buildMarkerGenes(genomeIds, ubiquityThreshold, singleCopyThreshold )
return genomeIds, markerGenes
def inferGeneTrees(self, phyloUbiquityThreshold, phyloSingleCopyThreshold, numThreads, outputGeneDir, outputModelDir, outgroupSize):
# make sure output directory is empty
if not os.path.exists(outputGeneDir):
os.makedirs(outputGeneDir)
if not os.path.exists(outputModelDir):
os.makedirs(outputModelDir)
files = os.listdir(outputGeneDir)
for f in files:
os.remove(os.path.join(outputGeneDir, f))
# get genomes and marker genes for taxonomic groups of interest
print ''
print 'Identifying genomes and marker genes of interest:'
metadata = self.img.genomeMetadata()
ingroupGenomeIds, ingroupMarkers = self.__taxonomicMarkers('Bacteria;Firmicutes', metadata, phyloUbiquityThreshold, phyloSingleCopyThreshold)
outgroupGenomeIds = self.img.genomeIdsByTaxonomy('Bacteria;Coprothermobacter', metadata)
#alphaGenomeIds, _ = self.__taxonomicMarkers('Bacteria;Proteobacteria;Alphaproteobacteria', metadata, phyloUbiquityThreshold, phyloSingleCopyThreshold)
print ' Identified ingroup genomes: %d' % len(ingroupGenomeIds)
print ' Identified outgroup genomes: %d' % len(outgroupGenomeIds)
numOutgroupTaxa = min(outgroupSize, len(outgroupGenomeIds))
print ''
print ' Selecting %d taxa from the outgroup.' % (numOutgroupTaxa)
genomeIds = ingroupGenomeIds.union(random.sample(outgroupGenomeIds, numOutgroupTaxa))
self.imgIdsToAceIds(genomeIds)
print ' Identified markers: %d' % len(ingroupMarkers)
# get mapping of marker ids to gene ids for each genome
print ' Determine genes for genomes of interest.'
genesInGenomes = self.__genesInGenomes(genomeIds)
# get HMM for each marker gene
print ' Fetching HMM for each marker genes.'
self.__fetchMarkerModels(ingroupMarkers, outputModelDir)
# align gene sequences and infer gene trees
print ' Aligning marker genes:'
self.__alignMarkers(genomeIds, ingroupMarkers, genesInGenomes, numThreads, outputGeneDir, outputModelDir)
return genomeIds
def imgIdsToAceIds(self, imgIds):
imgIdToAceId = {}
for line in open('ggg_tax_img.feb_2014.txt'):
lineSplit = line.split('\t')
imgIdToAceId[lineSplit[1].rstrip()] = lineSplit[0]
missing = 0
for imgId in imgIds:
if imgId not in imgIdToAceId:
missing += 1
print ' Number of genomes without an ACE id: ' + str(missing)
return imgIdToAceId
def aceIdsToImgIds(self):
aceIdsToImgIds = {}
for line in open('ggg_tax_img.feb_2014.txt'):
lineSplit = line.split('\t')
aceIdsToImgIds[lineSplit[0].strip()] = lineSplit[1].strip()
return aceIdsToImgIds
def run(self, numThreads, outgroupSize):
# identify genes suitable for phylogenetic inference
print '--- Identifying genes suitable for phylogenetic inference ---'
genomeIds = self.inferGeneTrees(self.phyloUbiquity, self.phyloSingleCopy, numThreads, self.alignmentDir, self.hmmDir, outgroupSize)
# infer gene trees
print ''
print '--- Inferring gene trees ---'
makeTrees = MakeTrees()
makeTrees.run(self.alignmentDir, self.geneTreeDir, '.aln.masked.faa', numThreads)
# test gene trees for paralogs
print ''
print '--- Testing for paralogs in gene trees ---'
paralogTest = ParalogTest()
paralogTest.run(self.geneTreeDir, self.paralogAcceptPer, '.tre', self.conspecificGeneTreeDir)
# test gene trees for consistency with IMG taxonomy
print ''
print '--- Testing taxonomic consistency of gene trees ---'
consistencyTest = ConsistencyTest()
consistencyTest.run(self.conspecificGeneTreeDir, '.tre', self.consistencyAcceptPer, self.consistencyMinTaxa, self.consistencyOut, self.finalGeneTreeDir)
# gather phylogenetically informative HMMs into a single model file
print ''
print '--- Gathering phylogenetically informative HMMs ---'
getPhylogeneticHMMs = GetPhylogeneticHMMs()
getPhylogeneticHMMs.run(self.hmmDir, self.finalGeneTreeDir, self.phyloHMMsOut)
# infer genome tree
print ''
print '--- Inferring full genome tree ---'
inferGenomeTree = InferGenomeTree()
inferGenomeTree.run(self.finalGeneTreeDir, self.alignmentDir, '.aln.masked.faa', self.concatenatedAlignFile, self.treeOut, self.taxonomyOut, bSupportValues = True)
# replace IMG identifiers with ACE identifiers
imgIdToAceId = self.imgIdsToAceIds(genomeIds)
with open(self.treeOut) as f:
tree = ''.join(f.readlines())
for genomeId in genomeIds:
if genomeId in imgIdToAceId:
tree = tree.replace('IMG_' + genomeId, imgIdToAceId[genomeId])
fout = open(self.treeOutAce, 'w')
fout.write(tree)
fout.close()
def __init__(self):
self.markerSetBuilder = MarkerSetBuilder()
self.img = IMG()
class Simulation(object):
def __init__(self):
self.markerSetBuilder = MarkerSetBuilder()
self.img = IMG()
def run(self):
print('\n Reading reference genome tree.')
treeFile = os.path.join(os.path.dirname(sys.argv[0]), '..', 'data',
'genome_tree', 'genome_tree_prok.refpkg',
'genome_tree.final.tre')
tree = dendropy.Tree.get_from_path(treeFile,
schema='newick',
as_rooted=True,
preserve_underscores=True)
# get all Finished, Trusted genomes
metadata = self.img.genomeMetadata()
bacteriaIds = self.img.getGenomesByClade('domain', 'Bacteria',
metadata)
print('# Bacteria: %d' % len(bacteriaIds))
start = time.time()
#self.markerSetBuilder.cachedGeneCountTable = self.img.geneCountTable(metadata.keys())
end = time.time()
print('globalGeneCountTable: %.2f' % (end - start))
start = time.time()
#self.markerSetBuilder.precomputeGenomeSeqLens(metadata.keys())
end = time.time()
print('precomputeGenomeSeqLens: %.2f' % (end - start))
start = time.time()
#self.markerSetBuilder.precomputeGenomeFamilyPositions(metadata.keys(), 5000)
end = time.time()
print('precomputeGenomeFamilyPositions: %.2f' % (end - start))
#start = time.time()
#test = self.img.geneDistTable(metadata.keys(), self.markerSetBuilder.globalGeneCountTable.keys(), spacingBetweenContigs=1e6)
#end = time.time()
#print 'geneDistTable: %.2f' % (end - start)
#t = raw_input('waiting...')
start = time.time()
#testGenomeId = archaeaIds.pop()
#testNode = tree.find_node_with_taxon_label('IMG_' + testGenomeId)
#binMarkerSets, refinedBinMarkerSet = self.markerSetBuilder.buildBinMarkerSet(tree, testNode.parent_node, 0.97, 0.97, bMarkerSet = True)
markerSet = self.markerSetBuilder.buildMarkerSet(
bacteriaIds, 0.97, 0.97)
end = time.time()
print('buildMarkerSet: %.2f' % (end - start))
print(len(markerSet.markerSet))
test = eval(
"[set(['TIGR01080', 'pfam08071', 'pfam00900']), set(['pfam08069', 'pfam00312']), set(['pfam00276', 'pfam00573', 'pfam00297']), set(['pfam00333', 'pfam00327', 'pfam03719']), set(['pfam04563', 'pfam04560', 'pfam04983', 'pfam04566', 'pfam04567', 'pfam04565', 'pfam04997', 'pfam00562', 'pfam05000', 'pfam00623', 'pfam01191', 'pfam04561', 'TIGR02389']), set(['pfam00831', 'pfam01868', 'pfam00189', 'pfam00237']), set(['pfam01280', 'pfam00861', 'pfam01655']), set(['pfam01194', 'pfam00380', 'pfam00572']), set(['TIGR00425', 'pfam08068']), set(['pfam01157', 'pfam03874']), set(['pfam00416', 'TIGR01018']), set(['pfam00679', 'pfam03764']), set(['TIGR00344', 'TIGR03683']), set(['pfam01193', 'pfam01000']), set(['pfam00750', 'pfam05746']), set(['pfam00935', 'pfam01667']), set(['pfam00867', 'pfam00752']), set(['pfam01172', 'pfam09377']), set(['pfam03950', 'pfam00749']), set(['pfam00181', 'pfam03947']), set(['pfam00687', 'pfam00466']), set(['TIGR03679', 'TIGR00289']), set(['pfam01198', 'pfam01912']), set(['pfam00673', 'pfam00281']), set(['TIGR00134']), set(['pfam00410']), set(['pfam00411']), set(['pfam01090']), set(['pfam01092']), set(['pfam04919']), set(['TIGR00336']), set(['pfam01864']), set(['TIGR00442']), set(['pfam01866']), set(['pfam01780']), set(['TIGR01046']), set(['pfam00318']), set(['pfam00252']), set(['pfam09173']), set(['pfam00238']), set(['pfam01798']), set(['pfam01246']), set(['pfam07541']), set(['pfam00736']), set(['TIGR00522']), set(['pfam01269']), set(['TIGR00329']), set(['pfam01015']), set(['TIGR00392']), set(['pfam00203']), set(['TIGR00398']), set(['pfam01725']), set(['pfam02005']), set(['TIGR00422']), set(['pfam03439']), set(['pfam01351']), set(['pfam01922']), set(['pfam11987']), set(['pfam04127']), set(['TIGR00064']), set(['TIGR00389']), set(['pfam13656']), set(['pfam00298']), set(['TIGR00432']), set(['TIGR03677']), set(['pfam00958']), set(['pfam05221']), set(['pfam00347']), set(['TIGR03685']), set(['pfam03876']), set(['pfam01192']), set(['pfam01984']), set(['pfam00827']), set(['pfam01982']), set(['pfam01981']), set(['TIGR00408']), set(['TIGR00270']), set(['TIGR03665']), set(['pfam02978']), set(['pfam03484']), set(['pfam01201']), set(['TIGR02076']), set(['pfam00832']), set(['pfam00833']), set(['TIGR00419']), set(['pfam00177']), set(['pfam06418']), set(['TIGR00057']), set(['TIGR00549']), set(['pfam13685']), set(['pfam05670']), set(['pfam01849']), set(['TIGR02338']), set(['TIGR00468']), set(['pfam09249']), set(['pfam01287']), set(['pfam00164']), set(['pfam01282']), set(['TIGR03724']), set(['pfam01200']), set(['TIGR02153']), set(['TIGR00670']), set(['pfam00398']), set(['TIGR01213']), set(['pfam06026']), set(['pfam04019']), set(['pfam04010']), set(['pfam00366'])]"
)
print(len(test))
for ms in markerSet.markerSet:
bMatch = False
for tms in test:
if tms == ms:
print(ms)
print(tms)
print('---------')
bMatch = True
break
if not bMatch:
print('BOO!')
if str(
markerSet.markerSet
) == "[set(['TIGR01080', 'pfam08071', 'pfam00900']), set(['pfam08069', 'pfam00312']), set(['pfam00276', 'pfam00573', 'pfam00297']), set(['pfam00333', 'pfam00327', 'pfam03719']), set(['pfam04563', 'pfam04560', 'pfam04983', 'pfam04566', 'pfam04567', 'pfam04565', 'pfam04997', 'pfam00562', 'pfam05000', 'pfam00623', 'pfam01191', 'pfam04561', 'TIGR02389']), set(['pfam00831', 'pfam01868', 'pfam00189', 'pfam00237']), set(['pfam01280', 'pfam00861', 'pfam01655']), set(['pfam01194', 'pfam00380', 'pfam00572']), set(['TIGR00425', 'pfam08068']), set(['pfam01157', 'pfam03874']), set(['pfam00416', 'TIGR01018']), set(['pfam00679', 'pfam03764']), set(['TIGR00344', 'TIGR03683']), set(['pfam01193', 'pfam01000']), set(['pfam00750', 'pfam05746']), set(['pfam00935', 'pfam01667']), set(['pfam00867', 'pfam00752']), set(['pfam01172', 'pfam09377']), set(['pfam03950', 'pfam00749']), set(['pfam00181', 'pfam03947']), set(['pfam00687', 'pfam00466']), set(['TIGR03679', 'TIGR00289']), set(['pfam01198', 'pfam01912']), set(['pfam00673', 'pfam00281']), set(['TIGR00134']), set(['pfam00410']), set(['pfam00411']), set(['pfam01090']), set(['pfam01092']), set(['pfam04919']), set(['TIGR00336']), set(['pfam01864']), set(['TIGR00442']), set(['pfam01866']), set(['pfam01780']), set(['TIGR01046']), set(['pfam00318']), set(['pfam00252']), set(['pfam09173']), set(['pfam00238']), set(['pfam01798']), set(['pfam01246']), set(['pfam07541']), set(['pfam00736']), set(['TIGR00522']), set(['pfam01269']), set(['TIGR00329']), set(['pfam01015']), set(['TIGR00392']), set(['pfam00203']), set(['TIGR00398']), set(['pfam01725']), set(['pfam02005']), set(['TIGR00422']), set(['pfam03439']), set(['pfam01351']), set(['pfam01922']), set(['pfam11987']), set(['pfam04127']), set(['TIGR00064']), set(['TIGR00389']), set(['pfam13656']), set(['pfam00298']), set(['TIGR00432']), set(['TIGR03677']), set(['pfam00958']), set(['pfam05221']), set(['pfam00347']), set(['TIGR03685']), set(['pfam03876']), set(['pfam01192']), set(['pfam01984']), set(['pfam00827']), set(['pfam01982']), set(['pfam01981']), set(['TIGR00408']), set(['TIGR00270']), set(['TIGR03665']), set(['pfam02978']), set(['pfam03484']), set(['pfam01201']), set(['TIGR02076']), set(['pfam00832']), set(['pfam00833']), set(['TIGR00419']), set(['pfam00177']), set(['pfam06418']), set(['TIGR00057']), set(['TIGR00549']), set(['pfam13685']), set(['pfam05670']), set(['pfam01849']), set(['TIGR02338']), set(['TIGR00468']), set(['pfam09249']), set(['pfam01287']), set(['pfam00164']), set(['pfam01282']), set(['TIGR03724']), set(['pfam01200']), set(['TIGR02153']), set(['TIGR00670']), set(['pfam00398']), set(['TIGR01213']), set(['pfam06026']), set(['pfam04019']), set(['pfam04010']), set(['pfam00366'])]":
print('Good to go!')
else:
print('oh, shit!!!!')
def run(self, metadataFile, percentThreshold):
img = IMG()
metadata = img.genomeMetadataFromFile(metadataFile)
matches = {}
pfamCount = {}
tigrCount = {}
for genomeCounter, genomeId in enumerate(metadata):
statusStr = ' Finished processing %d of %d (%.2f%%) genomes.' % (genomeCounter+1, len(metadata), float(genomeCounter+1)*100/len(metadata))
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
if metadata[genomeId]['status'] == 'Finished':
pfamFile = img.genomeDir + genomeId + '/' + genomeId + img.pfamExtension
if not os.path.exists(pfamFile):
continue
# get PFAM hits
geneIdToPfams = {}
bHeader = True
for line in open(pfamFile):
if bHeader:
bHeader = False
continue
lineSplit = line.split('\t')
if lineSplit[0] in geneIdToPfams:
geneIdToPfams[lineSplit[0]].add(lineSplit[8])
else:
geneIdToPfams[lineSplit[0]] = set([lineSplit[8]])
if lineSplit[8] in pfamCount:
pfamCount[lineSplit[8]].add(genomeId)
else:
pfamCount[lineSplit[8]] = set([genomeId])
# get TIGRFAM hits
geneIdToTigr = {}
bHeader = True
for line in open(img.genomeDir + genomeId + '/' + genomeId + img.tigrExtension):
if bHeader:
bHeader = False
continue
lineSplit = line.split('\t')
if lineSplit[0] in geneIdToTigr:
geneIdToTigr[lineSplit[0]].add(lineSplit[6])
else:
geneIdToTigr[lineSplit[0]] = set([lineSplit[6]])
if lineSplit[6] in tigrCount:
tigrCount[lineSplit[6]].add(genomeId)
else:
tigrCount[lineSplit[6]] = set([genomeId])
# keep track of TIGRFAMs matching the same gene as a PFAM
geneIds = set(geneIdToPfams.keys()).union(set(geneIdToTigr.keys()))
for geneId in geneIds:
pfams = geneIdToPfams.get(geneId, None)
tigrs = geneIdToTigr.get(geneId, None)
if pfams == None or tigrs == None:
continue
for pfamId in pfams:
for tigrId in tigrs:
key = pfamId + '-' + tigrId
if key in matches:
matches[key].add(genomeId)
else:
matches[key] = set([genomeId])
sys.stdout.write('\n')
# find TIGRFAMs that generally hit the same gene as a PFAM
fout = open('../data/pfam/tigrfam2pfam.tsv', 'w')
for key, genomeSet in matches.items():
pfam, tigr = key.split('-')
# deem a TIGRFAM HMM redundant if it is almost always hits that
# same ORF as a PFAM HMM
if float(len(genomeSet)) / len(tigrCount[tigr]) >= percentThreshold:
fout.write(pfam + '\t' + tigr + '\n')
fout.close()
def run(self, inputMetadataFile, outputMetadataFile, outputDir,
ubiquityThreshold, singleCopyThreshold, trustedCompleteness,
trustedContamination):
img = IMG()
markerSetBuilder = MarkerSetBuilder()
allOut = open(os.path.join(outputDir, 'genomes_all.tsv'), 'w')
allOut.write(
'Genome Id\tLineage\tGenome size (Mbps)\tScaffold count\tGene count\tCoding base count\tN50\tBiotic Relationship\tStatus\tCompleteness\tContamination\tMissing markers\tDuplicate markers\n'
)
trustedOut = open(os.path.join(outputDir, 'genomes_trusted.tsv'), 'w')
trustedOut.write(
'Genome Id\tLineage\tGenome size (Mbps)\tScaffold count\tGene count\tCoding base count\tN50\tBiotic Relationship\tStatus\tCompleteness\tContamination\tMissing markers\tDuplicate markers\n'
)
filteredOut = open(os.path.join(outputDir, 'genomes_filtered.tsv'),
'w')
filteredOut.write(
'Genome Id\tLineage\tGenome size (Mbps)\tScaffold count\tGene count\tCoding base count\tN50\tBiotic Relationship\tStatus\tCompleteness\tContamination\tMissing markers\tDuplicate markers\n'
)
metadataOut = open(outputMetadataFile, 'w')
# read input metadata file
metadata = img.genomeMetadataFromFile(inputMetadataFile)
finishedGenomes = defaultdict(set)
allGenomes = defaultdict(set)
metadataLine = {}
bHeader = True
for line in open(inputMetadataFile):
if bHeader:
metadataOut.write(line)
bHeader = False
continue
lineSplit = line.split('\t')
genomeId = lineSplit[0]
domain = lineSplit[1]
status = lineSplit[2]
if status == 'Finished':
finishedGenomes[domain].add(genomeId)
allGenomes[domain].add(genomeId)
metadataLine[genomeId] = line
allTrustedGenomeIds = set()
for lineage, allLineageGenomeIds in allGenomes.items():
print('[' + lineage + ']')
print(' Number of genomes: %d' % len(allLineageGenomeIds))
# tabulate genomes from each phylum
allPhylumCounts = {}
for genomeId in allLineageGenomeIds:
taxon = metadata[genomeId]['taxonomy'][1]
allPhylumCounts[taxon] = allPhylumCounts.get(taxon, 0) + 1
# identify marker genes for finished genomes
print(
'\nDetermining initial marker gene sets for genome filtering.')
markerSet = markerSetBuilder.buildMarkerSet(
finishedGenomes[lineage], ubiquityThreshold,
singleCopyThreshold)
print(
' Marker set consists of %s marker genes organized into %d sets.'
% (markerSet.numMarkers(), markerSet.numSets()))
fout = open(
os.path.join(outputDir,
'trusted_marker_sets_' + lineage + '.txt'), 'w')
fout.write(str(markerSet.markerSet))
fout.close()
# identifying trusted genomes (highly complete, low contamination genomes)
print('\nIdentifying highly complete, low contamination genomes.')
trustedGenomeIds = set()
filteredGenomes = set()
retainedStatus = {}
filteredStatus = {}
geneCountTable = img.geneCountTable(allLineageGenomeIds)
for genomeId in allLineageGenomeIds:
completeness, contamination, missingMarkers, duplicateMarkers = markerSetBuilder.genomeCheck(
markerSet.markerSet, genomeId, geneCountTable)
genomeStr = self.__genomeString(genomeId, metadata,
completeness, contamination,
missingMarkers,
duplicateMarkers)
if completeness >= trustedCompleteness and contamination <= trustedContamination:
trustedGenomeIds.add(genomeId)
allTrustedGenomeIds.add(genomeId)
retainedStatus[metadata[genomeId]
['status']] = retainedStatus.get(
metadata[genomeId]['status'], 0) + 1
trustedOut.write(genomeStr)
allOut.write(genomeStr)
metadataOut.write(metadataLine[genomeId])
else:
filteredGenomes.add(genomeId)
filteredStatus[metadata[genomeId]
['status']] = filteredStatus.get(
metadata[genomeId]['status'], 0) + 1
filteredOut.write(genomeStr)
allOut.write(genomeStr)
print(' Filtered genomes: %d (%.2f%%)' %
(len(filteredGenomes),
len(filteredGenomes) * 100.0 / len(allLineageGenomeIds)))
print(' ' + str(filteredStatus))
print(' \nTrusted genomes: %d (%.2f%%)' %
(len(trustedGenomeIds),
len(trustedGenomeIds) * 100.0 / len(allLineageGenomeIds)))
print(' ' + str(retainedStatus))
# determine status of retained genomes
print('\nTrusted genomes by phylum:')
trustedPhylumCounts = {}
for genomeId in trustedGenomeIds:
taxon = metadata[genomeId]['taxonomy'][1]
trustedPhylumCounts[taxon] = trustedPhylumCounts.get(taxon,
0) + 1
for phylum, count in allPhylumCounts.items():
print(' ' + phylum + ': %d of %d' %
(trustedPhylumCounts.get(phylum, 0), count))
print('')
allOut.close()
trustedOut.close()
filteredOut.close()
metadataOut.close()
# write out lineage statistics for genome distribution
allStats = {}
trustedStats = {}
for r in range(0, 6): # Domain to Genus
for genomeId, data in metadata.items():
taxaStr = ';'.join(data['taxonomy'][0:r + 1])
allStats[taxaStr] = allStats.get(taxaStr, 0) + 1
if genomeId in allTrustedGenomeIds:
trustedStats[taxaStr] = trustedStats.get(taxaStr, 0) + 1
sortedLineages = img.lineagesSorted(metadata)
fout = open(os.path.join(outputDir, 'lineage_stats.tsv'), 'w')
fout.write('Lineage\tGenomes with metadata\tTrusted genomes\n')
for lineage in sortedLineages:
fout.write(lineage + '\t' + str(allStats.get(lineage, 0)) + '\t' +
str(trustedStats.get(lineage, 0)) + '\n')
fout.close()
class MarkerSetSelection(object):
def __init__(self):
self.simFile = './experiments/simulation.tuning.genus.summary.tsv'
self.looRank = 5
self.markerSetBuilder = MarkerSetBuilder()
self.img = IMG()
def __stabilityTest(self,
genomeIds,
ubiquityThreshold=0.97,
singleCopyThreshold=0.97,
stabilityThreshold=0.05):
"""Test stability of marker set for a group of genomes using LOO-testing."""
# quick escape for lineage that are clearly stable
if len(genomeIds) > 200:
return True
# calculate marker sets using a LOO-testing
looMarkerGenes = []
for genomeId in genomeIds:
looGenomeIds = genomeIds.difference([genomeId])
# calculate marker genes
geneCountTable = self.img.geneCountTable(looGenomeIds)
markerGenes = self.markerSetBuilder.markerGenes(
looGenomeIds, geneCountTable,
ubiquityThreshold * len(looGenomeIds),
singleCopyThreshold * len(looGenomeIds))
tigrToRemove = self.img.identifyRedundantTIGRFAMs(markerGenes)
markerGenes = markerGenes - tigrToRemove
looMarkerGenes.append(markerGenes)
# calculate change in marker set for all pairs
markerSetSize = []
diffMarkerSet = []
for i in range(0, len(looMarkerGenes)):
markerSetSize.append(len(looMarkerGenes[i]))
for j in range(i + 1, len(looMarkerGenes)):
symmDiff = looMarkerGenes[i].symmetric_difference(
looMarkerGenes[j])
diffMarkerSet.append(len(symmDiff))
print(len(genomeIds), mean(diffMarkerSet), mean(markerSetSize))
return (float(mean(diffMarkerSet)) /
mean(markerSetSize)) <= stabilityThreshold
def __patristicDist(self, tree, taxa1, taxa2):
mrca = tree.mrca(taxon_labels=[taxa1.taxon.label, taxa2.taxon.label])
if mrca.parent_node == None:
# MRCA is the root of the tree
return taxa1.distance_from_root() + taxa2.distance_from_root()
else:
dist = taxa1.edge_length
parentNode = taxa1.parent_node
while parentNode != mrca:
dist += parentNode.edge_length
parentNode = parentNode.parent_node
dist += taxa2.edge_length
parentNode = taxa2.parent_node
while parentNode != mrca:
dist += parentNode.edge_length
parentNode = parentNode.parent_node
return dist
def __distToNodePercentileTest(self, genomeNode, markerSetNode, leaves,
percentileTest):
distToBin = self.__distanceToAncestor(genomeNode, markerSetNode)
distToLeaves = []
for leaf in leaves:
distToLeaves.append(self.__distanceToAncestor(leaf, markerSetNode))
return distToBin < percentile(distToLeaves, percentileTest)
def __selectMarkerSetNode(self, tree, genomeId, metadata,
taxonToGenomeIds):
"""Determine lineage-specific marker set to use for assessing the giving genome."""
# read genomes removed from tree as a result of duplicate sequences
duplicateSeqs = self.markerSetBuilder.readDuplicateSeqs()
# determine location of genome in tree
node = tree.find_node_with_taxon_label('IMG_' + genomeId)
# ascend tree to root looking for suitable marker set
curNode = node.parent_node
while curNode != None:
uniqueId = curNode.label.split('|')[0]
genomeIds = set()
for leaf in curNode.leaf_nodes():
genomeIds.add(leaf.taxon.label.replace('IMG_', ''))
duplicateGenomes = duplicateSeqs.get(leaf.taxon.label, [])
for dup in duplicateGenomes:
genomeIds.add(dup.replace('IMG_', ''))
# remove genome (LOO-style analysis)
print('Full:', len(genomeIds))
genomeIds.difference_update([genomeId])
print('LOO:', len(genomeIds))
# remove all genomes from the same taxonomic group as the genome of interest
taxon = metadata[genomeId]['taxonomy'][self.looRank]
genomeIds.difference_update(taxonToGenomeIds[taxon])
print('Rank reduced:', len(genomeIds))
print(uniqueId)
if len(genomeIds) > 10 and self.__stabilityTest(genomeIds):
uidSelected = uniqueId
break
curNode = curNode.parent_node
if curNode == None:
# reach root so use universal marker set
uidSelected = uniqueId
return uidSelected
def __bestMarkerSet(self, genomeId, simResults):
"""Get stats for best marker set."""
curBest = 1000
bestUID = None
for uid, results in simResults[genomeId].items():
numDescendants, dComp, dCont = results
if (dComp + dCont) < curBest:
numDescendantsBest = numDescendants
dCompBest = dComp
dContBest = dCont
bestUID = uid
curBest = dComp + dCont
return bestUID, numDescendantsBest, dCompBest, dContBest
def __workerThread(self, tree, simResults, metadata, taxonToGenomeIds,
queueIn, queueOut):
"""Process each data item in parallel."""
while True:
testGenomeId = queueIn.get(block=True, timeout=None)
if testGenomeId == None:
break
uidSelected = self.__selectMarkerSetNode(tree, testGenomeId,
metadata,
taxonToGenomeIds)
numDescendantsSelected, dCompSelected, dContSelected = simResults[
testGenomeId][uidSelected]
# find best marker set
bestUID, numDescendantsBest, dCompBest, dContBest = self.__bestMarkerSet(
testGenomeId, simResults)
queueOut.put((testGenomeId, uidSelected, numDescendantsSelected,
dCompSelected, dContSelected, bestUID,
numDescendantsBest, dCompBest, dContBest))
def __writerThread(self, numTestGenomes, writerQueue):
"""Store or write results of worker threads in a single thread."""
fout = open('./experiments/markerSetSelection.tsv', 'w')
fout.write(
'Genome Id\tSelected UID\t# descendants\tSelected dComp\tSelected dCont\tBest UID\t# descendants\tBest dComp\tBest dCont\tdDescendants\tdComp\tdCont\n'
)
itemsToProcess = 0
dComps = []
dConts = []
dCompsPer = []
dContsPer = []
bestComp = []
bestCont = []
selectedComp = []
selectedCont = []
while True:
testGenomeId, uidSelected, numDescendantsSelected, dCompSelected, dContSelected, bestUID, numDescendantsBest, dCompBest, dContBest = writerQueue.get(
block=True, timeout=None)
if testGenomeId == None:
break
itemsToProcess += 1
statusStr = ' Finished processing %d of %d (%.2f%%) test genomes.' % (
itemsToProcess, numTestGenomes, float(itemsToProcess) * 100 /
(numTestGenomes))
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
dComp = abs(dCompSelected - dCompBest)
dCont = abs(dContSelected - dContBest)
dDescendants = abs(numDescendantsSelected - numDescendantsBest)
fout.write(
'%s\t%s\t%d\t%.4f\t%.4f\t%s\t%d\t%.4f\t%.4f\t%d\t%.4f\t%.4f\n'
% (testGenomeId, uidSelected, numDescendantsSelected,
dCompSelected, dContSelected, bestUID, numDescendantsBest,
dCompBest, dContBest, dDescendants, dComp, dCont))
dComps.append(dComp)
dConts.append(dCont)
dCompsPer.append(dComp * 100.0 / dCompBest)
dContsPer.append(dCont * 100.0 / max(dContBest, 0.01))
bestComp.append(dCompBest)
bestCont.append(dContBest)
selectedComp.append(dCompSelected)
selectedCont.append(dContSelected)
sys.stdout.write('\n')
fout.close()
print('')
print(' General results:')
print(' Best comp: %.2f +/- %.2f' % (mean(bestComp), std(bestComp)))
print(' Best cont: %.2f +/- %.2f' % (mean(bestCont), std(bestCont)))
print(' Selected comp: %.2f +/- %.2f' %
(mean(selectedComp), std(selectedComp)))
print(' Selected cont: %.2f +/- %.2f' %
(mean(selectedCont), std(selectedCont)))
print('')
print(' Delta comp: %.2f +/- %.2f' % (mean(dComps), std(dComps)))
print(' Delta cont: %.2f +/- %.2f' % (mean(dConts), std(dConts)))
print(' Delta comp per error: %.1f +/- %.1f' %
(mean(dCompsPer), std(dCompsPer)))
print(' Delta cont per error: %.1f +/- %.1f' %
(mean(dContsPer), std(dContsPer)))
def __distanceToAncestor(self, leaf, ancestor):
dist = 0
curNode = leaf
while curNode != ancestor:
dist += curNode.edge_length
curNode = curNode.parent_node
return dist
def __bestNodeProperties(self, genomeId, tree, bestUID):
# determine location of genome in tree
node = tree.find_node_with_taxon_label('IMG_' + genomeId)
# find node of best marker set
curNode = node.parent_node
nodesToBin = 0
distanceToBin = node.edge_length
distanceToLeaves = []
while curNode != None:
uniqueId = curNode.label.split('|')[0]
nodesToBin += 1
if uniqueId == bestUID:
for leaf in curNode.leaf_nodes():
if leaf != node:
dist = self.__distanceToAncestor(leaf, curNode)
distanceToLeaves.append(dist)
break
distanceToBin += curNode.edge_length
curNode = curNode.parent_node
return nodesToBin, distanceToBin, mean(distanceToLeaves)
def __propertiesOfBestMarkerSets(self, tree, simResults):
numDescendants = []
nodesToBin = []
distanceToBin = []
avgDistanceToLeaf = []
percDiffs = []
for genomeId in simResults:
bestUID, numDescendantsBest, _, _ = self.__bestMarkerSet(
genomeId, simResults)
nodesToBinBest, distanceToBinBest, avgDistanceToLeafBest = self.__bestNodeProperties(
genomeId, tree, bestUID)
numDescendants.append(numDescendantsBest)
nodesToBin.append(nodesToBinBest)
distanceToBin.append(distanceToBinBest)
avgDistanceToLeaf.append(avgDistanceToLeafBest)
percDiff = abs(distanceToBinBest -
avgDistanceToLeafBest) * 100 / distanceToBinBest
percDiffs.append(percDiff)
print(' # descendants: %.2f +/- %.2f' %
(mean(numDescendants), std(numDescendants)))
print(' # nodes to bin: %.2f +/- %.2f' %
(mean(nodesToBin), std(nodesToBin)))
print(' Distance to bin: %.2f +/- %.2f' %
(mean(distanceToBin), std(distanceToBin)))
distanceToBin = array(distanceToBin)
avgDistanceToLeaf = array(avgDistanceToLeaf)
print(' Distance to bin - average distance to leaf: %.2f +/- %.2f' %
(mean(abs(distanceToBin - avgDistanceToLeaf)),
std(abs(distanceToBin - avgDistanceToLeaf))))
print(
' Percent difference to average leaf distance: %.2f +/- %.2f' %
(mean(percDiffs), std(percDiffs)))
print('')
def run(self, numThreads):
# read reference tree
print('\n Reading reference genome tree.')
treeFile = os.path.join(os.path.dirname(sys.argv[0]), '..', 'data',
'genome_tree', 'genome_tree_prok.refpkg',
'genome_tree.final.tre')
tree = dendropy.Tree.get_from_path(treeFile,
schema='newick',
as_rooted=True,
preserve_underscores=True)
# get all genomes with a given taxon label
metadata = self.img.genomeMetadata()
taxonToGenomeIds = defaultdict(set)
for genomeId in metadata:
for t in metadata[genomeId]['taxonomy']:
taxonToGenomeIds[t].add(genomeId)
# read simulation results
print(' Reading simulation results.')
simResults = defaultdict(dict)
with open(self.simFile) as f:
f.readline()
for line in f:
lineSplit = line.split('\t')
simId = lineSplit[0] + '-' + lineSplit[1] + '-' + lineSplit[
2] + '-' + lineSplit[3]
uid = lineSplit[5].split('|')[0].strip()
numDescendants = int(lineSplit[6])
comp = float(lineSplit[21])
cont = float(lineSplit[23])
simResults[simId][uid] = [numDescendants, comp, cont]
#print ''
#print ' Properties of best marker sets:'
#self.__propertiesOfBestMarkerSets(tree, simResults)
print(' Evaluating %d test genomes.' % len(simResults))
workerQueue = mp.Queue()
writerQueue = mp.Queue()
for testGenomeId in simResults:
workerQueue.put(testGenomeId)
for _ in range(numThreads):
workerQueue.put(None)
workerProc = [
mp.Process(target=self.__workerThread,
args=(tree, simResults, metadata, taxonToGenomeIds,
workerQueue, writerQueue))
for _ in range(numThreads)
]
writeProc = mp.Process(target=self.__writerThread,
args=(len(simResults), writerQueue))
writeProc.start()
for p in workerProc:
p.start()
for p in workerProc:
p.join()
writerQueue.put((None, None, None, None, None, None, None, None, None))
writeProc.join()
class GenomeTreeWorkflow(object):
def __init__(self, outputDir):
self.img = IMG()
self.markerSetBuilder = MarkerSetBuilder()
if os.path.exists(outputDir):
print '[Error] Output directory already exists: ' + outputDir
sys.exit(0)
else:
os.makedirs(outputDir)
self.__checkForHMMER()
self.__checkForFastTree()
self.hmmDir = os.path.join(outputDir, 'phylo_hmms')
self.alignmentDir = os.path.join(outputDir, 'gene_alignments')
self.geneTreeDir = os.path.join(outputDir, 'gene_trees')
self.conspecificGeneTreeDir = os.path.join(outputDir,
'gene_trees_conspecific')
self.finalGeneTreeDir = os.path.join(outputDir, 'gene_trees_final')
self.consistencyOut = os.path.join(outputDir,
'genome_tree.consistency.tsv')
self.concatenatedAlignFile = os.path.join(
outputDir, 'genome_tree.concatenated.faa')
self.derepConcatenatedAlignFile = os.path.join(
outputDir, 'genome_tree.concatenated.derep.fasta')
self.treeOut = os.path.join(outputDir, 'genome_tree.tre')
self.treeOutAce = os.path.join(outputDir, 'genome_tree.ace_ids.tre')
self.treeRootedOut = os.path.join(outputDir, 'genome_tree.rooted.tre')
self.treeTaxonomyOut = os.path.join(outputDir,
'genome_tree.taxonomy.tre')
self.treeDerepOut = os.path.join(outputDir, 'genome_tree.derep.tre')
self.treeDerepRootedOut = os.path.join(outputDir,
'genome_tree.derep.rooted.tre')
self.treeDerepBootstrapOut = os.path.join(outputDir,
'genome_tree.derep.bs.tre')
self.treeDerepFinalOut = os.path.join(outputDir,
'genome_tree.final.tre')
self.taxonomyOut = os.path.join(outputDir, 'genome_tree.taxonomy.tsv')
self.treeMetadata = os.path.join(outputDir, 'genome_tree.metadata.tsv')
self.phyloHMMsOut = os.path.join(outputDir, 'phylo.hmm')
self.derepSeqFile = os.path.join(outputDir, 'genome_tree.derep.txt')
self.phyloUbiquity = 0.90
self.phyloSingleCopy = 0.90
self.paralogAcceptPer = 0.01
# self.consistencyAcceptPer = 0.95 # for trees at the class-level
self.consistencyAcceptPer = 0.906 # for trees at the phylum-level
self.consistencyMinTaxa = 20
# create output directories
os.makedirs(self.hmmDir)
os.makedirs(self.alignmentDir)
os.makedirs(self.geneTreeDir)
os.makedirs(self.conspecificGeneTreeDir)
os.makedirs(self.finalGeneTreeDir)
def __checkForHMMER(self):
"""Check to see if HMMER is on the system path."""
try:
exit_status = os.system('hmmfetch -h > /dev/null')
except:
print "Unexpected error!", sys.exc_info()[0]
raise
if exit_status != 0:
print "[Error] hmmfetch is not on the system path"
sys.exit()
def __checkForFastTree(self):
"""Check to see if FastTree is on the system path."""
try:
exit_status = os.system('FastTree 2> /dev/null')
except:
print "Unexpected error!", sys.exc_info()[0]
raise
if exit_status != 0:
print "[Error] FastTree is not on the system path"
sys.exit()
def __genesInGenomes(self, genomeIds):
genesInGenomes = {}
for genomeId in genomeIds:
markerIdToGeneIds = defaultdict(set)
for line in open(
os.path.join(IMG.genomeDir, genomeId,
genomeId + IMG.pfamExtension)):
lineSplit = line.split('\t')
markerIdToGeneIds[lineSplit[8]].add(lineSplit[0])
for line in open(
os.path.join(IMG.genomeDir, genomeId,
genomeId + IMG.tigrExtension)):
lineSplit = line.split('\t')
markerIdToGeneIds[lineSplit[6]].add(lineSplit[0])
genesInGenomes[genomeId] = markerIdToGeneIds
return genesInGenomes
def __fetchMarkerModels(self, universalMarkerGenes, outputModelDir):
markerIdToName = {}
for line in open('/srv/whitlam/bio/db/pfam/27/Pfam-A.hmm'):
if 'NAME' in line:
name = line.split()[1].rstrip()
elif 'ACC' in line:
acc = line.split()[1].rstrip()
markerId = acc.replace('PF', 'pfam')
markerId = markerId[0:markerId.rfind('.')]
markerIdToName[markerId] = name
for markerId in universalMarkerGenes:
if 'pfam' in markerId:
os.system('hmmfetch /srv/whitlam/bio/db/pfam/27/Pfam-A.hmm ' +
markerIdToName[markerId] + ' > ' +
os.path.join(outputModelDir,
markerId.replace('pfam', 'PF') +
'.hmm'))
else:
os.system('hmmfetch /srv/whitlam/bio/db/tigrfam/13.0/' +
markerId + '.HMM ' + markerId + ' > ' +
os.path.join(outputModelDir, markerId + '.hmm'))
def __alignMarkers(self, genomeIds, markerGenes, genesInGenomes,
numThreads, outputGeneDir, outputModelDir):
"""Perform multithreaded alignment of marker genes using HMM align."""
workerQueue = mp.Queue()
writerQueue = mp.Queue()
for _, markerId in enumerate(markerGenes):
workerQueue.put(markerId)
for _ in range(numThreads):
workerQueue.put(None)
calcProc = [
mp.Process(target=self.__runHmmAlign,
args=(genomeIds, genesInGenomes, outputGeneDir,
outputModelDir, workerQueue, writerQueue))
for _ in range(numThreads)
]
writeProc = mp.Process(target=self.__reportThreads,
args=(len(markerGenes), writerQueue))
writeProc.start()
for p in calcProc:
p.start()
for p in calcProc:
p.join()
writerQueue.put(None)
writeProc.join()
def __runHmmAlign(self, genomeIds, genesInGenomes, outputGeneDir,
outputModelDir, queueIn, queueOut):
"""Run each marker gene in a separate thread."""
while True:
markerId = queueIn.get(block=True, timeout=None)
if markerId == None:
break
modelName = markerId
if modelName.startswith('pfam'):
modelName = modelName.replace('pfam', 'PF')
markerSeqFile = os.path.join(outputGeneDir, modelName + '.faa')
fout = open(markerSeqFile, 'w')
for genomeId in genomeIds:
seqs = readFasta(IMG.genomeDir + '/' + genomeId + '/' +
genomeId + '.genes.faa')
for geneId in genesInGenomes[genomeId].get(markerId, []):
if geneId not in seqs:
# this shouldn't be necessary, but the IMG metadata isn't always
# perfectly in sync with the sequence data
continue
fout.write('>' + genomeId + '|' + geneId + '\n')
fout.write(seqs[geneId] + '\n')
fout.close()
hmmer = HMMERRunner('align')
hmmer.align(os.path.join(outputModelDir, modelName + '.hmm'),
markerSeqFile,
os.path.join(outputGeneDir, modelName + '.aln.faa'),
trim=False,
outputFormat='Pfam')
self.__maskAlignment(
os.path.join(outputGeneDir, modelName + '.aln.faa'),
os.path.join(outputGeneDir, modelName + '.aln.masked.faa'))
queueOut.put(modelName)
def __reportThreads(self, numGenes, writerQueue):
"""Store confidence intervals (i.e., to shared memory)."""
numProcessedGenes = 0
while True:
markerId = writerQueue.get(block=True, timeout=None)
if markerId == None:
break
numProcessedGenes += 1
statusStr = ' Finished processing %d of %d (%.2f%%) marker genes.' % (
numProcessedGenes, numGenes,
float(numProcessedGenes) * 100 / numGenes)
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
sys.stdout.write('\n')
def __maskAlignment(self, inputFile, outputFile):
"""Read HMMER alignment in STOCKHOLM format and output masked alignment in FASTA format."""
# read STOCKHOLM alignment
seqs = {}
for line in open(inputFile):
line = line.rstrip()
if line == '' or line[0] == '#' or line == '//':
if 'GC RF' in line:
mask = line.split('GC RF')[1].strip()
continue
else:
lineSplit = line.split()
seqs[lineSplit[0]] = lineSplit[1].upper().replace('.',
'-').strip()
# output masked sequences in FASTA format
fout = open(outputFile, 'w')
for seqId, seq in seqs.iteritems():
fout.write('>' + seqId + '\n')
maskedSeq = ''.join(
[seq[i] for i in xrange(0, len(seq)) if mask[i] == 'x'])
fout.write(maskedSeq + '\n')
fout.close()
def __taxonomicMarkers(self, taxaStr, metadata, ubiquityThreshold,
singleCopyThreshold):
"""Get genomes and marker genes for a specific lineage."""
genomeIds = self.img.genomeIdsByTaxonomy(taxaStr, metadata)
# hack to add in other genomes with incorrect taxonomy
aceIds = set()
for line in open('./taxonomicTrees/firmicutes.nds'):
aceIds.add(line.strip())
aceIdsToImgIds = self.aceIdsToImgIds()
for aceId in aceIds:
if aceId in aceIdsToImgIds:
genomeId = aceIdsToImgIds[aceId]
if genomeId in metadata:
genomeIds.add(genomeId)
markerGenes = self.markerSetBuilder.buildMarkerGenes(
genomeIds, ubiquityThreshold, singleCopyThreshold)
return genomeIds, markerGenes
def inferGeneTrees(self, phyloUbiquityThreshold, phyloSingleCopyThreshold,
numThreads, outputGeneDir, outputModelDir,
outgroupSize):
# make sure output directory is empty
if not os.path.exists(outputGeneDir):
os.makedirs(outputGeneDir)
if not os.path.exists(outputModelDir):
os.makedirs(outputModelDir)
files = os.listdir(outputGeneDir)
for f in files:
os.remove(os.path.join(outputGeneDir, f))
# get genomes and marker genes for taxonomic groups of interest
print ''
print 'Identifying genomes and marker genes of interest:'
metadata = self.img.genomeMetadata()
ingroupGenomeIds, ingroupMarkers = self.__taxonomicMarkers(
'Bacteria;Firmicutes', metadata, phyloUbiquityThreshold,
phyloSingleCopyThreshold)
outgroupGenomeIds = self.img.genomeIdsByTaxonomy(
'Bacteria;Coprothermobacter', metadata)
# alphaGenomeIds, _ = self.__taxonomicMarkers('Bacteria;Proteobacteria;Alphaproteobacteria', metadata, phyloUbiquityThreshold, phyloSingleCopyThreshold)
print ' Identified ingroup genomes: %d' % len(ingroupGenomeIds)
print ' Identified outgroup genomes: %d' % len(outgroupGenomeIds)
numOutgroupTaxa = min(outgroupSize, len(outgroupGenomeIds))
print ''
print ' Selecting %d taxa from the outgroup.' % (numOutgroupTaxa)
genomeIds = ingroupGenomeIds.union(
random.sample(outgroupGenomeIds, numOutgroupTaxa))
self.imgIdsToAceIds(genomeIds)
print ' Identified markers: %d' % len(ingroupMarkers)
# get mapping of marker ids to gene ids for each genome
print ' Determine genes for genomes of interest.'
genesInGenomes = self.__genesInGenomes(genomeIds)
# get HMM for each marker gene
print ' Fetching HMM for each marker genes.'
self.__fetchMarkerModels(ingroupMarkers, outputModelDir)
# align gene sequences and infer gene trees
print ' Aligning marker genes:'
#***self.__alignMarkers(genomeIds, ingroupMarkers, genesInGenomes, numThreads, outputGeneDir, outputModelDir)
return genomeIds
def imgIdsToAceIds(self, imgIds):
imgIdToAceId = {}
for line in open('ggg_tax_img.feb_2014.txt'):
lineSplit = line.split('\t')
imgIdToAceId[lineSplit[1].rstrip()] = lineSplit[0]
missing = 0
for imgId in imgIds:
if imgId not in imgIdToAceId:
missing += 1
print ' Number of genomes without an ACE id: ' + str(missing)
return imgIdToAceId
def aceIdsToImgIds(self):
aceIdsToImgIds = {}
for line in open('ggg_tax_img.feb_2014.txt'):
lineSplit = line.split('\t')
aceIdsToImgIds[lineSplit[0].strip()] = lineSplit[1].strip()
return aceIdsToImgIds
def run(self, numThreads, outgroupSize):
# identify genes suitable for phylogenetic inference
print '--- Identifying genes suitable for phylogenetic inference ---'
genomeIds = self.inferGeneTrees(self.phyloUbiquity,
self.phyloSingleCopy, numThreads,
self.alignmentDir, self.hmmDir,
outgroupSize)
# infer gene trees
print ''
print '--- Inferring gene trees ---'
makeTrees = MakeTrees()
makeTrees.run(self.alignmentDir, self.geneTreeDir, '.aln.masked.faa',
numThreads)
# test gene trees for paralogs
print ''
print '--- Testing for paralogs in gene trees ---'
paralogTest = ParalogTest()
paralogTest.run(self.geneTreeDir, self.paralogAcceptPer, '.tre',
self.conspecificGeneTreeDir)
# test gene trees for consistency with IMG taxonomy
print ''
print '--- Testing taxonomic consistency of gene trees ---'
consistencyTest = ConsistencyTest()
consistencyTest.run(self.conspecificGeneTreeDir, '.tre',
self.consistencyAcceptPer, self.consistencyMinTaxa,
self.consistencyOut, self.finalGeneTreeDir)
# gather phylogenetically informative HMMs into a single model file
print ''
print '--- Gathering phylogenetically informative HMMs ---'
getPhylogeneticHMMs = GetPhylogeneticHMMs()
getPhylogeneticHMMs.run(self.hmmDir, self.finalGeneTreeDir,
self.phyloHMMsOut)
# infer genome tree
print ''
print '--- Inferring full genome tree ---'
inferGenomeTree = InferGenomeTree()
inferGenomeTree.run(self.finalGeneTreeDir,
self.alignmentDir,
'.aln.masked.faa',
self.concatenatedAlignFile,
self.treeOut,
self.taxonomyOut,
bSupportValues=True)
# replace IMG identifiers with ACE identifiers
imgIdToAceId = self.imgIdsToAceIds(genomeIds)
with open(self.treeOut) as f:
tree = ''.join(f.readlines())
for genomeId in genomeIds:
if genomeId in imgIdToAceId:
tree = tree.replace('IMG_' + genomeId,
imgIdToAceId[genomeId])
fout = open(self.treeOutAce, 'w')
fout.write(tree)
fout.close()
def __init__(self):
self.simFile = './experiments/simulation.tuning.genus.summary.tsv'
self.looRank = 5
self.markerSetBuilder = MarkerSetBuilder()
self.img = IMG()
class MarkerSetStability(object):
def __init__(self):
self.img = IMG()
self.markerset = MarkerSet()
def __processLineage(self, metadata, ubiquityThreshold,
singleCopyThreshold, minGenomes, queueIn, queueOut):
"""Assess stability of marker set for a specific named taxonomic group."""
while True:
lineage = queueIn.get(block=True, timeout=None)
if lineage == None:
break
genomeIds = self.img.genomeIdsByTaxonomy(lineage, metadata,
'trusted')
changeMarkerSetSize = {}
markerGenes = []
if len(genomeIds) >= minGenomes:
# calculate marker set for all genomes in lineage
geneCountTable = self.img.geneCountTable(genomeIds)
markerGenes = self.markerset.markerGenes(
genomeIds, geneCountTable,
ubiquityThreshold * len(genomeIds),
singleCopyThreshold * len(genomeIds))
tigrToRemove = self.img.identifyRedundantTIGRFAMs(markerGenes)
markerGenes = markerGenes - tigrToRemove
for selectPer in range(50, 101, 5):
numGenomesToSelect = int(
float(selectPer) / 100 * len(genomeIds))
perChange = []
for _ in range(0, 10):
# calculate marker set for subset of genomes
subsetGenomeIds = random.sample(
genomeIds, numGenomesToSelect)
geneCountTable = self.img.geneCountTable(
subsetGenomeIds)
subsetMarkerGenes = self.markerset.markerGenes(
subsetGenomeIds, geneCountTable,
ubiquityThreshold * numGenomesToSelect,
singleCopyThreshold * numGenomesToSelect)
tigrToRemove = self.img.identifyRedundantTIGRFAMs(
subsetMarkerGenes)
subsetMarkerGenes = subsetMarkerGenes - tigrToRemove
perChange.append(
float(
len(
markerGenes.symmetric_difference(
subsetMarkerGenes))) * 100.0 /
len(markerGenes))
changeMarkerSetSize[selectPer] = [
mean(perChange), std(perChange)
]
queueOut.put((lineage, len(genomeIds), len(markerGenes),
changeMarkerSetSize))
def __storeResults(self, outputFile, totalLineages, writerQueue):
"""Store results to file."""
fout = open(outputFile, 'w')
fout.write(
'Lineage\t# genomes\t# markers\tsubsample %\tmean % change\tstd % change\n'
)
numProcessedLineages = 0
while True:
lineage, numGenomes, numMarkerGenes, changeMarkerSetSize = writerQueue.get(
block=True, timeout=None)
if lineage == None:
break
numProcessedLineages += 1
statusStr = ' Finished processing %d of %d (%.2f%%) lineages.' % (
numProcessedLineages, totalLineages,
float(numProcessedLineages) * 100 / totalLineages)
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
for selectPer in sorted(changeMarkerSetSize.keys()):
fout.write('%s\t%d\t%d\t%d\t%f\t%f\n' %
(lineage, numGenomes, numMarkerGenes, selectPer,
changeMarkerSetSize[selectPer][0],
changeMarkerSetSize[selectPer][1]))
sys.stdout.write('\n')
fout.close()
def run(self, outputFile, ubiquityThreshold, singleCopyThreshold,
minGenomes, mostSpecificRank, numThreads):
"""Calculate stability of marker sets for named taxonomic groups."""
print(' Calculating stability of marker sets:')
random.seed(1)
# process each sequence in parallel
workerQueue = mp.Queue()
writerQueue = mp.Queue()
metadata = self.img.genomeMetadata()
lineages = self.img.lineagesByCriteria(metadata, minGenomes,
mostSpecificRank)
#lineages = ['Bacteria']
#lineages += ['Bacteria;Proteobacteria']
#lineages += ['Bacteria;Proteobacteria;Gammaproteobacteria']
#lineages += ['Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacteriales']
#lineages += ['Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacteriales;Enterobacteriaceae']
#lineages += ['Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacteriales;Enterobacteriaceae;Escherichia']
#lineages += ['Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacteriales;Enterobacteriaceae;Escherichia;coli']
#lineages = ['Archaea']
#lineages += ['Archaea;Euryarchaeota']
#lineages += ['Archaea;Euryarchaeota;Methanomicrobia']
#lineages += ['Archaea;Euryarchaeota;Methanomicrobia;Methanosarcinales']
#lineages += ['Archaea;Euryarchaeota;Methanomicrobia;Methanosarcinales;Methanosarcinaceae']
for lineage in lineages:
workerQueue.put(lineage)
for _ in range(numThreads):
workerQueue.put(None)
calcProc = [
mp.Process(target=self.__processLineage,
args=(metadata, ubiquityThreshold, singleCopyThreshold,
minGenomes, workerQueue, writerQueue))
for _ in range(numThreads)
]
writeProc = mp.Process(target=self.__storeResults,
args=(outputFile, len(lineages), writerQueue))
writeProc.start()
for p in calcProc:
p.start()
for p in calcProc:
p.join()
writerQueue.put((None, None, None, None))
writeProc.join()
class MarkerSetStability(object):
def __init__(self):
self.img = IMG()
self.markerset = MarkerSet()
def __processLineage(self, metadata, ubiquityThreshold, singleCopyThreshold, minGenomes, queueIn, queueOut):
"""Assess stability of marker set for a specific named taxonomic group."""
while True:
lineage = queueIn.get(block=True, timeout=None)
if lineage == None:
break
genomeIds = self.img.genomeIdsByTaxonomy(lineage, metadata, 'trusted')
changeMarkerSetSize = {}
markerGenes = []
if len(genomeIds) >= minGenomes:
# calculate marker set for all genomes in lineage
geneCountTable = self.img.geneCountTable(genomeIds)
markerGenes = self.markerset.markerGenes(genomeIds, geneCountTable, ubiquityThreshold*len(genomeIds), singleCopyThreshold*len(genomeIds))
tigrToRemove = self.img.identifyRedundantTIGRFAMs(markerGenes)
markerGenes = markerGenes - tigrToRemove
for selectPer in xrange(50, 101, 5):
numGenomesToSelect = int(float(selectPer)/100 * len(genomeIds))
perChange = []
for _ in xrange(0, 10):
# calculate marker set for subset of genomes
subsetGenomeIds = random.sample(genomeIds, numGenomesToSelect)
geneCountTable = self.img.geneCountTable(subsetGenomeIds)
subsetMarkerGenes = self.markerset.markerGenes(subsetGenomeIds, geneCountTable, ubiquityThreshold*numGenomesToSelect, singleCopyThreshold*numGenomesToSelect)
tigrToRemove = self.img.identifyRedundantTIGRFAMs(subsetMarkerGenes)
subsetMarkerGenes = subsetMarkerGenes - tigrToRemove
perChange.append(float(len(markerGenes.symmetric_difference(subsetMarkerGenes)))*100.0 / len(markerGenes))
changeMarkerSetSize[selectPer] = [mean(perChange), std(perChange)]
queueOut.put((lineage, len(genomeIds), len(markerGenes), changeMarkerSetSize))
def __storeResults(self, outputFile, totalLineages, writerQueue):
"""Store results to file."""
fout = open(outputFile, 'w')
fout.write('Lineage\t# genomes\t# markers\tsubsample %\tmean % change\tstd % change\n')
numProcessedLineages = 0
while True:
lineage, numGenomes, numMarkerGenes, changeMarkerSetSize = writerQueue.get(block=True, timeout=None)
if lineage == None:
break
numProcessedLineages += 1
statusStr = ' Finished processing %d of %d (%.2f%%) lineages.' % (numProcessedLineages, totalLineages, float(numProcessedLineages)*100/totalLineages)
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
for selectPer in sorted(changeMarkerSetSize.keys()):
fout.write('%s\t%d\t%d\t%d\t%f\t%f\n' % (lineage, numGenomes, numMarkerGenes, selectPer, changeMarkerSetSize[selectPer][0], changeMarkerSetSize[selectPer][1]))
sys.stdout.write('\n')
fout.close()
def run(self, outputFile, ubiquityThreshold, singleCopyThreshold, minGenomes, mostSpecificRank, numThreads):
"""Calculate stability of marker sets for named taxonomic groups."""
print ' Calculating stability of marker sets:'
random.seed(1)
# process each sequence in parallel
workerQueue = mp.Queue()
writerQueue = mp.Queue()
metadata = self.img.genomeMetadata()
lineages = self.img.lineagesByCriteria(metadata, minGenomes, mostSpecificRank)
#lineages = ['Bacteria']
#lineages += ['Bacteria;Proteobacteria']
#lineages += ['Bacteria;Proteobacteria;Gammaproteobacteria']
#lineages += ['Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacteriales']
#lineages += ['Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacteriales;Enterobacteriaceae']
#lineages += ['Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacteriales;Enterobacteriaceae;Escherichia']
#lineages += ['Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacteriales;Enterobacteriaceae;Escherichia;coli']
#lineages = ['Archaea']
#lineages += ['Archaea;Euryarchaeota']
#lineages += ['Archaea;Euryarchaeota;Methanomicrobia']
#lineages += ['Archaea;Euryarchaeota;Methanomicrobia;Methanosarcinales']
#lineages += ['Archaea;Euryarchaeota;Methanomicrobia;Methanosarcinales;Methanosarcinaceae']
for lineage in lineages:
workerQueue.put(lineage)
for _ in range(numThreads):
workerQueue.put(None)
calcProc = [mp.Process(target = self.__processLineage, args = (metadata, ubiquityThreshold, singleCopyThreshold, minGenomes, workerQueue, writerQueue)) for _ in range(numThreads)]
writeProc = mp.Process(target = self.__storeResults, args = (outputFile, len(lineages), writerQueue))
writeProc.start()
for p in calcProc:
p.start()
for p in calcProc:
p.join()
writerQueue.put((None, None, None, None))
writeProc.join()
def run(self, metadataFile, percentThreshold):
img = IMG()
metadata = img.genomeMetadataFromFile(metadataFile)
matches = {}
pfamCount = {}
tigrCount = {}
for genomeCounter, genomeId in enumerate(metadata):
statusStr = ' Finished processing %d of %d (%.2f%%) genomes.' % (genomeCounter+1, len(metadata), float(genomeCounter+1)*100/len(metadata))
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
if metadata[genomeId]['status'] == 'Finished':
pfamFile = img.genomeDir + genomeId + '/' + genomeId + img.pfamExtension
if not os.path.exists(pfamFile):
continue
# get PFAM hits
geneIdToPfams = {}
bHeader = True
for line in open(pfamFile):
if bHeader:
bHeader = False
continue
lineSplit = line.split('\t')
if lineSplit[0] in geneIdToPfams:
geneIdToPfams[lineSplit[0]].add(lineSplit[8])
else:
geneIdToPfams[lineSplit[0]] = set([lineSplit[8]])
if lineSplit[8] in pfamCount:
pfamCount[lineSplit[8]].add(genomeId)
else:
pfamCount[lineSplit[8]] = set([genomeId])
# get TIGRFAM hits
geneIdToTigr = {}
bHeader = True
for line in open(img.genomeDir + genomeId + '/' + genomeId + img.tigrExtension):
if bHeader:
bHeader = False
continue
lineSplit = line.split('\t')
if lineSplit[0] in geneIdToTigr:
geneIdToTigr[lineSplit[0]].add(lineSplit[6])
else:
geneIdToTigr[lineSplit[0]] = set([lineSplit[6]])
if lineSplit[6] in tigrCount:
tigrCount[lineSplit[6]].add(genomeId)
else:
tigrCount[lineSplit[6]] = set([genomeId])
# keep track of TIGRFAMs matching the same gene as a PFAM
geneIds = set(geneIdToPfams.keys()).union(set(geneIdToTigr.keys()))
for geneId in geneIds:
pfams = geneIdToPfams.get(geneId, None)
tigrs = geneIdToTigr.get(geneId, None)
if pfams == None or tigrs == None:
continue
for pfamId in pfams:
for tigrId in tigrs:
key = pfamId + '-' + tigrId
if key in matches:
matches[key].add(genomeId)
else:
matches[key] = set([genomeId])
sys.stdout.write('\n')
# find TIGRFAMs that generally hit the same gene as a PFAM
fout = open('../data/pfam/tigrfam2pfam.tsv', 'w')
for key, genomeSet in matches.iteritems():
pfam, tigr = key.split('-')
# deem a TIGRFAM HMM redundant if it is almost always hits that
# same ORF as a PFAM HMM
if float(len(genomeSet)) / len(tigrCount[tigr]) >= percentThreshold:
fout.write(pfam + '\t' + tigr + '\n')
fout.close()
def __init__(self):
self.img = IMG()
self.markerset = MarkerSet()
def __init__(self):
self.img = IMG()
class MarkerSetStabilityTest(object):
def __init__(self):
self.img = IMG()
self.markerset = MarkerSet()
def __processLineage(self, metadata, ubiquityThreshold, singleCopyThreshold, minGenomes, queueIn, queueOut):
"""Assess stability of marker set for a specific named taxonomic group."""
while True:
lineage = queueIn.get(block=True, timeout=None)
if lineage == None:
break
genomeIds = self.img.genomeIdsByTaxonomy(lineage, metadata, 'trusted')
markerGenes = []
perChange = []
numGenomesToSelect = int(0.9*len(genomeIds))
if len(genomeIds) >= minGenomes:
# calculate marker set for all genomes in lineage
geneCountTable = self.img.geneCountTable(genomeIds)
markerGenes = self.markerset.markerGenes(genomeIds, geneCountTable, ubiquityThreshold*len(genomeIds), singleCopyThreshold*len(genomeIds))
tigrToRemove = self.img.identifyRedundantTIGRFAMs(markerGenes)
markerGenes = markerGenes - tigrToRemove
for _ in xrange(0, 100):
# calculate marker set for subset of genomes
subsetGenomeIds = random.sample(genomeIds, numGenomesToSelect)
geneCountTable = self.img.geneCountTable(subsetGenomeIds)
subsetMarkerGenes = self.markerset.markerGenes(subsetGenomeIds, geneCountTable, ubiquityThreshold*numGenomesToSelect, singleCopyThreshold*numGenomesToSelect)
tigrToRemove = self.img.identifyRedundantTIGRFAMs(subsetMarkerGenes)
subsetMarkerGenes = subsetMarkerGenes - tigrToRemove
perChange.append(float(len(markerGenes.symmetric_difference(subsetMarkerGenes)))*100.0 / len(markerGenes))
if perChange != []:
queueOut.put((lineage, len(genomeIds), len(markerGenes), numGenomesToSelect, mean(perChange), std(perChange)))
else:
queueOut.put((lineage, len(genomeIds), len(markerGenes), numGenomesToSelect, -1, -1))
def __storeResults(self, outputFile, totalLineages, writerQueue):
"""Store results to file."""
fout = open(outputFile, 'w')
fout.write('Lineage\t# genomes\t# markers\t# sampled genomes\tmean % change\tstd % change\n')
numProcessedLineages = 0
while True:
lineage, numGenomes, numMarkerGenes, numSampledGenomes, meanPerChange, stdPerChange = writerQueue.get(block=True, timeout=None)
if lineage == None:
break
numProcessedLineages += 1
statusStr = ' Finished processing %d of %d (%.2f%%) lineages.' % (numProcessedLineages, totalLineages, float(numProcessedLineages)*100/totalLineages)
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
fout.write('%s\t%d\t%d\t%d\t%f\t%f\n' % (lineage, numGenomes, numMarkerGenes, numSampledGenomes, meanPerChange, stdPerChange))
sys.stdout.write('\n')
fout.close()
def run(self, outputFile, ubiquityThreshold, singleCopyThreshold, minGenomes, mostSpecificRank, numThreads):
"""Calculate stability of marker sets for named taxonomic groups."""
print ' Testing stability of marker sets:'
random.seed(1)
# process each sequence in parallel
workerQueue = mp.Queue()
writerQueue = mp.Queue()
metadata = self.img.genomeMetadata()
lineages = self.img.lineagesByCriteria(metadata, minGenomes, mostSpecificRank)
for lineage in lineages:
workerQueue.put(lineage)
for _ in range(numThreads):
workerQueue.put(None)
calcProc = [mp.Process(target = self.__processLineage, args = (metadata, ubiquityThreshold, singleCopyThreshold, minGenomes, workerQueue, writerQueue)) for _ in range(numThreads)]
writeProc = mp.Process(target = self.__storeResults, args = (outputFile, len(lineages), writerQueue))
writeProc.start()
for p in calcProc:
p.start()
for p in calcProc:
p.join()
writerQueue.put((None, None, None, None, None, None))
writeProc.join()
def __init__(self):
self.markerSetBuilder = MarkerSetBuilder()
self.img = IMG()
self.simContigLen = 10000
class Simulation(object):
def __init__(self):
self.markerSetBuilder = MarkerSetBuilder()
self.img = IMG()
def run(self):
print '\n Reading reference genome tree.'
treeFile = os.path.join(os.path.dirname(sys.argv[0]), '..', 'data', 'genome_tree', 'genome_tree_prok.refpkg', 'genome_tree.final.tre')
tree = dendropy.Tree.get_from_path(treeFile, schema='newick', as_rooted=True, preserve_underscores=True)
# get all Finished, Trusted genomes
metadata = self.img.genomeMetadata()
bacteriaIds = self.img.getGenomesByClade('domain', 'Bacteria', metadata)
print '# Bacteria: %d' % len(bacteriaIds)
start = time.time()
#self.markerSetBuilder.cachedGeneCountTable = self.img.geneCountTable(metadata.keys())
end = time.time()
print 'globalGeneCountTable: %.2f' % (end - start)
start = time.time()
#self.markerSetBuilder.precomputeGenomeSeqLens(metadata.keys())
end = time.time()
print 'precomputeGenomeSeqLens: %.2f' % (end - start)
start = time.time()
#self.markerSetBuilder.precomputeGenomeFamilyPositions(metadata.keys(), 5000)
end = time.time()
print 'precomputeGenomeFamilyPositions: %.2f' % (end - start)
#start = time.time()
#test = self.img.geneDistTable(metadata.keys(), self.markerSetBuilder.globalGeneCountTable.keys(), spacingBetweenContigs=1e6)
#end = time.time()
#print 'geneDistTable: %.2f' % (end - start)
#t = raw_input('waiting...')
start = time.time()
#testGenomeId = archaeaIds.pop()
#testNode = tree.find_node_with_taxon_label('IMG_' + testGenomeId)
#binMarkerSets, refinedBinMarkerSet = self.markerSetBuilder.buildBinMarkerSet(tree, testNode.parent_node, 0.97, 0.97, bMarkerSet = True)
markerSet = self.markerSetBuilder.buildMarkerSet(bacteriaIds, 0.97, 0.97)
end = time.time()
print 'buildMarkerSet: %.2f' % (end - start)
print len(markerSet.markerSet)
test = eval("[set(['TIGR01080', 'pfam08071', 'pfam00900']), set(['pfam08069', 'pfam00312']), set(['pfam00276', 'pfam00573', 'pfam00297']), set(['pfam00333', 'pfam00327', 'pfam03719']), set(['pfam04563', 'pfam04560', 'pfam04983', 'pfam04566', 'pfam04567', 'pfam04565', 'pfam04997', 'pfam00562', 'pfam05000', 'pfam00623', 'pfam01191', 'pfam04561', 'TIGR02389']), set(['pfam00831', 'pfam01868', 'pfam00189', 'pfam00237']), set(['pfam01280', 'pfam00861', 'pfam01655']), set(['pfam01194', 'pfam00380', 'pfam00572']), set(['TIGR00425', 'pfam08068']), set(['pfam01157', 'pfam03874']), set(['pfam00416', 'TIGR01018']), set(['pfam00679', 'pfam03764']), set(['TIGR00344', 'TIGR03683']), set(['pfam01193', 'pfam01000']), set(['pfam00750', 'pfam05746']), set(['pfam00935', 'pfam01667']), set(['pfam00867', 'pfam00752']), set(['pfam01172', 'pfam09377']), set(['pfam03950', 'pfam00749']), set(['pfam00181', 'pfam03947']), set(['pfam00687', 'pfam00466']), set(['TIGR03679', 'TIGR00289']), set(['pfam01198', 'pfam01912']), set(['pfam00673', 'pfam00281']), set(['TIGR00134']), set(['pfam00410']), set(['pfam00411']), set(['pfam01090']), set(['pfam01092']), set(['pfam04919']), set(['TIGR00336']), set(['pfam01864']), set(['TIGR00442']), set(['pfam01866']), set(['pfam01780']), set(['TIGR01046']), set(['pfam00318']), set(['pfam00252']), set(['pfam09173']), set(['pfam00238']), set(['pfam01798']), set(['pfam01246']), set(['pfam07541']), set(['pfam00736']), set(['TIGR00522']), set(['pfam01269']), set(['TIGR00329']), set(['pfam01015']), set(['TIGR00392']), set(['pfam00203']), set(['TIGR00398']), set(['pfam01725']), set(['pfam02005']), set(['TIGR00422']), set(['pfam03439']), set(['pfam01351']), set(['pfam01922']), set(['pfam11987']), set(['pfam04127']), set(['TIGR00064']), set(['TIGR00389']), set(['pfam13656']), set(['pfam00298']), set(['TIGR00432']), set(['TIGR03677']), set(['pfam00958']), set(['pfam05221']), set(['pfam00347']), set(['TIGR03685']), set(['pfam03876']), set(['pfam01192']), set(['pfam01984']), set(['pfam00827']), set(['pfam01982']), set(['pfam01981']), set(['TIGR00408']), set(['TIGR00270']), set(['TIGR03665']), set(['pfam02978']), set(['pfam03484']), set(['pfam01201']), set(['TIGR02076']), set(['pfam00832']), set(['pfam00833']), set(['TIGR00419']), set(['pfam00177']), set(['pfam06418']), set(['TIGR00057']), set(['TIGR00549']), set(['pfam13685']), set(['pfam05670']), set(['pfam01849']), set(['TIGR02338']), set(['TIGR00468']), set(['pfam09249']), set(['pfam01287']), set(['pfam00164']), set(['pfam01282']), set(['TIGR03724']), set(['pfam01200']), set(['TIGR02153']), set(['TIGR00670']), set(['pfam00398']), set(['TIGR01213']), set(['pfam06026']), set(['pfam04019']), set(['pfam04010']), set(['pfam00366'])]")
print len(test)
for ms in markerSet.markerSet:
bMatch = False
for tms in test:
if tms == ms:
print ms
print tms
print '---------'
bMatch = True
break
if not bMatch:
print 'BOO!'
if str(markerSet.markerSet) == "[set(['TIGR01080', 'pfam08071', 'pfam00900']), set(['pfam08069', 'pfam00312']), set(['pfam00276', 'pfam00573', 'pfam00297']), set(['pfam00333', 'pfam00327', 'pfam03719']), set(['pfam04563', 'pfam04560', 'pfam04983', 'pfam04566', 'pfam04567', 'pfam04565', 'pfam04997', 'pfam00562', 'pfam05000', 'pfam00623', 'pfam01191', 'pfam04561', 'TIGR02389']), set(['pfam00831', 'pfam01868', 'pfam00189', 'pfam00237']), set(['pfam01280', 'pfam00861', 'pfam01655']), set(['pfam01194', 'pfam00380', 'pfam00572']), set(['TIGR00425', 'pfam08068']), set(['pfam01157', 'pfam03874']), set(['pfam00416', 'TIGR01018']), set(['pfam00679', 'pfam03764']), set(['TIGR00344', 'TIGR03683']), set(['pfam01193', 'pfam01000']), set(['pfam00750', 'pfam05746']), set(['pfam00935', 'pfam01667']), set(['pfam00867', 'pfam00752']), set(['pfam01172', 'pfam09377']), set(['pfam03950', 'pfam00749']), set(['pfam00181', 'pfam03947']), set(['pfam00687', 'pfam00466']), set(['TIGR03679', 'TIGR00289']), set(['pfam01198', 'pfam01912']), set(['pfam00673', 'pfam00281']), set(['TIGR00134']), set(['pfam00410']), set(['pfam00411']), set(['pfam01090']), set(['pfam01092']), set(['pfam04919']), set(['TIGR00336']), set(['pfam01864']), set(['TIGR00442']), set(['pfam01866']), set(['pfam01780']), set(['TIGR01046']), set(['pfam00318']), set(['pfam00252']), set(['pfam09173']), set(['pfam00238']), set(['pfam01798']), set(['pfam01246']), set(['pfam07541']), set(['pfam00736']), set(['TIGR00522']), set(['pfam01269']), set(['TIGR00329']), set(['pfam01015']), set(['TIGR00392']), set(['pfam00203']), set(['TIGR00398']), set(['pfam01725']), set(['pfam02005']), set(['TIGR00422']), set(['pfam03439']), set(['pfam01351']), set(['pfam01922']), set(['pfam11987']), set(['pfam04127']), set(['TIGR00064']), set(['TIGR00389']), set(['pfam13656']), set(['pfam00298']), set(['TIGR00432']), set(['TIGR03677']), set(['pfam00958']), set(['pfam05221']), set(['pfam00347']), set(['TIGR03685']), set(['pfam03876']), set(['pfam01192']), set(['pfam01984']), set(['pfam00827']), set(['pfam01982']), set(['pfam01981']), set(['TIGR00408']), set(['TIGR00270']), set(['TIGR03665']), set(['pfam02978']), set(['pfam03484']), set(['pfam01201']), set(['TIGR02076']), set(['pfam00832']), set(['pfam00833']), set(['TIGR00419']), set(['pfam00177']), set(['pfam06418']), set(['TIGR00057']), set(['TIGR00549']), set(['pfam13685']), set(['pfam05670']), set(['pfam01849']), set(['TIGR02338']), set(['TIGR00468']), set(['pfam09249']), set(['pfam01287']), set(['pfam00164']), set(['pfam01282']), set(['TIGR03724']), set(['pfam01200']), set(['TIGR02153']), set(['TIGR00670']), set(['pfam00398']), set(['TIGR01213']), set(['pfam06026']), set(['pfam04019']), set(['pfam04010']), set(['pfam00366'])]":
print 'Good to go!'
else:
print 'oh, shit!!!!'
def __init__(self):
self.simFile = './experiments/simulation.tuning.genus.summary.tsv'
self.looRank = 5
self.markerSetBuilder = MarkerSetBuilder()
self.img = IMG()
class MarkerSetSelection(object):
def __init__(self):
self.simFile = './experiments/simulation.tuning.genus.summary.tsv'
self.looRank = 5
self.markerSetBuilder = MarkerSetBuilder()
self.img = IMG()
def __stabilityTest(self, genomeIds, ubiquityThreshold = 0.97, singleCopyThreshold = 0.97, stabilityThreshold = 0.05):
"""Test stability of marker set for a group of genomes using LOO-testing."""
# quick escape for lineage that are clearly stable
if len(genomeIds) > 200:
return True
# calculate marker sets using a LOO-testing
looMarkerGenes = []
for genomeId in genomeIds:
looGenomeIds = genomeIds.difference([genomeId])
# calculate marker genes
geneCountTable = self.img.geneCountTable(looGenomeIds)
markerGenes = self.markerSetBuilder.markerGenes(looGenomeIds, geneCountTable, ubiquityThreshold*len(looGenomeIds), singleCopyThreshold*len(looGenomeIds))
tigrToRemove = self.img.identifyRedundantTIGRFAMs(markerGenes)
markerGenes = markerGenes - tigrToRemove
looMarkerGenes.append(markerGenes)
# calculate change in marker set for all pairs
markerSetSize = []
diffMarkerSet = []
for i in xrange(0, len(looMarkerGenes)):
markerSetSize.append(len(looMarkerGenes[i]))
for j in xrange(i+1, len(looMarkerGenes)):
symmDiff = looMarkerGenes[i].symmetric_difference(looMarkerGenes[j])
diffMarkerSet.append(len(symmDiff))
print len(genomeIds), mean(diffMarkerSet), mean(markerSetSize)
return (float(mean(diffMarkerSet)) / mean(markerSetSize)) <= stabilityThreshold
def __patristicDist(self, tree, taxa1, taxa2):
mrca = tree.mrca(taxon_labels=[taxa1.taxon.label, taxa2.taxon.label])
if mrca.parent_node == None:
# MRCA is the root of the tree
return taxa1.distance_from_root() + taxa2.distance_from_root()
else:
dist = taxa1.edge_length
parentNode = taxa1.parent_node
while parentNode != mrca:
dist += parentNode.edge_length
parentNode = parentNode.parent_node
dist += taxa2.edge_length
parentNode = taxa2.parent_node
while parentNode != mrca:
dist += parentNode.edge_length
parentNode = parentNode.parent_node
return dist
def __distToNodePercentileTest(self, genomeNode, markerSetNode, leaves, percentileTest):
distToBin = self.__distanceToAncestor(genomeNode, markerSetNode)
distToLeaves = []
for leaf in leaves:
distToLeaves.append(self.__distanceToAncestor(leaf, markerSetNode))
return distToBin < percentile(distToLeaves, percentileTest)
def __selectMarkerSetNode(self, tree, genomeId, metadata, taxonToGenomeIds):
"""Determine lineage-specific marker set to use for assessing the giving genome."""
# read genomes removed from tree as a result of duplicate sequences
duplicateSeqs = self.markerSetBuilder.readDuplicateSeqs()
# determine location of genome in tree
node = tree.find_node_with_taxon_label('IMG_' + genomeId)
# ascend tree to root looking for suitable marker set
curNode = node.parent_node
while curNode != None:
uniqueId = curNode.label.split('|')[0]
genomeIds = set()
for leaf in curNode.leaf_nodes():
genomeIds.add(leaf.taxon.label.replace('IMG_', ''))
duplicateGenomes = duplicateSeqs.get(leaf.taxon.label, [])
for dup in duplicateGenomes:
genomeIds.add(dup.replace('IMG_', ''))
# remove genome (LOO-style analysis)
print 'Full:', len(genomeIds)
genomeIds.difference_update([genomeId])
print 'LOO:', len(genomeIds)
# remove all genomes from the same taxonomic group as the genome of interest
taxon = metadata[genomeId]['taxonomy'][self.looRank]
genomeIds.difference_update(taxonToGenomeIds[taxon])
print 'Rank reduced:', len(genomeIds)
print uniqueId
if len(genomeIds) > 10 and self.__stabilityTest(genomeIds):
uidSelected = uniqueId
break
curNode = curNode.parent_node
if curNode == None:
# reach root so use universal marker set
uidSelected = uniqueId
return uidSelected
def __bestMarkerSet(self, genomeId, simResults):
"""Get stats for best marker set."""
curBest = 1000
bestUID = None
for uid, results in simResults[genomeId].iteritems():
numDescendants, dComp, dCont = results
if (dComp + dCont) < curBest:
numDescendantsBest = numDescendants
dCompBest = dComp
dContBest = dCont
bestUID = uid
curBest = dComp + dCont
return bestUID, numDescendantsBest, dCompBest, dContBest
def __workerThread(self, tree, simResults, metadata, taxonToGenomeIds, queueIn, queueOut):
"""Process each data item in parallel."""
while True:
testGenomeId = queueIn.get(block=True, timeout=None)
if testGenomeId == None:
break
uidSelected = self.__selectMarkerSetNode(tree, testGenomeId, metadata, taxonToGenomeIds)
numDescendantsSelected, dCompSelected, dContSelected = simResults[testGenomeId][uidSelected]
# find best marker set
bestUID, numDescendantsBest, dCompBest, dContBest = self.__bestMarkerSet(testGenomeId, simResults)
queueOut.put((testGenomeId, uidSelected, numDescendantsSelected, dCompSelected, dContSelected, bestUID, numDescendantsBest, dCompBest, dContBest))
def __writerThread(self, numTestGenomes, writerQueue):
"""Store or write results of worker threads in a single thread."""
fout = open('./experiments/markerSetSelection.tsv', 'w')
fout.write('Genome Id\tSelected UID\t# descendants\tSelected dComp\tSelected dCont\tBest UID\t# descendants\tBest dComp\tBest dCont\tdDescendants\tdComp\tdCont\n')
itemsToProcess = 0
dComps = []
dConts = []
dCompsPer = []
dContsPer = []
bestComp = []
bestCont = []
selectedComp = []
selectedCont = []
while True:
testGenomeId, uidSelected, numDescendantsSelected, dCompSelected, dContSelected, bestUID, numDescendantsBest, dCompBest, dContBest = writerQueue.get(block=True, timeout=None)
if testGenomeId == None:
break
itemsToProcess += 1
statusStr = ' Finished processing %d of %d (%.2f%%) test genomes.' % (itemsToProcess, numTestGenomes, float(itemsToProcess)*100/(numTestGenomes))
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
dComp = abs(dCompSelected - dCompBest)
dCont = abs(dContSelected - dContBest)
dDescendants = abs(numDescendantsSelected - numDescendantsBest)
fout.write('%s\t%s\t%d\t%.4f\t%.4f\t%s\t%d\t%.4f\t%.4f\t%d\t%.4f\t%.4f\n' % (testGenomeId, uidSelected, numDescendantsSelected, dCompSelected, dContSelected, bestUID, numDescendantsBest, dCompBest, dContBest, dDescendants, dComp, dCont))
dComps.append(dComp)
dConts.append(dCont)
dCompsPer.append(dComp*100.0 / dCompBest)
dContsPer.append(dCont*100.0 / max(dContBest, 0.01))
bestComp.append(dCompBest)
bestCont.append(dContBest)
selectedComp.append(dCompSelected)
selectedCont.append(dContSelected)
sys.stdout.write('\n')
fout.close()
print ''
print ' General results:'
print ' Best comp: %.2f +/- %.2f' % (mean(bestComp), std(bestComp))
print ' Best cont: %.2f +/- %.2f' % (mean(bestCont), std(bestCont))
print ' Selected comp: %.2f +/- %.2f' % (mean(selectedComp), std(selectedComp))
print ' Selected cont: %.2f +/- %.2f' % (mean(selectedCont), std(selectedCont))
print ''
print ' Delta comp: %.2f +/- %.2f' % (mean(dComps), std(dComps))
print ' Delta cont: %.2f +/- %.2f' % (mean(dConts), std(dConts))
print ' Delta comp per error: %.1f +/- %.1f' % (mean(dCompsPer), std(dCompsPer))
print ' Delta cont per error: %.1f +/- %.1f' % (mean(dContsPer), std(dContsPer))
def __distanceToAncestor(self, leaf, ancestor):
dist = 0
curNode = leaf
while curNode != ancestor:
dist += curNode.edge_length
curNode = curNode.parent_node
return dist
def __bestNodeProperties(self, genomeId, tree, bestUID):
# determine location of genome in tree
node = tree.find_node_with_taxon_label('IMG_' + genomeId)
# find node of best marker set
curNode = node.parent_node
nodesToBin = 0
distanceToBin = node.edge_length
distanceToLeaves = []
while curNode != None:
uniqueId = curNode.label.split('|')[0]
nodesToBin += 1
if uniqueId == bestUID:
for leaf in curNode.leaf_nodes():
if leaf != node:
dist = self.__distanceToAncestor(leaf, curNode)
distanceToLeaves.append(dist)
break
distanceToBin += curNode.edge_length
curNode = curNode.parent_node
return nodesToBin, distanceToBin, mean(distanceToLeaves)
def __propertiesOfBestMarkerSets(self, tree, simResults):
numDescendants = []
nodesToBin = []
distanceToBin = []
avgDistanceToLeaf = []
percDiffs = []
for genomeId in simResults:
bestUID, numDescendantsBest, _, _ = self.__bestMarkerSet(genomeId, simResults)
nodesToBinBest, distanceToBinBest, avgDistanceToLeafBest = self.__bestNodeProperties(genomeId, tree, bestUID)
numDescendants.append(numDescendantsBest)
nodesToBin.append(nodesToBinBest)
distanceToBin.append(distanceToBinBest)
avgDistanceToLeaf.append(avgDistanceToLeafBest)
percDiff = abs(distanceToBinBest - avgDistanceToLeafBest) * 100 / distanceToBinBest
percDiffs.append(percDiff)
print ' # descendants: %.2f +/- %.2f' % (mean(numDescendants), std(numDescendants))
print ' # nodes to bin: %.2f +/- %.2f' % (mean(nodesToBin), std(nodesToBin))
print ' Distance to bin: %.2f +/- %.2f' % (mean(distanceToBin), std(distanceToBin))
distanceToBin = array(distanceToBin)
avgDistanceToLeaf = array(avgDistanceToLeaf)
print ' Distance to bin - average distance to leaf: %.2f +/- %.2f' % (mean(abs(distanceToBin - avgDistanceToLeaf)), std(abs(distanceToBin - avgDistanceToLeaf)))
print ' Percent difference to average leaf distance: %.2f +/- %.2f' % (mean(percDiffs), std(percDiffs))
print ''
def run(self, numThreads):
# read reference tree
print '\n Reading reference genome tree.'
treeFile = os.path.join(os.path.dirname(sys.argv[0]), '..', 'data', 'genome_tree', 'genome_tree_prok.refpkg', 'genome_tree.final.tre')
tree = dendropy.Tree.get_from_path(treeFile, schema='newick', as_rooted=True, preserve_underscores=True)
# get all genomes with a given taxon label
metadata = self.img.genomeMetadata()
taxonToGenomeIds = defaultdict(set)
for genomeId in metadata:
for t in metadata[genomeId]['taxonomy']:
taxonToGenomeIds[t].add(genomeId)
# read simulation results
print ' Reading simulation results.'
simResults = defaultdict(dict)
with open(self.simFile) as f:
f.readline()
for line in f:
lineSplit = line.split('\t')
simId = lineSplit[0] + '-' + lineSplit[1] + '-' + lineSplit[2] + '-' + lineSplit[3]
uid = lineSplit[5].split('|')[0].strip()
numDescendants = int(lineSplit[6])
comp = float(lineSplit[21])
cont = float(lineSplit[23])
simResults[simId][uid] = [numDescendants, comp, cont]
#print ''
#print ' Properties of best marker sets:'
#self.__propertiesOfBestMarkerSets(tree, simResults)
print ' Evaluating %d test genomes.' % len(simResults)
workerQueue = mp.Queue()
writerQueue = mp.Queue()
for testGenomeId in simResults:
workerQueue.put(testGenomeId)
for _ in range(numThreads):
workerQueue.put(None)
workerProc = [mp.Process(target = self.__workerThread, args = (tree, simResults, metadata, taxonToGenomeIds, workerQueue, writerQueue)) for _ in range(numThreads)]
writeProc = mp.Process(target = self.__writerThread, args = (len(simResults), writerQueue))
writeProc.start()
for p in workerProc:
p.start()
for p in workerProc:
p.join()
writerQueue.put((None, None, None, None, None, None, None, None, None))
writeProc.join()