def __init__(self):
self.config = Utils.loadConfig()
self.task = self.config.get('eval', 'task')
self.gold = self.config.get('eval', 'goldID.path')
self.result = self.config.get('eval', 'result.path')
self.threshold = float(self.config.get('eval', 'threshold'))
self.sparkContext = SparkContext(conf=Utils.getSparkConf('filter'))
self.Similarity = Similarity.Similarity(self.config)
self.Filter = Filter.Filter(self.config,
sparkContext=self.sparkContext)
self.Merger = Merger.Merger(self.config)
self.goldIDs = Utils.readFileLines(self.gold)[1:]
self.resultFiles = Utils.listFilesExt(self.result, 'IDs.test')
# total nb of gold genes
self.nbGoldGenes = len(self.goldIDs)
# total nb of gold clusters
self.foldedGold = Utils.foldClusterData(self.goldIDs, 'gold', 0)
self.goldGenes = [
gene for genes in self.foldedGold.values() for gene in genes
]
self.nbGoldClusters = len(self.foldedGold)
self.outputheader = 'goldClusterID\tgoldGeneID\tpredictedClusterLabel\tpredictedClusterID\n'
self.scoreheader = 'goldClusterID\tpredictedClusterID\tclusterScore\n'
def clustersToGFF(clusterspath, gffpath, goldpath, annotpath, source_type):
gffcontent = Gff3(gffpath)
clustercontent, goldContent, annotationContent = "", "", ""
clustercontent = Utils.readFileLines(clusterspath)
clusters = Utils.foldClusterData(
clustercontent, "",
0.5) if 'score' in clusterspath else Utils.foldClusterData(
clustercontent, "gold", "")
goldContent = '\t'.join(Utils.readFileLines(goldpath)) if goldpath else ""
annotationList = Utils.readFileLines(annotpath) if annotpath else ""
annotationContent = ('\n').join(annotationList) if annotpath else ""
# sort dict by key
clusters = OrderedDict(sorted(clusters.items(), key=lambda x: x[0]))
gffclusterfile = clusterspath.rsplit('.', 1)[0] + '.percluster.gff3'
gffgenefile = clusterspath.rsplit('.', 1)[0] + '.pergene.gff3'
outputcluster, outputgene = "##gff-version 3\n", "##gff-version 3\n"
# filter only "mRNA" features, return dict {gene name, gff line}
mRNAdict = {
line['attributes']['Name'].replace('.1', ''): line
for line in gffcontent.lines if line['type'] == 'mRNA'
}
for key, value in clusters.items():
for gene in value:
gene = gene.replace('.1', '')
thisgene = mRNAdict.get(gene)
if (thisgene is not None):
chr = thisgene['seqid']
position = str(thisgene['start']) + '\t' + str(thisgene['end'])
score = '?'
strand = thisgene['strand']
phase = thisgene['phase']
info = 'Name=' + gene + ';Note=' + key + '\n'
if (goldContent):
if (gene in annotationContent):
annot = [
item for item in annotationList if gene in item
]
annot = annot[0].split('\t')[1] if annot else ''
if ('backbone' in annot):
info = info.replace("\n",
";color=#EE0000\n") # red
elif ('tailor' in annot):
info = info.replace("\n",
";color=#EE9300\n") # orange
elif ('transcript') in annot:
info = info.replace(
"\n", ";color=#048014\n") # forest green
elif ('transport' in annot):
info = info.replace(
"\n", ";color=#1888f0\n") # light blue
elif (gene in goldContent):
info = info.replace(
"\n", ";color=#9931f2\n") # bright purple
outputgene += chr + '\t' + source_type + '\t' + position + '\t' + score + '\t' + strand + '\t' + phase + '\t' + info
else:
print('gene not found:', gene)
startID = value[0].replace('.1', '')
endID = value[-1].replace('.1', '')
startGene = mRNAdict.get(startID)
endGene = mRNAdict.get(endID)
chr = startGene['seqid']
position = str(startGene['start']) + '\t' + str(endGene['end'])
strand = startGene['strand']
phase = startGene['phase']
score = '?'
info = 'Name=' + key + ';Note=' + ('|').join(value) + '\n'
outputcluster += chr + '\t' + source_type + '\t' + position + '\t' + score + '\t' + strand + '\t' + phase + '\t' + info
Utils.writeFile(gffclusterfile, outputcluster)
Utils.writeFile(gffgenefile, outputgene)
return gffcontent
