Mercurial > repos > vipints > deseq_hts

comparison dexseq-hts_1.0/src/dexseq_prepare_annotation.py @ 11:cec4b4fb30be draft default tip

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DEXSeq version 1.6 added
author vipints <vipin@cbio.mskcc.org>
date Tue, 08 Oct 2013 08:22:45 -0400
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10:2fe512c7bfdf 11:cec4b4fb30be
1 import sys, collections, itertools, os.path
2
3 if len( sys.argv ) != 3:
4 sys.stderr.write( "Script to prepare annotation for DEXSeq.\n\n" )
5 sys.stderr.write( "Usage: python %s <in.gtf> <out.gff>\n\n" % os.path.basename(sys.argv[0]) )
6 sys.stderr.write( "This script takes an annotation file in Ensembl GTF format\n" )
7 sys.stderr.write( "and outputs a 'flattened' annotation file suitable for use\n" )
8 sys.stderr.write( "with the count_in_exons.py script.\n" )
9 sys.exit(1)
10
11 try:
12 import HTSeq
13 except ImportError:
14 sys.stderr.write( "Could not import HTSeq. Please install the HTSeq Python framework\n" )
15 sys.stderr.write( "available from http://www-huber.embl.de/users/anders/HTSeq\n" )
16 sys.exit(1)
17
18 gtf_file = sys.argv[1]
19 out_file = sys.argv[2]
20
21 ## make sure that it can handle GFF files.
22 parent_child_map = dict()
23 for feature in HTSeq.GFF_Reader( gtf_file ):
24 if feature.type in ['mRNA',
25 'transcript',
26 'ncRNA',
27 'miRNA',
28 'pseudogenic_transcript',
29 'rRNA',
30 'snoRNA',
31 'snRNA',
32 'tRNA',
33 'scRNA']:
34 parent_child_map[feature.attr['ID']] = feature.attr['Parent']
35
36 # Step 1: Store all exons with their gene and transcript ID
37 # in a GenomicArrayOfSets
38
39 exons = HTSeq.GenomicArrayOfSets( "auto", stranded=True )
40 for f in HTSeq.GFF_Reader( gtf_file ):
41 if not f.type in ["exon", "pseudogenic_exon"]:
42 continue
43 if not f.attr.get('gene_id'):
44 f.attr['gene_id'] = parent_child_map[f.attr['Parent']]
45 f.attr['transcript_id'] = f.attr['Parent']
46 f.attr['gene_id'] = f.attr['gene_id'].replace( ":", "_" )
47 exons[f.iv] += ( f.attr['gene_id'], f.attr['transcript_id'] )
48
49 # Step 2: Form sets of overlapping genes
50
51 # We produce the dict 'gene_sets', whose values are sets of gene IDs. Each set
52 # contains IDs of genes that overlap, i.e., share bases (on the same strand).
53 # The keys of 'gene_sets' are the IDs of all genes, and each key refers to
54 # the set that contains the gene.
55 # Each gene set forms an 'aggregate gene'.
56
57 gene_sets = collections.defaultdict( lambda: set() )
58 for iv, s in exons.steps():
59 # For each step, make a set, 'full_set' of all the gene IDs occuring
60 # in the present step, and also add all those gene IDs, whch have been
61 # seen earlier to co-occur with each of the currently present gene IDs.
62 full_set = set()
63 for gene_id, transcript_id in s:
64 full_set.add( gene_id )
65 full_set |= gene_sets[ gene_id ]
66 # Make sure that all genes that are now in full_set get associated
67 # with full_set, i.e., get to know about their new partners
68 for gene_id in full_set:
69 assert gene_sets[ gene_id ] <= full_set
70 gene_sets[ gene_id ] = full_set
71
72
73 # Step 3: Go through the steps again to get the exonic sections. Each step
74 # becomes an 'exonic part'. The exonic part is associated with an
75 # aggregate gene, i.e., a gene set as determined in the previous step,
76 # and a transcript set, containing all transcripts that occur in the step.
77 # The results are stored in the dict 'aggregates', which contains, for each
78 # aggregate ID, a list of all its exonic_part features.
79
80 aggregates = collections.defaultdict( lambda: list() )
81 for iv, s in exons.steps( ):
82 # Skip empty steps
83 if len(s) == 0:
84 continue
85 # Take one of the gene IDs, find the others via gene sets, and
86 # form the aggregate ID from all of them
87 gene_id = list(s)[0][0]
88 assert set( gene_id for gene_id, transcript_id in s ) <= gene_sets[ gene_id ]
89 aggregate_id = '+'.join( gene_sets[ gene_id ] )
90 # Make the feature and store it in 'aggregates'
91 f = HTSeq.GenomicFeature( aggregate_id, "exonic_part", iv )
92 f.source = os.path.basename( sys.argv[1] )
93 f.attr = {}
94 f.attr[ 'gene_id' ] = aggregate_id
95 transcript_set = set( ( transcript_id for gene_id, transcript_id in s ) )
96 f.attr[ 'transcripts' ] = '+'.join( transcript_set )
97 aggregates[ aggregate_id ].append( f )
98
99
100 # Step 4: For each aggregate, number the exonic parts
101
102 aggregate_features = []
103 for l in aggregates.values():
104 for i in xrange( len(l)-1 ):
105 assert l[i].name == l[i+1].name, str(l[i+1]) + " has wrong name"
106 assert l[i].iv.end <= l[i+1].iv.start, str(l[i+1]) + " starts too early"
107 if l[i].iv.chrom != l[i+1].iv.chrom:
108 raise ValueError, "Same name found on two chromosomes: %s, %s" % ( str(l[i]), str(l[i+1]) )
109 if l[i].iv.strand != l[i+1].iv.strand:
110 raise ValueError, "Same name found on two strands: %s, %s" % ( str(l[i]), str(l[i+1]) )
111 aggr_feat = HTSeq.GenomicFeature( l[0].name, "aggregate_gene",
112 HTSeq.GenomicInterval( l[0].iv.chrom, l[0].iv.start,
113 l[-1].iv.end, l[0].iv.strand ) )
114 aggr_feat.source = os.path.basename( sys.argv[1] )
115 aggr_feat.attr = { 'gene_id': aggr_feat.name }
116 for i in xrange( len(l) ):
117 l[i].attr['exonic_part_number'] = "%03d" % ( i+1 )
118 aggregate_features.append( aggr_feat )
119
120
121 # Step 5: Sort the aggregates, then write everything out
122
123 aggregate_features.sort( key = lambda f: ( f.iv.chrom, f.iv.start ) )
124
125 fout = open( out_file, "w" )
126 for aggr_feat in aggregate_features:
127 fout.write( aggr_feat.get_gff_line() )
128 for f in aggregates[ aggr_feat.name ]:
129 fout.write( f.get_gff_line() )
130
131 fout.close()