diff GFFtools-GX/GFFParser.py @ 3:ff2c2e6f4ab3

Uploaded version 2.0.0 of gfftools ready to import to local instance

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/GFFtools-GX/GFFParser.py	Wed Jun 11 16:29:25 2014 -0400
@@ -0,0 +1,491 @@
+#!/usr/bin/env python
+"""
+Extract genome annotation from a GFF (a tab delimited format for storing sequence features and annotations) file.
+
+Requirements: 
+    Numpy :- http://numpy.org/ 
+    Scipy :- http://scipy.org/ 
+
+Copyright (C)	
+
[phone] Friedrich Miescher Laboratory of the Max Planck Society, Tubingen, Germany. 
[phone] Memorial Sloan Kettering Cancer Center, New York City, USA.
+"""
+
+import re
+import os
+import sys
+import urllib
+import numpy as np
+import scipy.io as sio
+from collections import defaultdict
+import helper as utils 
+
+def attribute_tags(col9):
+    """ 
+    Split the key-value tags from the attribute column, it takes column number 9 from GTF/GFF file 
+
+    @args col9: attribute column from GFF file 
+    @type col9: str
+    """
+    info = defaultdict(list)
+    is_gff = False
+    
+    if not col9:
+        return is_gff, info
+        
+    # trim the line ending semi-colon  ucsc may have some white-space  
+    col9 = col9.rstrip(';| ')
+    # attributes from 9th column 
+    atbs = col9.split(" ; ")
+    if len(atbs) == 1:
+        atbs = col9.split("; ")
+        if len(atbs) == 1:
+            atbs = col9.split(";")
+    # check the GFF3 pattern which has key value pairs like:
+    gff3_pat = re.compile("\w+=")
+    # sometime GTF have: gene_id uc002zkg.1;
+    gtf_pat = re.compile("\s?\w+\s")
+
+    key_vals = []
+
+    if gff3_pat.match(atbs[0]): # gff3 pattern 
+        is_gff = True
+        key_vals = [at.split('=') for at in atbs]
+    elif gtf_pat.match(atbs[0]): # gtf pattern
+        for at in atbs:
+            key_vals.append(at.strip().split(" ",1))
+    else:
+        # to handle attribute column has only single value 
+        key_vals.append(['ID', atbs[0]])
+    # get key, val items 
+    for item in key_vals:
+        key, val = item
+        # replace the double qoutes from feature identifier 
+        val = re.sub('"', '', val)
+        # replace the web formating place holders to plain text format 
+        info[key].extend([urllib.unquote(v) for v in val.split(',') if v])
+
+    return is_gff, info
+                
+def spec_features_keywd(gff_parts):
+    """
+    Specify the feature key word according to the GFF specifications
+
+    @args gff_parts: attribute field key 
+    @type gff_parts: str 
+    """
+    for t_id in ["transcript_id", "transcriptId", "proteinId"]:
+        try:
+            gff_parts["info"]["Parent"] = gff_parts["info"][t_id]
+            break
+        except KeyError:
+            pass
+    for g_id in ["gene_id", "geneid", "geneId", "name", "gene_name", "genename"]:
+        try:
+            gff_parts["info"]["GParent"] = gff_parts["info"][g_id]
+            break
+        except KeyError:
+            pass
+    ## TODO key words
+    for flat_name in ["Transcript", "CDS"]:
+        if gff_parts["info"].has_key(flat_name):
+            # parents
+            if gff_parts['type'] in [flat_name] or re.search(r'transcript', gff_parts['type'], re.IGNORECASE):
+                if not gff_parts['id']:
+                    gff_parts['id'] = gff_parts['info'][flat_name][0]
+                    #gff_parts["info"]["ID"] = [gff_parts["id"]]
+            # children 
+            elif gff_parts["type"] in ["intron", "exon", "three_prime_UTR",
+                        "coding_exon", "five_prime_UTR", "CDS", "stop_codon",
+                        "start_codon"]:
+                gff_parts["info"]["Parent"] = gff_parts["info"][flat_name]
+            break
+    return gff_parts
+
+def Parse(ga_file):
+    """
+    Parsing GFF/GTF file based on feature relationship, it takes the input file.
+
+    @args ga_file: input file name 
+    @type ga_file: str 
+    """
+    child_map = defaultdict(list)
+    parent_map = dict()
+
+    ga_handle = utils.open_file(ga_file)
+
+    for rec in ga_handle:
+        rec = rec.strip('\n\r')
+
+        # skip empty line fasta identifier and commented line
+        if not rec or rec[0] in  ['#', '>']:
+            continue
+        # skip the genome sequence 
+        if not re.search('\t', rec):
+            continue
+
+        parts = rec.split('\t')
+        assert len(parts) >= 8, rec
+
+        # process the attribute column (9th column)
+        ftype, tags = attribute_tags(parts[-1])
+        if not tags: # skip the line if no attribute column.
+	        continue 
+        
+        # extract fields  
+        if parts[1]:
+            tags["source"] = parts[1]
+        if parts[7]:
+            tags["phase"] = parts[7]
+
+        gff_info = dict()
+        gff_info['info'] = dict(tags)
+        #gff_info["is_gff3"] = ftype
+        gff_info['chr'] = parts[0]
+        gff_info['score'] = parts[5]
+
+        if parts[3] and parts[4]:
+            gff_info['location'] = [int(parts[3]) ,
+                        int(parts[4])]
+            gff_info['type'] = parts[2]
+            gff_info['id'] = tags.get('ID', [''])[0]
+            if parts[6] in ['?', '.']:
+                parts[6] = None 
+            gff_info['strand'] = parts[6]
+
+            # key word according to the GFF spec.
+            if not ftype:
+                gff_info = spec_features_keywd(gff_info)
+        
+            # link the feature relationships
+            if gff_info['info'].has_key('Parent'): 
+                for p in gff_info['info']['Parent']:
+                    if p == gff_info['id']:
+                        gff_info['id'] = ''
+                        break
+                rec_category = 'child'
+            elif gff_info['id']:
+                rec_category = 'parent'
+            else:
+                rec_category = 'record'
+
+            # depends on the record category organize the features
+            if rec_category == 'child':
+                for p in gff_info['info']['Parent']:
+                    # create the data structure based on source and feature id 
+                    child_map[(gff_info['chr'], gff_info['info']['source'], p)].append(
+                                            dict( type = gff_info['type'], 
+                                            location =  gff_info['location'], 
+                                            strand = gff_info['strand'], 
+                                            score = gff_info['score'], 
+                                            ID = gff_info['id'],
+                                            gene_id = gff_info['info'].get('GParent', '') 
+                                            ))
+            elif rec_category == 'parent':
+                parent_map[(gff_info['chr'], gff_info['info']['source'], gff_info['id'])] = dict( 
+                                            type = gff_info['type'], 
+                                            location = gff_info['location'],
+                                            strand = gff_info['strand'],
+                                            score = gff_info['score'], 
+                                            name = tags.get('Name', [''])[0])
+            elif rec_category == 'record':
+                #TODO how to handle plain records?
+                c = 1 
+    ga_handle.close()
+    
+    # depends on file type create parent feature  
+    if not ftype:
+        parent_map, child_map = create_missing_feature_type(parent_map, child_map)    
+    
+    # connecting parent child relations  
+    # // essentially the parent child features are here from any type of GTF/GFF2/GFF3 file
+    gene_mat = format_gene_models(parent_map, child_map) 
+
+    return gene_mat 
+    
+def format_gene_models(parent_nf_map, child_nf_map): 
+    """
+    Genarate GeneObject based on the parsed file contents
+
+    @args parent_nf_map: parent features with source and chromosome information 
+    @type parent_nf_map: collections defaultdict
+    @args child_nf_map: transctipt and exon information are encoded 
+    @type child_nf_map: collections defaultdict
+    """
+    g_cnt = 0 
+    gene = np.zeros((len(parent_nf_map),), dtype = utils.init_gene())
+
+    for pkey, pdet in parent_nf_map.items():
+
+        # considering only gene features 
+        if not re.search(r'gene', pdet.get('type', '')):
+            continue 
+        # infer the gene start and stop if not there in the 
+        if not pdet.get('location', []):
+            GNS, GNE = [], []
+            # multiple number of transcripts 
+            for L1 in child_nf_map[pkey]:
+                GNS.append(L1.get('location', [])[0]) 
+                GNE.append(L1.get('location', [])[1]) 
+            GNS.sort()
+            GNE.sort()
+            pdet['location'] = [GNS[0], GNE[-1]]
+        orient = pdet.get('strand', '')
+
+        gene[g_cnt]['id'] = g_cnt +1 
+        gene[g_cnt]['chr'] = pkey[0]
+        gene[g_cnt]['source'] = pkey[1]
+        gene[g_cnt]['name'] = pkey[-1]
+        gene[g_cnt]['start'] = pdet.get('location', [])[0]
+        gene[g_cnt]['stop'] = pdet.get('location', [])[1]
+        gene[g_cnt]['strand'] = orient  
+        gene[g_cnt]['score'] = pdet.get('score','')
+        
+        # default value 
+        gene[g_cnt]['is_alt_spliced'] = gene[g_cnt]['is_alt'] = 0
+        if len(child_nf_map[pkey]) > 1:
+            gene[g_cnt]['is_alt_spliced'] = gene[g_cnt]['is_alt'] = 1
+
+        # complete sub-feature for all transcripts 
+        dim = len(child_nf_map[pkey])
+        TRS = np.zeros((dim,), dtype=np.object)
+        TR_TYP = np.zeros((dim,), dtype=np.object)
+        EXON = np.zeros((dim,), dtype=np.object)
+        UTR5 = np.zeros((dim,), dtype=np.object)
+        UTR3 = np.zeros((dim,), dtype=np.object)
+        CDS = np.zeros((dim,), dtype=np.object)
+        TISc = np.zeros((dim,), dtype=np.object)
+        TSSc = np.zeros((dim,), dtype=np.object)
+        CLV = np.zeros((dim,), dtype=np.object)
+        CSTOP = np.zeros((dim,), dtype=np.object)
+        TSTAT = np.zeros((dim,), dtype=np.object)
+
+        # fetching corresponding transcripts 
+        for xq, Lv1 in enumerate(child_nf_map[pkey]):
+
+            TID = Lv1.get('ID', '')
+            TRS[xq]= np.array([TID])
+
+            TYPE = Lv1.get('type', '')
+            TR_TYP[xq] = np.array('')
+            TR_TYP[xq] = np.array(TYPE) if TYPE else TR_TYP[xq]
+
+            orient = Lv1.get('strand', '')
+
+            # fetching different sub-features 
+            child_feat = defaultdict(list)
+            for Lv2 in child_nf_map[(pkey[0], pkey[1], TID)]:
+                E_TYP = Lv2.get('type', '')
+                child_feat[E_TYP].append(Lv2.get('location'))
+            
+            # make general ascending order of coordinates 
+            if orient == '-':
+                for etype, excod in child_feat.items():
+                    if len(excod) > 1:
+                        if excod[0][0] > excod[-1][0]:
+                            excod.reverse()
+                            child_feat[etype] = excod
+
+            # make exon coordinate from cds and utr regions 
+            if not child_feat.get('exon'):  
+                if child_feat.get('CDS'):
+                    exon_cod = utils.make_Exon_cod( orient, 
+                                NonetoemptyList(child_feat.get('five_prime_UTR')), 
+                                NonetoemptyList(child_feat.get('CDS')),
+                                NonetoemptyList(child_feat.get('three_prime_UTR')))
+                    child_feat['exon'] = exon_cod 
+                else: 
+                    # TODO only UTR's
+                    # searching through keys to find a pattern describing exon feature 
+                    ex_key_pattern = [k for k in child_feat if k.endswith("exon")]
+                    if ex_key_pattern:
+                        child_feat['exon'] = child_feat[ex_key_pattern[0]]
+
+            # stop_codon are seperated from CDS, add the coordinates based on strand
+            if child_feat.get('stop_codon'):
+                if orient == '+':
+                    if child_feat.get('stop_codon')[0][0] - child_feat.get('CDS')[-1][1] == 1:
+                        child_feat['CDS'][-1] = [child_feat.get('CDS')[-1][0], child_feat.get('stop_codon')[0][1]]
+                    else:
+                        child_feat['CDS'].append(child_feat.get('stop_codon')[0])
+                elif orient == '-':
+                    if child_feat.get('CDS')[0][0] - child_feat.get('stop_codon')[0][1] == 1:
+                        child_feat['CDS'][0] = [child_feat.get('stop_codon')[0][0], child_feat.get('CDS')[0][1]]
+                    else:
+                        child_feat['CDS'].insert(0, child_feat.get('stop_codon')[0])
+
+            # transcript signal sites 
+            TIS, cdsStop, TSS, cleave = [], [], [], []
+            cds_status, exon_status, utr_status = 0, 0, 0
+
+            if child_feat.get('exon'):
+                TSS = [child_feat.get('exon')[-1][1]]
+                TSS = [child_feat.get('exon')[0][0]] if orient == '+' else TSS 
+                cleave = [child_feat.get('exon')[0][0]]
+                cleave = [child_feat.get('exon')[-1][1]] if orient == '+' else cleave
+                exon_status = 1
+
+            if child_feat.get('CDS'):
+                if orient == '+': 
+                    TIS = [child_feat.get('CDS')[0][0]]
+                    cdsStop = [child_feat.get('CDS')[-1][1]-3]
+                else:
+                    TIS = [child_feat.get('CDS')[-1][1]]
+                    cdsStop = [child_feat.get('CDS')[0][0]+3]
+                cds_status = 1 
+                # cds phase calculation 
+                child_feat['CDS'] = utils.add_CDS_phase(orient, child_feat.get('CDS'))
+            
+            # sub-feature status 
+            if child_feat.get('three_prime_UTR') or child_feat.get('five_prime_UTR'):
+                utr_status =1 
+            
+            if utr_status == cds_status == exon_status == 1: 
+                t_status = 1
+            else:
+                t_status = 0
+            
+            # add sub-feature # make array for export to different out
+            TSTAT[xq] = t_status
+            EXON[xq] = np.array(child_feat.get('exon'), np.float64)
+            UTR5[xq] = np.array(NonetoemptyList(child_feat.get('five_prime_UTR')))
+            UTR3[xq] = np.array(NonetoemptyList(child_feat.get('three_prime_UTR')))
+            CDS[xq] = np.array(NonetoemptyList(child_feat.get('CDS')))
+            TISc[xq] = np.array(TIS)
+            CSTOP[xq] = np.array(cdsStop)
+            TSSc[xq] = np.array(TSS)
+            CLV[xq] = np.array(cleave)
+            
+        # add sub-features to the parent gene feature
+        gene[g_cnt]['transcript_status'] = TSTAT
+        gene[g_cnt]['transcripts'] = TRS 
+        gene[g_cnt]['exons'] = EXON
+        gene[g_cnt]['utr5_exons'] = UTR5 
+        gene[g_cnt]['cds_exons'] = CDS 
+        gene[g_cnt]['utr3_exons'] = UTR3 
+        gene[g_cnt]['transcript_type'] = TR_TYP
+        gene[g_cnt]['tis'] = TISc
+        gene[g_cnt]['cdsStop'] = CSTOP
+        gene[g_cnt]['tss'] = TSSc
+        gene[g_cnt]['cleave'] = CLV
+        
+        gene[g_cnt]['gene_info'] = dict( ID = pkey[-1], 
+                                Name = pdet.get('name'), 
+                                Source = pkey[1]) 
+        # few empty fields // TODO fill this:
+        gene[g_cnt]['anno_id'] = []
+        gene[g_cnt]['confgenes_id'] = []
+        gene[g_cnt]['alias'] = ''
+        gene[g_cnt]['name2'] = []
+        gene[g_cnt]['chr_num'] = []
+        gene[g_cnt]['paralogs'] = []
+        gene[g_cnt]['transcript_info'] = []
+        gene[g_cnt]['transcript_valid'] = []
+        gene[g_cnt]['exons_confirmed'] = []
+        gene[g_cnt]['tis_conf'] = []
+        gene[g_cnt]['tis_info'] = []
+        gene[g_cnt]['cdsStop_conf'] = []
+        gene[g_cnt]['cdsStop_info'] = []
+        gene[g_cnt]['tss_info'] = []
+        gene[g_cnt]['tss_conf'] = []
+        gene[g_cnt]['cleave_info'] = []
+        gene[g_cnt]['cleave_conf'] = []
+        gene[g_cnt]['polya_info'] = []
+        gene[g_cnt]['polya_conf'] = []
+        gene[g_cnt]['is_valid'] = []
+        gene[g_cnt]['transcript_complete'] = []
+        gene[g_cnt]['is_complete'] = []
+        gene[g_cnt]['is_correctly_gff3_referenced'] = ''
+        gene[g_cnt]['splicegraph'] = []
+        g_cnt += 1 
+
+    ## deleting empty gene records from the main array
+    XPFLG=0
+    for XP, ens in enumerate(gene):
+        if ens[0]==0:
+            XPFLG=1
+            break
+    
+    if XPFLG==1:
+        XQC = range(XP, len(gene)+1)
+        gene = np.delete(gene, XQC)
+
+    return gene 
+
+def NonetoemptyList(XS):
+    """
+    Convert a None type to empty list 
+
+    @args XS: None type 
+    @type XS: str 
+    """
+    return [] if XS is None else XS 
+
+def create_missing_feature_type(p_feat, c_feat):
+    """
+    GFF/GTF file defines only child features. This function tries to create 
+    the parent feature from the information provided in the attribute column. 
+
+    example: 
+    chr21   hg19_knownGene  exon    9690071 9690100 0.000000        +       .       gene_id "uc002zkg.1"; transcript_id "uc002zkg.1"; 
+    chr21   hg19_knownGene  exon    9692178 9692207 0.000000        +       .       gene_id "uc021wgt.1"; transcript_id "uc021wgt.1"; 
+    chr21   hg19_knownGene  exon    9711935 9712038 0.000000        +       .       gene_id "uc011abu.2"; transcript_id "uc011abu.2"; 
+
+    This function gets the parsed feature annotations. 
+    
+    @args p_feat: Parent feature map  
+    @type p_feat: collections defaultdict
+    @args c_feat: Child feature map  
+    @type c_feat: collections defaultdict
+    """
+
+    child_n_map = defaultdict(list)
+    for fid, det in c_feat.items():
+        # get the details from grand child  
+        GID = STRD = SCR = None
+        SPOS, EPOS = [], [] 
+        TYP = dict()
+        for gchild in det:
+            GID = gchild.get('gene_id', [''])[0] 
+            SPOS.append(gchild.get('location', [])[0]) 
+            EPOS.append(gchild.get('location', [])[1]) 
+            STRD = gchild.get('strand', '')
+            SCR = gchild.get('score', '')
+            TYP[gchild.get('type', '')] = 1
+        SPOS.sort() 
+        EPOS.sort()
+        
+        # infer transcript type
+        transcript_type = 'transcript'
+        transcript_type = 'mRNA' if TYP.get('CDS', '') or TYP.get('cds', '') else transcript_type
+        
+        # gene id and transcript id are same
+        transcript_id = fid[-1]
+        if GID == transcript_id:
+            transcript_id = 'Transcript:' + str(GID)
+        
+        # level -1 feature type 
+        p_feat[(fid[0], fid[1], GID)] = dict( type = 'gene',
+                                            location = [], ## infer location based on multiple transcripts  
+                                            strand = STRD,
+                                            name = GID )
+        # level -2 feature type 
+        child_n_map[(fid[0], fid[1], GID)].append(
+                                            dict( type = transcript_type,
+                                            location =  [SPOS[0], EPOS[-1]], 
+                                            strand = STRD, 
+                                            score = SCR, 
+                                            ID = transcript_id,
+                                            gene_id = '' ))
+        # reorganizing the grand child
+        for gchild in det:
+            child_n_map[(fid[0], fid[1], transcript_id)].append(
+                                            dict( type = gchild.get('type', ''),
+                                            location =  gchild.get('location'),
+                                            strand = gchild.get('strand'), 
+                                            ID = gchild.get('ID'),
+                                            score = gchild.get('score'),
+                                            gene_id = '' ))
+    return p_feat, child_n_map 
+