unipept: unipept.py comparison

comparison unipept.py @ 4:4953dcd7dd39 draft

planemo upload for repository http://unipept.ugent.be/apidocs commit e91b0fe16bf468b34884508652359b91847d1f95-dirty

author	galaxyp
date	Wed, 23 Jan 2019 09:16:38 -0500
parents	34758ab8aaa4
children	917fd3ebc223

comparison

equal deleted inserted replaced

-:34758ab8aaa4
+:4953dcd7dd39
 import os
 import sys
 import re
 import urllib
 import urllib2
+"""
+pept2taxa	json
+pept2lca	json
+pept2prot
+pept2ec		ecjson	ec
+pept2go			go
+pept2funct	go	ec
+peptinfo	json ecjson ec go
+"""
 try:
 import xml.etree.cElementTree as ET
 except ImportError:
 import xml.etree.ElementTree as ET
 def warn_err(msg,exit_code=1):
 sys.stderr.write(msg)
 if exit_code:
 sys.exit(exit_code)
+go_types = ['biological process', 'molecular function', 'cellular component']
+ec_name_dict = {
+'1' : 'Oxidoreductase',
+'1.1' : 'act on the CH-OH group of donors',
+'1.2' : 'act on the aldehyde or oxo group of donors',
+'1.3' : 'act on the CH-CH group of donors',
+'1.4' : 'act on the CH-NH2 group of donors',
+'1.5' : 'act on CH-NH group of donors',
+'1.6' : 'act on NADH or NADPH',
+'1.7' : 'act on other nitrogenous compounds as donors',
+'1.8' : 'act on a sulfur group of donors',
+'1.9' : 'act on a heme group of donors',
+'1.10' : 'act on diphenols and related substances as donors',
+'1.11' : 'act on peroxide as an acceptor -- peroxidases',
+'1.12' : 'act on hydrogen as a donor',
+'1.13' : 'act on single donors with incorporation of molecular oxygen',
+'1.14' : 'act on paired donors with incorporation of molecular oxygen',
+'1.15' : 'act on superoxide radicals as acceptors',
+'1.16' : 'oxidize metal ions',
+'1.17' : 'act on CH or CH2 groups',
+'1.18' : 'act on iron-sulfur proteins as donors',
+'1.19' : 'act on reduced flavodoxin as donor',
+'1.20' : 'act on phosphorus or arsenic as donors',
+'1.21' : 'act on X-H and Y-H to form an X-Y bond',
+'1.97' : 'other oxidoreductases',
+'2' : 'Transferase',
+'2.1' : 'transfer one-carbon groups, Methylase',
+'2.2' : 'transfer aldehyde or ketone groups',
+'2.3' : 'acyltransferases',
+'2.4' : 'glycosyltransferases',
+'2.5' : 'transfer alkyl or aryl groups, other than methyl groups',
+'2.6' : 'transfer nitrogenous groups',
+'2.7' : 'transfer phosphorus-containing groups',
+'2.8' : 'transfer sulfur-containing groups',
+'2.9' : 'transfer selenium-containing groups',
+'3' : 'Hydrolase',
+'3.1' : 'act on ester bonds',
+'3.2' : 'act on sugars - glycosylases',
+'3.3' : 'act on ether bonds',
+'3.4' : 'act on peptide bonds - Peptidase',
+'3.5' : 'act on carbon-nitrogen bonds, other than peptide bonds',
+'3.6' : 'act on acid anhydrides',
+'3.7' : 'act on carbon-carbon bonds',
+'3.8' : 'act on halide bonds',
+'3.9' : 'act on phosphorus-nitrogen bonds',
+'3.10' : 'act on sulfur-nitrogen bonds',
+'3.11' : 'act on carbon-phosphorus bonds',
+'3.12' : 'act on sulfur-sulfur bonds',
+'3.13' : 'act on carbon-sulfur bonds',
+'4' : 'Lyase',
+'4.1' : 'carbon-carbon lyases',
+'4.2' : 'carbon-oxygen lyases',
+'4.3' : 'carbon-nitrogen lyases',
+'4.4' : 'carbon-sulfur lyases',
+'4.5' : 'carbon-halide lyases',
+'4.6' : 'phosphorus-oxygen lyases',
+'5' : 'Isomerase',
+'5.1' : 'racemases and epimerases',
+'5.2' : 'cis-trans-isomerases',
+'5.3' : 'intramolecular oxidoreductases',
+'5.4' : 'intramolecular transferases -- mutases',
+'5.5' : 'intramolecular lyases',
+'5.99' : 'other isomerases',
+'6' : 'Ligase',
+'6.1' : 'form carbon-oxygen bonds',
+'6.2' : 'form carbon-sulfur bonds',
+'6.3' : 'form carbon-nitrogen bonds',
+'6.4' : 'form carbon-carbon bonds',
+'6.5' : 'form phosphoric ester bonds',
+'6.6' : 'form nitrogen-metal bonds',
+}
 pept2lca_column_order = ['peptide','taxon_rank','taxon_id','taxon_name']
 pept2lca_extra_column_order = ['peptide','superkingdom','kingdom','subkingdom','superphylum','phylum','subphylum','superclass','class','subclass','infraclass','superorder','order','suborder','infraorder','parvorder','superfamily','family','subfamily','tribe','subtribe','genus','subgenus','species_group','species_subgroup','species','subspecies','varietas','forma' ]
 pept2lca_all_column_order = pept2lca_column_order + pept2lca_extra_column_order[1:]
 pept2prot_column_order = ['peptide','uniprot_id','taxon_id']
 pept2prot_extra_column_order = pept2prot_column_order + ['taxon_name','ec_references','go_references','refseq_ids','refseq_protein_ids','insdc_ids','insdc_protein_ids']
+pept2ec_column_order = [['peptide', 'total_protein_count'], ['ec_number', 'protein_count']]
+pept2ec_extra_column_order = [['peptide', 'total_protein_count'], ['ec_number', 'protein_count', 'name']]
+pept2go_column_order = [['peptide', 'total_protein_count'], ['go_term', 'protein_count']]
+pept2go_extra_column_order = [['peptide', 'total_protein_count'], ['go_term', 'protein_count', 'name']]
+pept2funct_column_order = ['peptide', 'total_protein_count', 'ec', 'go']
 def __main__():
 version = '2.0'
 pep_pat = '^([ABCDEFGHIKLMNPQRSTVWXYZ]+)$'
 def best_match(peptide,matches):
 if not matches:
 return None
 elif len(matches) == 1:
 return matches[0].copy()
-else:
+elif 'taxon_rank' in matches[0]:
 # find the most specific match (peptide is always the first column order field)
 for col in reversed(pept2lca_extra_column_order[1:]):
 col_id = col+"_id" if options.extra else col
 for match in matches:
 if 'taxon_rank' in match and match['taxon_rank'] == col:
 return match.copy()
 if col_id in match and match[col_id]:
 return match.copy()
+else:
+return sorted(matches, key=lambda x: len(x['peptide']))[-1].copy()
 return None
+def get_taxon_json(resp):
+found_keys = set()
+for i,pdict in enumerate(resp):
+found_keys |= set(pdict.keys())
+taxa_cols = []
+for col in pept2lca_extra_column_order[-1:0:-1]:
+if col+'_id' in found_keys:
+taxa_cols.append(col)
+id_to_node = dict()
+def get_node(id,name,rank,child,seq):
+if id not in id_to_node:
+data = {'count' : 0, 'self_count' : 0, 'valid_taxon' : 1,  'rank' : rank, 'sequences' : [] }
+node = {'id' : id, 'name' : name, 'children' : [], 'kids': [],'data' : data }
+id_to_node[id] = node
+else:
+node = id_to_node[id]
+node['data']['count'] += 1
+if seq is not None and seq not in node['data']['sequences']:
+node['data']['sequences'].append(seq)
+if child is None:
+node['data']['self_count'] += 1
+elif child['id'] not in node['kids']:
+node['kids'].append(child['id'])
+node['children'].append(child)
+return node
+root = get_node(1,'root','no rank',None,None)
+for i,pdict in enumerate(resp):
+sequence = pdict.get('peptide',pdict.get('tryptic_peptide',None))
+seq = sequence
+child = None
+for col in taxa_cols:
+col_id = col+'_id'
+if col_id in pdict and pdict.get(col_id):
+col_name = col if col in found_keys else col+'_name'
+child = get_node(pdict.get(col_id,None),pdict.get(col_name,''),col,child,seq)
+seq = None
+if child:
+get_node(1,'root','no rank',child,None)
+return root
+def get_ec_json(resp):
+ecMap = dict()
+for pdict in resp:
+if 'ec' in pdict:
+for ec in pdict['ec']:
+ec_number = ec['ec_number']
+if ec_number not in ecMap:
+ecMap[ec_number] = []
+ecMap[ec_number].append(pdict)
+def get_ids(ec):
+ids = []
+i = len(ec)
+while i >= 0:
+ids.append(ec[:i])
+i = ec.rfind('.',0,i - 1)
+return ids
+id_to_node = dict()
+def get_node(id,name,child,seq):
+if id not in id_to_node:
+data = {'count' : 0, 'self_count' : 0, 'sequences' : [] }
+node = {'id' : id, 'name' : name, 'children' : [], 'kids': [],'data' : data }
+id_to_node[id] = node
+else:
+node = id_to_node[id]
+node['data']['count'] += 1
+if seq is not None and seq not in node['data']['sequences']:
+node['data']['sequences'].append(seq)
+if child is None:
+node['data']['self_count'] += 1
+elif child['id'] not in node['kids']:
+node['kids'].append(child['id'])
+node['children'].append(child)
+return node
+root = get_node(0,'-.-.-.-',None,None)
+for i in range(1,7):
+child = get_node(str(i),'%s\n%s' %(str(i), ec_name_dict[str(i)] ),None,None)
+get_node(0,'-.-.-.-',child,None)
+for i,pdict in enumerate(resp):
+sequence = pdict.get('peptide',pdict.get('tryptic_peptide',None))
+seq = sequence
+if 'ec' in pdict:
+for ec in pdict['ec']:
+child = None
+protein_count = ec['protein_count']
+ec_number = ec['ec_number']
+for ec_id in get_ids(ec_number):
+ec_name = str(ec_id)
+## if len(ec_id) == 3:
+##   ec_name = '%s\n%s\n%s' %(str(ec_id), ec_name_dict[str(ec_id[0])],  ec_name_dict[str(ec_id)])
+child = get_node(ec_id,ec_name,child,seq)
+seq = None
+if child:
+get_node(0,'-.-.-.-',child,None)
+return root
+def get_taxon_dict(resp, column_order, extra=False, names=False):
+found_keys = set()
+results = []
+for i,pdict in enumerate(resp):
+results.append(pdict)
+found_keys |= set(pdict.keys())
+# print >> sys.stderr, "%s\n%s" % (pdict.keys(),found_keys)
+column_names = []
+column_keys = []
+for col in column_order:
+if col in found_keys:
+column_names.append(col)
+column_keys.append(col)
+elif names:
+col_id = col+'_id'
+col_name = col+'_name'
+if extra:
+if col_id in found_keys:
+column_names.append(col_id)
+column_keys.append(col_id)
+if names:
+if col_name in found_keys:
+column_names.append(col)
+column_keys.append(col_name)
+else:
+if col+'_name' in found_keys:
+column_names.append(col)
+column_keys.append(col+'_name')
+elif col+'_id' in found_keys:
+column_names.append(col)
+column_keys.append(col+'_id')
+# print >> sys.stderr, "%s\n%s" % (column_names,column_keys)
+taxa = dict() ## peptide : [taxonomy]
+for i,pdict in enumerate(results):
+peptide = pdict['peptide'] if 'peptide' in pdict else None
+if peptide and peptide not in taxa:
+vals = [str(pdict[x]) if x in pdict and pdict[x] else '' for x in column_keys]
+taxa[peptide] = vals
+return (taxa,column_names)
+def get_ec_dict(resp, extra=False):
+ec_cols = ['ec_numbers', 'ec_protein_counts']
+if extra:
+ec_cols.append('ec_names')
+ec_dict = dict()
+for i,pdict in enumerate(resp):
+peptide = pdict['peptide']
+ec_numbers = []
+protein_counts = []
+ec_names = []
+if 'ec' in pdict:
+for ec in pdict['ec']:
+ec_numbers.append(ec['ec_number'])
+protein_counts.append(str(ec['protein_count']))
+if extra:
+ec_names.append(ec['name'] if 'name' in ec else '')
+vals = [','.join(ec_numbers),','.join(protein_counts)]
+if extra:
+vals.append(','.join(ec_names))
+ec_dict[peptide] = vals
+return (ec_dict, ec_cols)
+def get_go_dict(resp, extra=False):
+go_cols = ['go_terms', 'go_protein_counts']
+if extra:
+go_cols.append('go_names')
+go_dict = dict()
+for i,pdict in enumerate(resp):
+peptide = pdict['peptide']
+go_terms = []
+protein_counts = []
+go_names = []
+if 'go' in pdict:
+for go in pdict['go']:
+if 'go_term' in go:
+go_terms.append(go['go_term'])
+protein_counts.append(str(go['protein_count']))
+if extra:
+go_names.append(go['name'] if 'name' in go else '')
+else:
+for go_type in go_types:
+if go_type in go:
+for _go in go[go_type]:
+go_terms.append(_go['go_term'])
+protein_counts.append(str(_go['protein_count']))
+if extra:
+go_names.append(_go['name'] if 'name' in _go else '')
+vals = [','.join(go_terms),','.join(protein_counts)]
+if extra:
+vals.append(','.join(go_names))
+go_dict[peptide] = vals
+return (go_dict, go_cols)
+def write_ec_table(outfile, resp, column_order):
+with open(outfile,'w') as fh:
+for i,pdict in enumerate(resp):
+if 'ec' in pdict:
+tvals = [str(pdict[x]) if x in pdict and pdict[x] else '' for x in column_order[0]]
+for ec in pdict['ec']:
+vals = [str(ec[x]) if x in ec and ec[x] else '' for x in column_order[-1]]
+fh.write('%s\n' % '\t'.join(tvals + vals))
+def write_go_table(outfile, resp, column_order):
+with open(outfile,'w') as fh:
+for i,pdict in enumerate(resp):
+if 'go' in pdict:
+tvals = [str(pdict[x]) if x in pdict and pdict[x] else '' for x in column_order[0]]
+for go in pdict['go']:
+if 'go_term' in go:
+vals = [str(go[x]) if x in go and go[x] else '' for x in column_order[-1]]
+fh.write('%s\n' % '\t'.join(tvals + vals))
+else:
+for go_type in go_types:
+if go_type in go:
+for _go in go[go_type]:
+vals = [str(_go[x]) if x in _go and _go[x] else '' for x in column_order[-1]]
+vals.append(go_type)
+fh.write('%s\n' % '\t'.join(tvals + vals))
 #Parse Command Line
 parser = optparse.OptionParser()
 # unipept API choice
-parser.add_option( '-a', '--api', dest='unipept', default='pept2lca', choices=['pept2lca','pept2taxa','pept2prot'], help='The unipept application: pept2lca, pept2taxa, or pept2prot' )
+parser.add_option( '-a', '--api', dest='unipept', default='pept2lca', choices=['pept2lca','pept2taxa','pept2prot', 'pept2ec', 'pept2go', 'pept2funct', 'peptinfo'],
+help='The unipept application: pept2lca, pept2taxa, pept2prot, pept2ec, pept2go, pept2funct, or peptinfo' )
 # input files
 parser.add_option( '-t', '--tabular', dest='tabular', default=None, help='A tabular file that contains a peptide column' )
 parser.add_option( '-c', '--column', dest='column', type='int', default=0, help='The column (zero-based) in the tabular file that contains peptide sequences' )
 parser.add_option( '-f', '--fasta', dest='fasta', default=None, help='A fasta file containing peptide sequences' )
 parser.add_option( '-m', '--mzid', dest='mzid', default=None, help='A mxIdentML file containing peptide sequences' )
 parser.add_option( '-p', '--pepxml', dest='pepxml', default=None, help='A pepxml file containing peptide sequences' )
 # Unipept Flags
 parser.add_option( '-e', '--equate_il', dest='equate_il', action='store_true', default=False, help='isoleucine (I) and leucine (L) are equated when matching tryptic peptides to UniProt records' )
 parser.add_option( '-x', '--extra', dest='extra', action='store_true', default=False, help='return the complete lineage of the taxonomic lowest common ancestor' )
 parser.add_option( '-n', '--names', dest='names', action='store_true', default=False, help='return the names of all ranks in the lineage of the taxonomic lowest common ancestor' )
+parser.add_option( '-D', '--domains', dest='domains', action='store_true', default=False, help='group response by GO namaspace: biological process, molecular function, cellular component' )
 parser.add_option( '-M', '--max_request', dest='max_request', type='int', default=200, help='The maximum number of entries per unipept request' )
 # output fields
 parser.add_option( '-A', '--allfields', dest='allfields', action='store_true', default=False, help='inlcude fields: taxon_rank,taxon_id,taxon_name csv and tsv outputs' )
 # Warn vs Error Flag
 parser.add_option( '-S', '--strict', dest='strict', action='store_true', default=False, help='Print exit on invalid peptide' )
 # output files
 parser.add_option( '-J', '--json', dest='json', default=None, help='Output file path for json formatted results')
+parser.add_option( '-j', '--ec_json', dest='ec_json', default=None, help='Output file path for json formatted results')
+parser.add_option( '-E', '--ec_tsv', dest='ec_tsv', default=None, help='Output file path for EC TAB-separated-values (.tsv) formatted results')
+parser.add_option( '-G', '--go_tsv', dest='go_tsv', default=None, help='Output file path for GO TAB-separated-values (.tsv) formatted results')
+parser.add_option( '-L', '--lineage_tsv', dest='lineage_tsv', default=None, help='Output file path for Lineage TAB-separated-values (.tsv) formatted results')
 parser.add_option( '-T', '--tsv', dest='tsv', default=None, help='Output file path for TAB-separated-values (.tsv) formatted results')
 parser.add_option( '-C', '--csv', dest='csv', default=None, help='Output file path for Comma-separated-values (.csv) formatted results')
 parser.add_option( '-U', '--unmatched', dest='unmatched', default=None, help='Output file path for peptide with no matches' )
+parser.add_option( '-u', '--url', dest='url', default='http://api.unipept.ugent.be/api/v1/', help='unipept url http://api.unipept.ugent.be/api/v1/' )
 # debug
+parser.add_option( '-g', '--get', dest='get', action='store_true', default=False, help='Use GET instead of POST' )
 parser.add_option( '-d', '--debug', dest='debug', action='store_true', default=False, help='Turning on debugging' )
 parser.add_option( '-v', '--version', dest='version', action='store_true', default=False, help='pring version and exit' )
 (options, args) = parser.parse_args()
 if options.version:
 print >> sys.stdout,"%s" % version
 if options.names or options.json:
 post_data.append(("extra","true"))
 post_data.append(("names","true"))
 elif options.extra or options.json:
 post_data.append(("extra","true"))
+if options.domains:
+post_data.append(("domains","true"))
 post_data += [('input[]', x) for x in trypticPeptides[idx[i]:idx[i+1]]]
+if options.debug: print >> sys.stdout, "post_data: %s\n" % (str(post_data))
 headers = {'Content-Type': 'application/x-www-form-urlencoded',  'Accept': 'application/json'}
-url = 'http://api.unipept.ugent.be/api/v1/%s' % options.unipept
+## headers = {'Accept': 'application/json'}
-req = urllib2.Request( url, headers = headers, data = urllib.urlencode(post_data) )
+url = '%s/%s' % (options.url.rstrip('/'),options.unipept)
-unipept_resp += json.loads( urllib2.urlopen( req ).read() )
+if options.get:
+params = '&'.join(['%s=%s' % (i[0],i[1]) for i in post_data])
+url = '%s.json?%s' % (url,params)
+req = urllib2.Request( url )
+else:
+url = '%s.json' % (url)
+req = urllib2.Request( url, headers = headers, data = urllib.urlencode(post_data) )
+if options.debug: print >> sys.stdout, "url: %s\n" % (str(url))
+try:
+resp = urllib2.urlopen( req )
+if options.debug: print >> sys.stdout,"%s %s\n" % (url,str(resp.getcode()))
+if resp.getcode() == 200:
+unipept_resp += json.loads( urllib2.urlopen( req ).read() )
+except Exception, e:
+warn_err('HTTP Error %s\n' % (str(e)),exit_code=None)
 unmatched_peptides = []
 peptideMatches = []
 if options.debug: print >> sys.stdout,"unipept response: %s\n" % str(unipept_resp)
-if options.unipept == 'pept2prot' or options.unipept == 'pept2taxa':
+if options.unipept in ['pept2prot', 'pept2taxa']:
 dupkey = 'uniprot_id' if options.unipept == 'pept2prot' else 'taxon_id' ## should only keep one of these per input peptide
 ## multiple entries per trypticPeptide for pep2prot or pep2taxa
 mapping = {}
 for match in unipept_resp:
 mapping.setdefault(match['peptide'],[]).append(match)
 else:
 for key,match in keyToMatch.iteritems():
 match['tryptic_peptide'] = match['peptide']
 match['peptide'] = peptide
 peptideMatches.append(match)
-else:
+elif options.unipept in ['pept2lca', 'peptinfo']:
 ## should be one response per trypticPeptide for pep2lca
 respMap = {v['peptide']:v for v in unipept_resp}
 ## map resp back to peptides
 for peptide in peptides:
 matches = list()
 for part in pepToParts[peptide]:
 if part in respMap:
+matches.append(respMap[part])
+match = best_match(peptide,matches)
+if not match:
+unmatched_peptides.append(peptide)
+longest_tryptic_peptide = sorted(pepToParts[peptide], key=lambda x: len(x))[-1]
+match = {'peptide' : longest_tryptic_peptide}
+match['tryptic_peptide'] = match['peptide']
+match['peptide'] = peptide
+peptideMatches.append(match)
+else:
+respMap = {v['peptide']:v for v in unipept_resp}
+## map resp back to peptides
+for peptide in peptides:
+matches = list()
+for part in pepToParts[peptide]:
+if part in respMap and 'total_protein_count' in respMap[part]:
 matches.append(respMap[part])
 match = best_match(peptide,matches)
 if not match:
 unmatched_peptides.append(peptide)
 longest_tryptic_peptide = sorted(pepToParts[peptide], key=lambda x: len(x))[-1]
 with open(options.unmatched,'w') as outputFile:
 for peptide in peptides:
 if peptide in unmatched_peptides:
 outputFile.write("%s\n" % peptide)
 if options.json:
-if options.unipept == 'pept2prot':
+if options.unipept in ['pept2lca', 'pept2taxa', 'peptinfo']:
+root = get_taxon_json(resp)
+with open(options.json,'w') as outputFile:
+outputFile.write(json.dumps(root))
+elif options.unipept in ['pept2prot', 'pept2ec', 'pept2go', 'pept2funct']:
 with open(options.json,'w') as outputFile:
 outputFile.write(str(resp))
-else:
+if options.ec_json:
-found_keys = set()
+if options.unipept in ['pept2ec', 'pept2funct', 'peptinfo']:
+root = get_ec_json(resp)
+with open(options.ec_json,'w') as outputFile:
+outputFile.write(json.dumps(root))
+if options.tsv or options.csv:
+rows = []
+column_names = None
+if options.unipept in ['pept2ec', 'pept2go', 'pept2funct', 'peptinfo']:
+taxa = None
+ec_dict = None
+go_dict = None
+if options.unipept in ['peptinfo']:
+(taxa,taxon_cols) = get_taxon_dict(resp, column_order, extra=options.extra, names=options.names)
+if options.unipept in ['pept2ec', 'pept2funct', 'peptinfo']:
+(ec_dict,ec_cols) = get_ec_dict(resp, extra=options.extra)
+if options.unipept in ['pept2go', 'pept2funct', 'peptinfo']:
+(go_dict,go_cols) = get_go_dict(resp, extra=options.extra)
 for i,pdict in enumerate(resp):
-found_keys |= set(pdict.keys())
+peptide = pdict['peptide']
-taxa_cols = []
+total_protein_count = str(pdict['total_protein_count']) if 'total_protein_count' in pdict else '0'
-for col in pept2lca_extra_column_order[-1:0:-1]:
+column_names = ['peptide', 'total_protein_count']
-if col+'_id' in found_keys:
+vals = [peptide,total_protein_count]
-taxa_cols.append(col)
+if ec_dict:
-id_to_node = dict()
+vals += ec_dict[peptide]
-def get_node(id,name,rank,child,seq):
+column_names += ec_cols
-if id not in id_to_node:
+if go_dict:
-data = {'count' : 0, 'self_count' : 0, 'valid_taxon' : 1,  'rank' : rank, 'sequences' : [] }
+vals += go_dict[peptide]
-node = {'id' : id, 'name' : name, 'children' : [], 'kids': [],'data' : data }
+column_names += go_cols
-id_to_node[id] = node
+if taxa:
-else:
+vals += taxa[peptide][1:]
-node = id_to_node[id]
+column_names += taxon_cols[1:]
-node['data']['count'] += 1
+rows.append(vals)
-if seq is not None and seq not in node['data']['sequences']:
+elif options.unipept in ['pept2lca', 'pept2taxa', 'pept2prot']:
-node['data']['sequences'].append(seq)
+(taxa,taxon_cols) = get_taxon_dict(resp, column_order, extra=options.extra, names=options.names)
-if child is None:
+column_names = taxon_cols
-node['data']['self_count'] += 1
+rows = taxa.values()
-elif child['id'] not in node['kids']:
+for peptide,vals in taxa.iteritems():
-node['kids'].append(child['id'])
+rows.append(vals)
-node['children'].append(child)
-return node
-root = get_node(1,'root','no rank',None,None)
-for i,pdict in enumerate(resp):
-sequence = pdict.get('peptide',pdict.get('tryptic_peptide',None))
-seq = sequence
-child = None
-for col in taxa_cols:
-col_id = col+'_id'
-if col_id in pdict and pdict.get(col_id):
-col_name = col if col in found_keys else col+'_name'
-child = get_node(pdict.get(col_id,None),pdict.get(col_name,''),col,child,seq)
-seq = None
-if child:
-get_node(1,'root','no rank',child,None)
-with open(options.json,'w') as outputFile:
-outputFile.write(json.dumps(root))
-if options.tsv or options.csv:
-# 'pept2lca','pept2taxa','pept2prot'
-found_keys = set()
-results = []
-for i,pdict in enumerate(resp):
-results.append(pdict)
-found_keys |= set(pdict.keys())
-# print >> sys.stderr, "%s\n%s" % (pdict.keys(),found_keys)
-column_names = []
-column_keys = []
-for col in column_order:
-if col in found_keys:
-column_names.append(col)
-column_keys.append(col)
-elif options.extra or options.names:
-col_id = col+'_id'
-col_name = col+'_name'
-if options.extra:
-if col_id in found_keys:
-column_names.append(col_id)
-column_keys.append(col_id)
-if options.names:
-if col_name in found_keys:
-column_names.append(col)
-column_keys.append(col_name)
-else:
-if col+'_name' in found_keys:
-column_names.append(col)
-column_keys.append(col+'_name')
-elif col+'_id' in found_keys:
-column_names.append(col)
-column_keys.append(col+'_id')
-# print >> sys.stderr, "%s\n%s" % (column_names,column_keys)
-taxa = []
-for i,pdict in enumerate(results):
-vals = [str(pdict[x]) if x in pdict and pdict[x] else '' for x in column_keys]
-if vals not in taxa:
-taxa.append(vals)
 if options.tsv:
 with open(options.tsv,'w') as outputFile:
-outputFile.write("#%s\n"% '\t'.join(column_names))
+if column_names:
-for vals in taxa:
+outputFile.write("#%s\n"% '\t'.join(column_names))
+for vals in rows:
 outputFile.write("%s\n"% '\t'.join(vals))
 if options.csv:
 with open(options.csv,'w') as outputFile:
-outputFile.write("%s\n"% ','.join(column_names))
+if column_names:
-for vals in taxa:
+outputFile.write("%s\n"% ','.join(column_names))
+for vals in rows:
 outputFile.write("%s\n"% ','.join(['"%s"' % (v if v else '') for v in vals]))
+if options.ec_tsv and options.unipept in ['pept2ec', 'pept2funct', 'peptinfo']:
+column_order = pept2ec_extra_column_order if options.extra else pept2ec_column_order
+write_ec_table(options.ec_tsv, resp, column_order)
+if options.go_tsv and options.unipept in ['pept2go', 'pept2funct', 'peptinfo']:
+column_order = pept2go_extra_column_order if options.extra else pept2go_column_order
+write_go_table(options.go_tsv, resp, column_order)
 if __name__ == "__main__" : __main__()

Mercurial > repos > galaxyp > unipept

comparison unipept.py @ 4:4953dcd7dd39 draft