Mercurial > repos > galaxyp > unipept
changeset 5:917fd3ebc223 draft
"planemo upload for repository http://unipept.ugent.be/apidocs commit dd464f03c32f657fc555081117da18ba4c091af6-dirty"
| author | galaxyp |
|---|---|
| date | Thu, 30 Apr 2020 07:39:28 -0400 |
| parents | 4953dcd7dd39 |
| children | 9aaa46d45472 |
| files | unipept.py unipept.xml |
| diffstat | 2 files changed, 729 insertions(+), 616 deletions(-) [+] |
line wrap: on
line diff
--- a/unipept.py Wed Jan 23 09:16:38 2019 -0500 +++ b/unipept.py Thu Apr 30 07:39:28 2020 -0400 @@ -1,663 +1,732 @@ #!/usr/bin/env python """ # -#------------------------------------------------------------------------------ -# University of Minnesota -# Copyright 2015, Regents of the University of Minnesota -#------------------------------------------------------------------------------ # Author: # # James E Johnson # #------------------------------------------------------------------------------ """ - import json -import logging import optparse -from optparse import OptionParser -import os import sys import re -import urllib -import urllib2 +import urllib.error +import urllib.parse +import urllib.request -""" -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): + +def warn_err(msg, exit_code=1): sys.stderr.write(msg) if exit_code: - sys.exit(exit_code) + sys.exit(exit_code) + go_types = ['biological process', 'molecular function', 'cellular component'] +ipr_types = ['Domain', 'Family', 'Homologous_superfamily', 'Repeat', 'Conserved_site', 'Active_site', 'Binding_site', 'PTM'] 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', + '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_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'] +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'] +pept2interpro_column_order = [['peptide', 'total_protein_count'], ['code', 'protein_count']] +pept2interpro_extra_column_order = [['peptide', 'total_protein_count'], ['code', 'protein_count', 'type', 'name']] +pept2funct_column_order = ['peptide', 'total_protein_count', 'ec', 'go', 'ipr'] + def __main__(): - version = '2.0' - pep_pat = '^([ABCDEFGHIKLMNPQRSTVWXYZ]+)$' + version = '4.3' + pep_pat = '^([ABCDEFGHIKLMNPQRSTVWXYZ]+)$' - def read_tabular(filepath,col): - peptides = [] - with open(filepath) as fp: - for i,line in enumerate(fp): - if line.strip() == '' or line.startswith('#'): - continue - fields = line.rstrip('\n').split('\t') - peptide = fields[col] - if not re.match(pep_pat,peptide): - warn_err('"%s" is not a peptide (line %d column %d of tabular file: %s)\n' % (peptide,i,col,filepath),exit_code=invalid_ec) - peptides.append(peptide) - return peptides + def read_tabular(filepath, col): + peptides = [] + with open(filepath) as fp: + for i, line in enumerate(fp): + if line.strip() == '' or line.startswith('#'): + continue + fields = line.rstrip('\n').split('\t') + peptide = fields[col] + if not re.match(pep_pat, peptide): + warn_err('"%s" is not a peptide (line %d column %d of tabular file: %s)\n' % (peptide, i, col, filepath), exit_code=invalid_ec) + peptides.append(peptide) + return peptides - def get_fasta_entries(fp): - name, seq = None, [] - for line in fp: - line = line.rstrip() - if line.startswith(">"): - if name: yield (name, ''.join(seq)) - name, seq = line, [] - else: - seq.append(line) - if name: yield (name, ''.join(seq)) + def get_fasta_entries(fp): + name, seq = None, [] + for line in fp: + line = line.rstrip() + if line.startswith(">"): + if name: + yield (name, ''.join(seq)) + name, seq = line, [] + else: + seq.append(line) + if name: + yield (name, ''.join(seq)) - def read_fasta(filepath): - peptides = [] - with open(filepath) as fp: - for id, peptide in get_fasta_entries(fp): - if not re.match(pep_pat,peptide): - warn_err('"%s" is not a peptide (id %s of fasta file: %s)\n' % (peptide,id,filepath),exit_code=invalid_ec) - peptides.append(peptide) - return peptides + def read_fasta(filepath): + peptides = [] + with open(filepath) as fp: + for id, peptide in get_fasta_entries(fp): + if not re.match(pep_pat, peptide): + warn_err('"%s" is not a peptide (id %s of fasta file: %s)\n' % (peptide, id, filepath), exit_code=invalid_ec) + peptides.append(peptide) + return peptides - def read_mzid(fp): - peptides = [] - for event, elem in ET.iterparse(fp): - if event == 'end': - if re.search('PeptideSequence',elem.tag): - peptides.append(elem.text) - return peptides + def read_mzid(fp): + peptides = [] + for event, elem in ET.iterparse(fp): + if event == 'end': + if re.search('PeptideSequence', elem.tag): + peptides.append(elem.text) + return peptides - def read_pepxml(fp): - peptides = [] - for event, elem in ET.iterparse(fp): - if event == 'end': - if re.search('search_hit',elem.tag): - peptides.append(elem.get('peptide')) - return peptides + def read_pepxml(fp): + peptides = [] + for event, elem in ET.iterparse(fp): + if event == 'end': + if re.search('search_hit', elem.tag): + peptides.append(elem.get('peptide')) + return peptides - def best_match(peptide,matches): - if not matches: - return None - elif len(matches) == 1: - return matches[0].copy() - 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 best_match(peptide, matches): + if not matches: + return None + elif len(matches) == 1: + return matches[0].copy() + 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_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_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 is not None: + 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_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_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 + ec_number = ec['ec_number'] + for ec_id in get_ids(ec_number): + ec_name = 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' + 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: - 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) + 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_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 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'])) + def get_ipr_dict(resp, extra=False): + ipr_cols = ['ipr_codes', 'ipr_protein_counts'] + if extra: + ipr_cols.append('ipr_types') + ipr_cols.append('ipr_names') + ipr_dict = dict() + for i, pdict in enumerate(resp): + peptide = pdict['peptide'] + ipr_codes = [] + protein_counts = [] + ipr_names = [] + ipr_types = [] + if 'ipr' in pdict: + for ipr in pdict['ipr']: + if 'code' in ipr: + ipr_codes.append(ipr['code']) + protein_counts.append(str(ipr['protein_count'])) + if extra: + ipr_types.append(ipr['type'] if 'type' in ipr else '') + ipr_names.append(ipr['name'] if 'name' in ipr else '') + else: + for ipr_type in ipr_types: + if ipr_type in ipr: + for _ipr in ipr[ipr_type]: + ipr_codes.append(_ipr['code']) + protein_counts.append(str(_ipr['protein_count'])) + if extra: + ipr_types.append(ipr_type) + ipr_names.append(_ipr['name'] if 'name' in _ipr else '') + vals = [','.join(ipr_codes), ','.join(protein_counts)] 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) + vals.append(','.join(ipr_types)) + vals.append(','.join(ipr_names)) + ipr_dict[peptide] = vals + return (ipr_dict, ipr_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_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)) + 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)) + + def write_ipr_table(outfile, resp, column_order): + with open(outfile, 'w') as fh: + for i, pdict in enumerate(resp): + if 'ipr' in pdict: + tvals = [str(pdict[x]) if x in pdict and pdict[x] else '' for x in column_order[0]] + for ipr in pdict['ipr']: + if 'code' in ipr: + vals = [str(ipr[x]) if x in ipr and ipr[x] else '' for x in column_order[-1]] + fh.write('%s\n' % '\t'.join(tvals + vals)) + else: + for ipr_type in ipr_types: + if ipr_type in ipr: + for _ipr in ipr[ipr_type]: + vals = [str(_ipr[x]) if x in _ipr and _ipr[x] else '' for x in column_order[-1]] + vals.append(ipr_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', '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 - sys.exit(0) - invalid_ec = 2 if options.strict else None - peptides = [] - ## Get peptide sequences - if options.mzid: - peptides += read_mzid(options.mzid) - if options.pepxml: - peptides += read_pepxml(options.pepxml) - if options.tabular: - peptides += read_tabular(options.tabular,options.column) - if options.fasta: - peptides += read_fasta(options.fasta) - if args and len(args) > 0: - for i,peptide in enumerate(args): - if not re.match(pep_pat,peptide): - warn_err('"%s" is not a peptide (arg %d)\n' % (peptide,i),exit_code=invalid_ec) - peptides.append(peptide) - if len(peptides) < 1: - warn_err("No peptides input!",exit_code=1) - column_order = pept2lca_column_order - if options.unipept == 'pept2prot': - column_order = pept2prot_extra_column_order if options.extra else pept2prot_column_order - else: - if options.extra or options.names: - column_order = pept2lca_all_column_order if options.allfields else pept2lca_extra_column_order - else: - column_order = pept2lca_column_order - ## map to tryptic peptides - pepToParts = {p: re.split("\n", re.sub(r'(?<=[RK])(?=[^P])','\n', p)) for p in peptides} - partToPeps = {} - for peptide, parts in pepToParts.iteritems(): - if options.debug: print >> sys.stdout, "peptide: %s\ttryptic: %s\n" % (peptide, parts) - for part in parts: - if len(part) > 50: - warn_err("peptide: %s tryptic fragment len %d > 50 for %s\n" % (peptide,len(part),part),exit_code=None) - if 5 <= len(part) <= 50: - partToPeps.setdefault(part,[]).append(peptide) - trypticPeptides = partToPeps.keys() - ## unipept - unipept_resp = [] - idx = range(0,len(trypticPeptides),options.max_request) - idx.append(len(trypticPeptides)) - for i in range(len(idx)-1): - post_data = [] - if options.equate_il: - post_data.append(("equate_il","true")) - 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'} - ## headers = {'Accept': 'application/json'} - url = '%s/%s' % (options.url.rstrip('/'),options.unipept) - 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 ) + # Parse Command Line + parser = optparse.OptionParser() + # unipept API choice + parser.add_option('-a', '--api', dest='unipept', default='pept2lca', choices=['pept2lca', 'pept2taxa', 'pept2prot', 'pept2ec', 'pept2go', 'pept2interpro', '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('-I', '--ipr_tsv', dest='ipr_tsv', default=None, help='Output file path for InterPro 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='print version and exit') + (options, args) = parser.parse_args() + if options.version: + print('%s' % version) + sys.exit(0) + invalid_ec = 2 if options.strict else None + peptides = [] + # Get peptide sequences + if options.mzid: + peptides += read_mzid(options.mzid) + if options.pepxml: + peptides += read_pepxml(options.pepxml) + if options.tabular: + peptides += read_tabular(options.tabular, options.column) + if options.fasta: + peptides += read_fasta(options.fasta) + if args and len(args) > 0: + for i, peptide in enumerate(args): + if not re.match(pep_pat, peptide): + warn_err('"%s" is not a peptide (arg %d)\n' % (peptide, i), exit_code=invalid_ec) + peptides.append(peptide) + if len(peptides) < 1: + warn_err("No peptides input!", exit_code=1) + column_order = pept2lca_column_order + if options.unipept == 'pept2prot': + column_order = pept2prot_extra_column_order if options.extra else pept2prot_column_order 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 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) - for peptide in peptides: - # Get the intersection of matches to the tryptic parts - keyToMatch = None - for part in pepToParts[peptide]: - if part in mapping: - temp = {match[dupkey] : match for match in mapping[part]} - if keyToMatch: - dkeys = set(keyToMatch.keys()) - set(temp.keys()) - for k in dkeys: - del keyToMatch[k] - else: - keyToMatch = temp - ## keyToMatch = keyToMatch.fromkeys([x for x in keyToMatch if x in temp]) if keyToMatch else temp - if not keyToMatch: - unmatched_peptides.append(peptide) - else: - for key,match in keyToMatch.iteritems(): - match['tryptic_peptide'] = match['peptide'] - match['peptide'] = peptide - peptideMatches.append(match) - 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] - match = {'peptide' : longest_tryptic_peptide} - match['tryptic_peptide'] = match['peptide'] - match['peptide'] = peptide - peptideMatches.append(match) - resp = peptideMatches - if options.debug: print >> sys.stdout,"\nmapped response: %s\n" % str(resp) - ## output results - if not (options.unmatched or options.json or options.tsv or options.csv): - print >> sys.stdout, str(resp) - if options.unmatched: - 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 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)) - if options.ec_json: - 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): - peptide = pdict['peptide'] - total_protein_count = str(pdict['total_protein_count']) if 'total_protein_count' in pdict else '0' - column_names = ['peptide', 'total_protein_count'] - vals = [peptide,total_protein_count] - if ec_dict: - vals += ec_dict[peptide] - column_names += ec_cols - if go_dict: - vals += go_dict[peptide] - column_names += go_cols - if taxa: - vals += taxa[peptide][1:] - column_names += taxon_cols[1:] - rows.append(vals) - elif options.unipept in ['pept2lca', 'pept2taxa', 'pept2prot']: - (taxa,taxon_cols) = get_taxon_dict(resp, column_order, extra=options.extra, names=options.names) - column_names = taxon_cols - rows = taxa.values() - for peptide,vals in taxa.iteritems(): - rows.append(vals) - if options.tsv: - with open(options.tsv,'w') as outputFile: - if column_names: - 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: - if column_names: - 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 options.extra or options.names: + column_order = pept2lca_all_column_order if options.allfields else pept2lca_extra_column_order + else: + column_order = pept2lca_column_order + # map to tryptic peptides + pepToParts = {p: re.split('\n', re.sub(r'(?<=[RK])(?=[^P])', '\n', p)) for p in peptides} + partToPeps = {} + for peptide, parts in pepToParts.items(): + if options.debug: + print("peptide: %s\ttryptic: %s\n" % (peptide, parts), file=sys.stderr) + for part in parts: + if len(part) > 50: + warn_err("peptide: %s tryptic fragment len %d > 50 for %s\n" % (peptide, len(part), part), exit_code=None) + if 5 <= len(part) <= 50: + partToPeps.setdefault(part, []).append(peptide) + trypticPeptides = list(partToPeps.keys()) + # unipept + unipept_resp = [] + idx = list(range(0, len(trypticPeptides), options.max_request)) + idx.append(len(trypticPeptides)) + for i in range(len(idx) - 1): + post_data = [] + if options.equate_il: + post_data.append(('equate_il', 'true')) + 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('post_data: %s\n' % (str(post_data)), file=sys.stderr) + headers = {'Content-Type': 'application/x-www-form-urlencoded', 'Accept': 'application/json'} + url = '%s/%s' % (options.url.rstrip('/'), options.unipept) + if options.get: + params = '&'.join(["%s=%s" % (i[0], i[1]) for i in post_data]) + url = '%s.json?%s' % (url, params) + req = urllib.request.Request(url) + else: + url = '%s.json' % (url) + data = urllib.parse.urlencode(post_data).encode() + params = '&'.join(["%s=%s" % (i[0], i[1]) for i in post_data]) + if options.debug: + print('data:\n%s\n' % (data), file=sys.stderr) + req = urllib.request.Request(url, headers=headers, data=urllib.parse.urlencode(post_data).encode(), method='POST') + if options.debug: + print("url: %s\n" % (str(url)), file=sys.stderr) + try: + resp = urllib.request.urlopen(req) + rdata = resp.read() + if options.debug: + print("%s %s\n" % (url, str(resp.getcode())), file=sys.stderr) + if resp.getcode() == 200: + if options.debug: + print("rdata: \n%s\n\n" % (rdata), file=sys.stderr) + unipept_resp += json.loads(rdata) + # unipept_resp += json.loads(urllib.request.urlopen(req).read()) + except Exception as e: + warn_err('HTTP Error %s\n' % (str(e)), exit_code=None) + unmatched_peptides = [] + peptideMatches = [] + if options.debug: + print("unipept response: %s\n" % str(unipept_resp), file=sys.stderr) + 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) + for peptide in peptides: + # Get the intersection of matches to the tryptic parts + keyToMatch = None + for part in pepToParts[peptide]: + if part in mapping: + temp = {match[dupkey]: match for match in mapping[part]} + if keyToMatch: + dkeys = set(keyToMatch.keys()) - set(temp.keys()) + for k in dkeys: + del keyToMatch[k] + else: + keyToMatch = temp + # keyToMatch = keyToMatch.fromkeys([x for x in keyToMatch if x in temp]) if keyToMatch else temp + if not keyToMatch: + unmatched_peptides.append(peptide) + else: + for key, match in keyToMatch.items(): + match['tryptic_peptide'] = match['peptide'] + match['peptide'] = peptide + peptideMatches.append(match) + 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] + match = {'peptide': longest_tryptic_peptide} + match['tryptic_peptide'] = match['peptide'] + match['peptide'] = peptide + peptideMatches.append(match) + resp = peptideMatches + if options.debug: + print("\nmapped response: %s\n" % str(resp), file=sys.stderr) + # output results + if not (options.unmatched or options.json or options.tsv or options.csv): + print(str(resp)) + if options.unmatched: + 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 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', 'pept2interpro', 'pept2funct']: + with open(options.json, 'w') as outputFile: + outputFile.write(str(resp)) + if options.ec_json: + 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', 'pept2interpro', 'pept2funct', 'peptinfo']: + taxa = None + ec_dict = None + go_dict = None + ipr_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) + if options.unipept in ['pept2interpro', 'pept2funct', 'peptinfo']: + (ipr_dict, ipr_cols) = get_ipr_dict(resp, extra=options.extra) + for i, pdict in enumerate(resp): + peptide = pdict['peptide'] + total_protein_count = str(pdict['total_protein_count']) if 'total_protein_count' in pdict else '0' + column_names = ['peptide', 'total_protein_count'] + vals = [peptide, total_protein_count] + if ec_dict: + vals += ec_dict[peptide] + column_names += ec_cols + if go_dict: + vals += go_dict[peptide] + column_names += go_cols + if ipr_dict: + vals += ipr_dict[peptide] + column_names += ipr_cols + if taxa: + vals += taxa[peptide][1:] + column_names += taxon_cols[1:] + rows.append(vals) + elif options.unipept in ['pept2lca', 'pept2taxa', 'pept2prot']: + (taxa, taxon_cols) = get_taxon_dict(resp, column_order, extra=options.extra, names=options.names) + column_names = taxon_cols + rows = list(taxa.values()) + for peptide, vals in taxa.items(): + rows.append(vals) + if options.tsv: + with open(options.tsv, 'w') as outputFile: + if column_names: + 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: + if column_names: + 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 options.ipr_tsv and options.unipept in ['pept2interpro', 'pept2funct', 'peptinfo']: + column_order = pept2interpro_extra_column_order if options.extra else pept2interpro_column_order + write_ipr_table(options.ipr_tsv, resp, column_order) -if __name__ == "__main__" : __main__() + +if __name__ == "__main__": + __main__()
--- a/unipept.xml Wed Jan 23 09:16:38 2019 -0500 +++ b/unipept.xml Thu Apr 30 07:39:28 2020 -0400 @@ -1,4 +1,4 @@ -<tool id="unipept" name="Unipept" version="4.0.0"> +<tool id="unipept" name="Unipept" version="4.3.0"> <description>retrieve taxonomy for peptides</description> <macros> <xml name="equate_il"> @@ -40,7 +40,7 @@ </xml> </macros> <requirements> - <requirement type="package" version="2.7">python</requirement> + <requirement type="package" version="3">python</requirement> </requirements> <stdio> <exit_code range="1:" /> @@ -94,6 +94,9 @@ #if 'go_tsv' in str($selected_outputs).split(',') and str($unipept.api) in ['pept2go', 'pept2funct', 'peptinfo']: --go_tsv $output_go_tsv #end if + #if 'ipr_tsv' in str($selected_outputs).split(',') and str($unipept.api) in ['pept2interpro', 'pept2funct', 'peptinfo']: + --ipr_tsv $output_ipr_tsv + #end if #if 'unmatched' in str($selected_outputs).split(','): --unmatched $output_unmatched #end if @@ -106,6 +109,7 @@ <option value="pept2prot">pept2prot: UniProt entries containing a given tryptic peptide</option> <option value="pept2ec">pept2ec: Tryptic peptides and associated EC terms</option> <option value="pept2go">pept2go: Tryptic peptides and associated GO terms</option> + <option value="pept2interpro">pept2interpro: Tryptic peptides and associated InterPro entries</option> <option value="pept2funct">pept2funct: Tryptic peptides and associated EC and GO terms</option> <option value="peptinfo">peptinfo: Tryptic peptides and associated EC and GO terms and lowest common ancestor taxonomy</option> </param> @@ -146,6 +150,14 @@ </expand> <expand macro="domains" /> </when> + <when value="pept2interpro"> + <expand macro="equate_il" /> + <expand macro="extra_true"> + <help>Return the name of the GO-term. + </help> + </expand> + <expand macro="domains" /> + </when> <when value="pept2funct"> <expand macro="equate_il" /> <expand macro="extra_true"> @@ -194,6 +206,7 @@ <option value="csv">Comma Separated Values (.csv) with one line per peptide</option> <option value="json">JSON Taxomony Tree (for pept2lca, pep2taxa, and peptinfo)</option> <option value="go_tsv">Peptide GO terms in normalized tabular (for pept2go, pept2funct, and peptinfo)</option> + <option value="ipr_tsv">Peptide InterPro entries in normalized tabular (for pept2interpro, pept2funct, and peptinfo)</option> <option value="ec_tsv">Peptide EC terms in normalized tabular (for pept2ec, pept2funct, and peptinfo)</option> <option value="ec_json">JSON EC Coverage Tree (for pept2ec, pep2funct, and peptinfo)</option> <option value="unmatched">Unmatched peptides</option> @@ -244,6 +257,13 @@ <action name="column_names" type="metadata" default="Peptide,Total Protein Count,GO Term,Protein Count,GO Name" /> </actions> </data> + <data name="output_ipr_tsv" format="tabular" label="${tool.name} ${unipept.api} on ${on_string} InterPro tsv"> + <filter>'ipr_tsv' in selected_outputs and unipept['api'] in ('pept2interpro', 'pept2funct', 'peptinfo')</filter> + <actions> + <action name="column_names" type="metadata" default="Peptide,Total Protein Count,InterPro Code,Protein Count,InterPro Type,InterPro Name" /> + </actions> + </data> + <data name="output_unmatched" format="tabular" label="${tool.name} ${unipept.api} on ${on_string} unmatched"> <filter>'unmatched' in selected_outputs</filter> <actions> @@ -279,6 +299,8 @@ <param name="extra" value="True"/> <param name="names" value="True"/> <param name="selected_outputs" value="json,tsv"/> +<!-- +--> <output name="output_json"> <assert_contents> <has_text text="VMDVNDHKPEFYNCSLPACTFTPEEAQVNFTGYVDEHASPHIPIDDLTMVVYDPDKGSNGTFLLSLGGPDAEAFSVSPERAAGSASVQVLVRVSALVDYERQTAMAV" /> @@ -303,7 +325,6 @@ <assert_contents> <has_text text="sapiens" /> <has_text text="paniscus" /> - <has_text text="Gorilla" /> </assert_contents> </output> </test> @@ -314,11 +335,12 @@ <param name="column" value="2"/> <param name="extra" value="True"/> <param name="names" value="True"/> - <param name="selected_outputs" value="tsv,ec_tsv,go_tsv,unmatched"/> + <param name="selected_outputs" value="tsv,ec_tsv,go_tsv,ipr_tsv,unmatched"/> <output name="output_tsv"> <assert_contents> <has_text text="GO:0004802" /> <has_text text="2.2.1.1" /> + <has_text text="IPR005475" /> </assert_contents> </output> <output name="output_ec_tsv"> @@ -331,6 +353,11 @@ <has_text text="GO:0004802" /> </assert_contents> </output> + <output name="output_ipr_tsv"> + <assert_contents> + <has_text text="IPR005475" /> + </assert_contents> + </output> </test> </tests> <help><![CDATA[ @@ -485,6 +512,19 @@ name: Optional, name of the GO-term. Included when the extra parameter is set to true. + **pept2interpro** - http://unipept.ugent.be/apidocs/pept2interpro + + Returns the functional InterPro entries associated with a given tryptic peptide. This is the same information as provided when performing a search with the Tryptic Peptide Analysis in the web interface. + + By default, each object contains the following information fields extracted from the UniProt record and NCBI taxonomy:: + + peptide: the peptide that matched this record + total_protein_count: Total amount of proteins matched with the given peptide + code: InterPro entry code associated with the current tryptic peptide + protein_count: amount of proteins matched with the given tryptic peptide that are labeled with the current InterPro code. + type: Optional, type of the InterPro entry. Included when the extra parameter is set to true. + name: Optional, name of the InterPro entry. Included when the extra parameter is set to true. + **pept2funct** - http://unipept.ugent.be/apidocs/pept2funct Returns the functional EC-numbers and GO-terms associated with a given tryptic peptide. This is the same information as provided when performing a search with the Tryptic Peptide Analysis in the web interface. @@ -499,6 +539,10 @@ go_term: The GO-term associated with the current tryptic peptide. protein_count: amount of proteins matched with the given tryptic peptide that are labeled with the current GO-term. name: Optional, name of the GO-term. Included when the extra parameter is set to true. + code: InterPro entry code associated with the current tryptic peptide + protein_count: amount of proteins matched with the given tryptic peptide that are labeled with the current InterPro code. + type: Optional, type of the InterPro entry. Included when the extra parameter is set to true. + name: Optional, name of the InterPro entry. Included when the extra parameter is set to true. **Attributions**