view unipept.py @ 1:b65ee881ca64 draft

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

#!/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

"""
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 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 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_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 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', '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 )
    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 __name__ == "__main__" : __main__()