comparison uniprot_ID_mapper/uniprot_id_mapping_071615.py @ 3:6651ac4651f0 draft default tip

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3:6651ac4651f0

#!/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 timeit
start = timeit.default_timer()
try:
    import xml.etree.cElementTree as ET
except ImportError:
    import xml.etree.ElementTree as ET

"""print 
http://www.uniprot.org/help/programmatic_access#id_mapping_examples
http://www.uniprot.org/help/uploadlists

From input files or stdin get accessions/IDs to map.
An input option can specify what id_type the input accessions/IDs are, 
otherwise, we should attempt to categorize the input accessions/IDs according to their inferred id_type

input_dict = dict()
for id_string in input_ids:
  (id,id_type) = getAccession(id_string)
  if not id_type in input_dict:
    input_dict[id_type] = []
  input_dict[id_type].append(id)


# We need to retrieve by from_id_type and to_id_type
for id_type in input_dict:
  for 


tabular output:
#input_id	to_id	to_id


"""
def warn_err(msg,exit_code=1):
    sys.stderr.write(msg)
    if exit_code:
      sys.exit(exit_code)

def __main__():
  version = '1.0'
  idDict = {
    #Category:UniProt
    'ACC+ID':'UniProtKB AC/ID',
    'ACC':'UniProtKB AC',
    'ID':'UniProtKB ID',
    'UPARC':'UniParc',
    'NF50':'UniRef50',
    'NF90':'UniRef90',
    'NF100':'UniRef100',
    'GENENAME':'Gene name',
    #Category:Other sequence databases
    'EMBL_ID':'EMBL/GenBank/DDBJ',
    'EMBL':'EMBL/GenBank/DDBJ CDS',
    'PIR':'PIR',
    'UNIGENE_ID':'UniGene',
    'P_ENTREZGENEID':'Entrez Gene (GeneID)',
    'P_GI':'GI number*',
    'P_REFSEQ_AC':'RefSeq Protein', 
    'REFSEQ_NT_ID':'RefSeq Nucleotide',
    #Category:3D structure databases
    'PDB_ID':'PDB',
    'DISPROT_ID':'DisProt',
    #Category:Protein-protein interaction databases
    'BIOGRID_ID':'BioGrid',
    'DIP_ID':'DIP',
    'MINT_ID':'MINT',
    'STRING_ID':'STRING',
    #Category:Chemistry
    'CHEMBL_ID':'ChEMBL',
    'DRUGBANK_ID':'DrugBank',
    'GUIDETOPHARMACOLOGY_ID':'GuidetoPHARMACOLOGY',
    #Category:Protein family/group databases
    'ALLERGOME_ID':'Allergome',
    'MEROPS_ID':'MEROPS',
    'MYCOCLAP_ID':'mycoCLAP',
    'PEROXIBASE_ID':'PeroxiBase',
    'REBASE_ID':'REBASE',
    'TCDB_ID':'TCDB',
    #Category:Polymorphism databases
    'DMDM_ID':'DMDM',
    #Category:2D gel databases
    'WORLD_2DPAGE_ID':'World-2DPAGE',
    #Category:Protocols and materials databases
    'DNASU_ID':'DNASU',
    #Category:Genome annotation databases
    'ENSEMBL_ID':'Ensembl',
    'ENSEMBL_PRO_ID':'Ensembl Protein',
    'ENSEMBL_TRS_ID':'Ensembl Transcript',
    'ENSEMBLGENOME_ID':'Ensembl Genomes',
    'ENSEMBLGENOME_PRO_ID':'Ensembl Genomes Protein',
    'ENSEMBLGENOME_TRS_ID':'Ensembl Genomes Transcript',
    'P_ENTREZGENEID':'GeneID',
    'KEGG_ID':'KEGG',
    'PATRIC_ID':'PATRIC',
    'UCSC_ID':'UCSC',
    'VECTORBASE_ID':'VectorBase',
    #Category:Organism-specific gene databases
    'ARACHNOSERVER_ID':'ArachnoServer',
    'CGD':'CGD',
    'CONOSERVER_ID':'ConoServer',
    'CYGD_ID':'CYGD',
    'DICTYBASE_ID':'dictyBase',
    'ECHOBASE_ID':'EchoBASE',
    'ECOGENE_ID':'EcoGene',
    'EUHCVDB_ID':'euHCVdb',
    'EUPATHDB_ID':'EuPathDB',
    'FLYBASE_ID':'FlyBase',
    'GENECARDS_ID':'GeneCards',
    'GENEFARM_ID':'GeneFarm',
    'GENOLIST_ID':'GenoList',
    'H_INVDB_ID':'H-InvDB',
    'HGNC_ID':'HGNC',
    'HPA_ID':'HPA',
    'LEGIOLIST_ID':'LegioList',
    'LEPROMA_ID':'Leproma',
    'MAIZEGDB_ID':'MaizeGDB',
    'MIM_ID':'MIM',
    'MGI_ID':'MGI',
    'NEXTPROT_ID':'neXtProt',
    'ORPHANET_ID':'Orphanet',
    'PHARMGKB_ID':'PharmGKB', 
    'POMBASE_ID':'PomBase',
    'PSEUDOCAP_ID':'PseudoCAP',
    'RGD_ID':'RGD',
    'SGD_ID':'SGD',
    'TAIR_ID':'TAIR',
    'TUBERCULIST_ID':'TubercuList',
    'WORMBASE_ID':'WormBase',
    'WORMBASE_TRS_ID':'WormBase Transcript',
    'WORMBASE_PRO_ID':'WormBase Protein',
    'XENBASE_ID':'Xenbase',
    'ZFIN_ID':'ZFIN',
    #Category:Phylogenomic databases
    'EGGNOG_ID':'eggNOG',
    'GENETREE_ID':'GeneTree',
    'HOGENOM_ID':'HOGENOM',
    'HOVERGEN_ID':'HOVERGEN',
    'KO_ID':'KO',
    'OMA_ID':'OMA',
    'ORTHODB_ID':'OrthoDB',
    'PROTCLUSTDB_ID':'ProtClustDB',
    'TREEFAM_ID':'TreeFam',
    #Category:Enzyme and pathway databases
    'BIOCYC_ID':'BioCyc',
    'REACTOME_ID':'Reactome',
    'UNIPATHWAY_ID':'UniPathWay',
    #Category:Gene expression databases
    'CLEANEX_ID':'CleanEx',
    #Category:Other
    'CHITARS_ID':'ChiTaRS',
    'GENOMERNAI_ID':'GenomeRNAi',
    'GENEWIKI_ID':'GeneWiki',
    'NEXTBIO_ID':'NextBio'
  }

  """ 
  TODO:  
    Would be better to be able to infer the from_id type for the input accession/ID  especially for a fasta file
    - Allow the options.from_id to be unspecified and empty, and try to determine the from_id by the input ID/Accession.  

  # rather than an array of input accessions, we need to put them in a dict() by type
  def addAccession(id_dict,id_type,id):
    if id_type not in id_dict:
      id_dict[id_type] = []-++
    id_dict[id_type] = [].append(id)

  # returns(accession, id_type)
  def getAccession(header, matchType):
    # (format regex pattern, FROM_ID)
    # TODO  try to determine which type of accession ID we have by matching by regular expressions
    # each regex match should have a groups[0] that given the accession
    # for id_types, see: http://www.uniprot.org/help/programmatic_access#id_mapping_examples
    fmts = [
      ('>?(?:sp|tr|sw)\|(\w+).*','ACC+ID'),
      ('>?gi|\d+\|ref\|(NP_\d+\.\d+).*','ACC+ID'),
      ('>/UniRef\d+_(\w+).*','ACC+ID'),
      ('>/(UPI\d+).*','ACC+ID'),
      ('NP_\d+\.\d+','ACC+ID'),
      ('ENSP\d+','ENSEMBL_PRO_ID'),
      ('ENST\d+','ENSEMBL_TRS_ID'),
    ]
    for pat,cat in fmts:
      m = re.match(pat,header)
      if m:
        matchType.append(cat)
        return (m.groups()[0],cat)
    matchType.append('ACC+ID')
    return (header,'ACC+ID')

    # Then when we are retrieving the id mappings, we need to incrementally fetch by from_id / to_id types
  idMaps = dict() # {to_id,idMap}
  for to_id in options.to_id:
    idMap = dict()
    idMaps[to_id] = idMap
    for (from_id,ids) id_dict.items():
      # limit the requests to 500 at a time
      idx = range(0,len(ids),500)
      idx.append(len(ids))
      for i in range(len(idx)-1):
        getIdMapping(ids[idx[i]:idx[i+1]],from_id,to_id,idMap=idMap)
  """

  """
  Some examples of fasta ID lines From:
    https://code.google.com/p/compomics-utilities/source/browse/trunk/src/main/java/com/compomics/util/protein/Header.java
>sw|Pxxxx|ACTB_HUMAN xxxx xxx xxxx ...
>gi|xxxxx|xx|xxxxx|(x) xxxx xxx xxxx ...
>IPI:IPIxxxxxx.y|REFSEQ_XP:XP_aaaaa[|many more like this can be present] Tax_Id=9606 descr
>HIT000000001.10|HIX0021591.10|AB002292.2|NO|NO|HC|cds 185..4219|DH domain containing protein.
>OExyz (OExyz) xxx xxx xxx
>hflu_lsi_xxxx xxx xxx xxx
>C_tr_Lx_x [xxx - xxx] | xxx xxx
>M. tub.xxx|Rvxxx| xxx xxx
// Drosophile DB.
// We need to find two elements:
//   - the accession String (retrieved as the trimmed version of everything
//     up to (and NOT including) " pep:"
//   - the description (everything (trimmed) starting from (and including) the " pep:".
>CGxxxxxx pep:xxxxxx
>xxxx xxx SGDID:xxxx xxx
>generic_some_tag|proten_accession|a description for this protein
// Old (everything before 9.0 release (31 Oct 2006)) standard SwissProt header as
// present in the Expasy FTP FASTA file.
// Is formatted something like this:
>XXX_YYYY (acc) rest
>sp|accession|ID descr rest (including taxonomy, if available
>tr|accession|ID descr rest (including taxonomy, if available)
>nxp|NX_P02768-1|ALB|Serum albumin|Iso
// New (9.0 release (31 Oct 2006) and beyond) standard SwissProt header as
// present in the Expasy FTP FASTA file.
// Is formatted something like this:
>accession|ID descr rest (including taxonomy, if available)
// Flybase FASTA format.
>FBxxx type=xxx
// A header translating a genome sequence into a protein sequences.
// We need to find two elements, separated by a space:
//   - the accession string (retrieved as the first part of a space delimited String).
//   - the nucleic acid start and stop site (between brackets, separated by a '-').
>dm345_3L-sense [234353534-234353938]
>dmic_c_1_469 Dialister micraerophilus DSM 19965 [161699 - 160872] aspartate-semialdehyde dehydrogenase Database
>synsp_j_c_8_5 Synergistes[G-2] sp. oral taxon 357 W5455 (JCVI) [820 - 1089]  ORF
// The Arabidopsis thaliana database; TAIR format
>AT1G08520.1 | Symbol: PDE166 | magnesium-chelatase subunit chlD, chloroplast, putative / Mg-protoporphyrin IX chelatase, putative (CHLD), similar to Mg-chelatase SP:O24133 from Nicotiana tabacum, GB:AF014399 GI:2318116 from (Pisum sativum) | chr1:2696415-2700961 FORWARD | Aliases: T27G7.20, T27G7_20, PDE166, PIGMENT DEFECTIVE 166
...
// Okay, try the often-used 'generic' approach. If this fails, we go to the worse-case scenario, ie. do not process at all.
// Testing for this is somewhat more complicated.
// Often used simple header; looks like:
// >NP0465 (NP0465) A description for this protein.
// We need to find two elements:
//   - the accession String (easily retrieved as the next String until a space is encountered).
//   - the description
>NP0465 (NP0465) A description for this protein.
 GenBank                           gb|accession|locus
 EMBL Data Library                 emb|accession|locus
 DDBJ, DNA Database of Japan       dbj|accession|locus
 NBRF PIR                          pir||entry
 Protein Research Foundation       prf||name
 SWISS-PROT                        sp|accession|entry name
 Brookhaven Protein Data Bank      pdb|entry|chain
 Patents                           pat|country|number
 GenInfo Backbone Id               bbs|number
 General database identifier       gnl|database|identifier
 NCBI Reference Sequence           ref|accession|locus
 Local Sequence identifier         lcl|identifier
  """


  def getFastaAccession(header):
    # TODO parse the ID and return the accession
    # (format regex pattern, FROM_ID)
    # TODO  try to determine which type of accession ID we have by matching by reg|([A-NR-Z][0-9](?:[A-Z][A-Z0-9]{2}[0-9]){1,2})ular expressions
    # each regex match should have a groups[0] that given the accession
    # for id_types, see: http://www.uniprot.org/help/programmatic_access#id_mapping_examples
    fmts = [
      ('>?(?:sp|tr|sw)\|(\w+).*','ACC+ID'),
      ('>?gi|\d+\|ref\|(NP_\d+\.\d+).*','ACC+ID'),
      ('NP_\d+\.\d+','ACC+ID'),

      ('([OPQ][0-9][A-Z0-9]{3}[0-9])|([A-NR-Z][0-9](?:[A-Z][A-Z0-9]{2}[0-9]){1,2})','ACC'),
      ('(...*)_....*','ID'),
      ('>/(UPI\d+).*','UPARC'),
      ('>/UniRef50_(\w+).*','NF50'),
      ('UniRef90_(\w+).*','NF90'),
      ('UniRef100_(\w+).*','NF100'),
      ('([A-C][A-Z]?\d{6})|(DM-Z)\d{5})','EMBL_ID'),
      ('ENSP\d+','ENSEMBL_PRO_ID'),
      ('ENST\d+','ENSEMBL_TRS_ID')
    ]
    for pat,cat in fmts:
      m = re.match(pat,header)
      if m:
        return m.groups()[0]
    return header

  # rather than an array of input accessions, we need to put them in a dict() by type
  def addAccession(id_dict,id_type,id):
    for idtype in id_type:
      if idtype not in id_dict:
        id_dict[idtype] = []
      id_dict[idtype].append(id)

  # returns(accession, id_type)
  def getAccession(header):
    # (format regex pattern, FROM_ID)
    # TODO  try to determine which type of accession ID we have by matching by regular expressions
    # each regex match should have a groups[0] that given the accession
    # for id_types, see: http://www.uniprot.org/help/programmatic_access#id_mapping_examples
    fmts = [
      ('([OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9](?:[A-Z][A-Z0-9]{2}[0-9]){1,2})','ACC'),
      ('.*_[A-Z]*','ID'),
      ('UPI(\d+).*','UPARC'),
      ('(UniRef50_\w+.*)','NF50'),
      ('UniRef90_(\w+).*','NF90'),
      ('(UniRef100_\w+.*)','NF100'),
      ('a[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9](?:[A-Z][A-Z0-9]{2}[0-9]){1,2}','GENENAME'),

      ('([A-L][A-Z]?\d{6})|([A-NR-Z]\d{5})|([A-Z]{4}\d{8})','EMBL_ID'), 
      ('K\d*','KO_ID'),

      ('([A-Z]*\d*\.\d$)','EMBL'),
      ('([IBC]\d{5})','PIR'),
      ('(Hs\.\d*)','UNIGENE_ID'),
      ('[A-Z]P_(\d*\.\d)','P_REFSEQ_AC'),
      ('[NX][MC]_(\d*\.\d)','REFSEQ_NT_ID'),
      ('(\d[A-Z0-9]{3})','PDB_ID'),
      ('(DP\d{5})','DISPROT_ID'),
      ('(DIP-\d*N$)','DIP_ID'),
      ('(MINT-\d*)','MINT_ID'),
      ('(9606\.ENSP\d*)','STRING_ID'),
      ('(CHEMBL\d*)','CHEMBL_ID'),
      ('(DB\d*)','DRUGBANK_ID'),
      ('([A-Z]\d\d\.[A-Z0-9]\d{2})','MEROPS_ID'),
      ('NOTHING POPS UP','MYCOCLAP_ID'),
      ('(\d\.[A-Z](?:\.\d*){3})','TCDB_ID'),
      ('NOTHING POPS UP','WORLD_2DPAGE_ID'),
      ('(ENSG\d*)','ENSEMBAF85406.1BL_ID'),
      ('(ENSP\d+)','ENSEMBL_PRO_ID'),
      ('(ENST\d+)','ENSEMBL_TRS_ID'),
      (' ','ENSEMBLGENOME_ID'),
      (' ','ENSEMBLGENOME_PRO_ID'),
      (' ','ENSEMBLGENOME_TRS_ID'),
      ('(hsa:\d*)','KEGG_ID'),
      ('(uc\d*[a-z]*\.\d$)','UCSC_ID'),
      ('VEL:J:LDKJFS','VECTORBASE_ID'),
      ('AS\d*','ARACHNOSERVER_ID'),
      


      ('(.*[CKN]OG\d*)','EGGNOG_ID'),
      ('(ENSGT\d*)','GENETREE_ID'),
      ('HOG\d*','HOGENOM_ID'),
      ('HBG\d*','HOVERGEN_ID'),

      ('[A-Z]{7}','OMA_ID'),

      ('EOG\d*','ORTHODB_ID'),
      ('asdfsadfasdf','PROTCLUSTDB_ID'),
      ('TF\d*','TREEFAM_ID'),
      ('REACT_\d*','REACTOME_ID'),
      ('asdfasl;DF:LJk','UNIPATHWAY_ID'),
      ('HS_\d*','CLEANEX_ID'),
      ('SAMEAS GENE NAME','CHITARS_ID'),
      ('GENENAME_\(gene\)','GENEWIKI_ID'),
    ]
    if re.match('\d*$', header): # For ambiguous number only ID types
         numIDtypes = [
           'P_ENTREZGENEID',
           'P_GI',
           'DMDM_ID',
           'BIOGRID_ID',
           'GUIDETOPHARMACOLOGY_ID',
           'ALLERGOME_ID',
           'PEROXIBASE_ID',
           'REBASE_ID',
           'DNASU_ID',
           'GENOMERNAI_ID',
           'NEXTBIO_ID']
         ambiguous = []
         for numIDs in numIDtypes:
           nm = getIdMapping([header],numIDs,'ACC',ambiguity=True) 
           if nm != None:
              ambiguous.append(nm)
         if ambiguous == []:
           ambiguous.append('No Match')
         return (header, ambiguous)
    for pat,cat in fmts:
      m = re.match(pat,header)
      if m:
        return (m.groups()[0],[cat])
    return (header,['ACC+ID'])


  def read_tabular(filepath,col):
    accessions = []
    with open(filepath) as fp:
      for i,line in enumerate(fp):
        if line.strip() == '' or line.startswith('#'):
          continue
        fields = line.rstrip('\n').split('\t')
        accession = fields[col]
        accessions.append(accession)
    return accessions

  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):
    accessions = []
    with open(filepath) as fp:
      for id, peptide in get_fasta_entries(fp):
        accessions.append(getFastaAccession(id))
    return accessions

  def read_mzid(fp):
    accessions = []
    for event, elem in ET.iterparse(fp):
      if event == 'end':
        if re.search('DBSequence',elem.tag):
          accessions.append(elem.attrib['accession'])
    return accessions

  def read_pepxml(fp):
    accessions = []
    for event, elem in ET.iterparse(fp):
      if event == 'end':
        if re.search('search_hit',elem.tag):
          accessions.append(elem.get('protein'))
    return accessions

  def getUniprotSequence(uniprot_id):
    url = "http://www.uniprot.org/uniprot/%s.fasta" % uniprot_id 
    print url
    fp = urllib2.urlopen(url)
    for id, seq in get_fasta_entries(fp):
      if seq:
        return seq
    return ''




  def getIdMapping(accessions,from_id,to_id,fh=None,idMap=None,ambiguity=None,crossReference=None,idMaps=None):
    # print >> sys.stderr, "%s accessions(%d): %s ..." % (to_id,len(accessions),' '.join(accessions[:min(len(accessions),3)]))
    if not accessions:
      return
    url = 'http://www.uniprot.org/mapping/'
    # Cross Referencing: Mapping to non-UniprotKB ('ACC') IDs to other non-UniprotKB ('ACC') IDs
    if to_id != 'ACC' and from_id != 'ACC':
      crMap = {}
      getIdMapping(accessions,from_id,'ACC',idMap=crMap)
      crMap2 = crMap.copy()
      for x in crMap.keys():
         for y in crMap[x].keys():
            for z in crMap[x][y]: 
               crMap2[x][y] = []
               getIdMapping([z],'ACC',to_id,idMap=crMap2,crossReference=[x,from_id])
      idMap.update(crMap2)
      return
    params = {
    'from': from_id,
    'to': to_id,
    'format':'tab',
    'query': '\n'.join(accessions)
    }
    data = urllib.urlencode(params)
    request = urllib2.Request(url, data)
    contact = "" # Please set your email address here to help us debug in case of problems.
    request.add_header('User-Agent', 'Python %s' % contact)
    response = None
    for i in range(3):
      try:
        response = urllib2.urlopen(request)
        break
      except Exception, e:
        warn_err("%s",exit_code=None)
    if response:
      response.next()
    print params

    for line in response:
      # print >> sys.stderr, "idMap: %s" % line
      if fh:
        fh.write(line)
      if ambiguity: # if there was a response, then an ambiguous match can be made
        return from_id
      if idMap != None:
        id1,id2 = line.strip().split('\t')
        print id2
        if crossReference != None:
          id1, from_id = crossReference
        # print >> sys.stderr, "idMap: %s:%s" % (id1,id2)
        try:
           idMap[id1][from_id].append(id2)
        except:
           idMap[id1] = {from_id:[id2]}
    if ambiguity == None:
      for line in response:
        for acc in accessions:
          idMap[acc] = {from_id:['N/A']}
        return


  #Parse Command Line
  parser = optparse.OptionParser()
  # 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' )
  # Decoy pattern
  parser.add_option( '-D', '--decoy', dest='decoy', default=None, help='Decoy pattern to be trimmed from IDs , e.g. _REVERSED' )
  # filter patterns
  parser.add_option( '-I', '--include', dest='include_pat', default=None, help='Include pattern to filter IDs' )
  parser.add_option( '-X', '--exclude', dest='exclude_pat', default=None, help='Exclude pattern to filter IDs' )
  # Unipept Flags
  parser.add_option( '-F', '--from', dest='from_id', default='ACC+ID', choices=idDict.keys(), help='From ID type' )
  parser.add_option( '-T', '--to', dest='to_id', default=[], action="append", choices=idDict.keys(), help='To ID type' )
  # output files
  parser.add_option( '-o', '--output', dest='output', default=None, help='Output file path for TAB-separated-values')
  # parser.add_option( '-O', '--format', dest='format', default='tab', choices=['list','tab','json'], help='output format' )
  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 >> sys.stdout,"%s" % version
    sys.exit(0)

  accessions = []
  ## Get accessions
  if options.mzid:
    accessions += read_mzid(options.mzid)
  if options.pepxml:
    accessions += read_pepxml(options.pepxml)
  if options.tabular:
    accessions += read_tabular(options.tabular,options.column) 
  if options.fasta:
    accessions += read_fasta(options.fasta) 
  if args and len(args) > 0:
    for i,accession in enumerate(args):
      accessions.append(accession) 
  # filter accessions
  if options.decoy:
    filtered_accs = [re.sub(options.decoy,'',x) for x in accessions]
    accessions = filtered_accs 
  if options.include_pat:
    filtered_accs = []
    for acc in accessions:
      if re.match(options.include_pat,acc):
        filtered_accs.append(acc)
    accessions = filtered_accs 
  if options.exclude_pat:
    filtered_accs = []
    for acc in accessions:
      if not re.match(options.exclude_pat,acc):
        filtered_accs.append(acc)
    accessions = filtered_accs 
  if len(accessions) < 1:
    warn_err("No accessions input!",exit_code=1)
  if options.output != None:
    try:
      outputPath = os.path.abspath(options.output)
      outputFile = open(outputPath, 'w')
    except Exception, e:
      print >> sys.stderr, "failed: %s" % e
      exit(3)
  else:
    outputFile = sys.stdout


  # Removes duplicates in accessions
  seen = set()
  seen_add = seen.add
  accessions = [x for x in accessions if not (x in seen or seen_add(x))]
  # Sorts accessions to inferred ID types i+n a dictionary
  id_dict = {}
  for header in accessions:
      id , id_types = getAccession(header)
      addAccession(id_dict,id_types,id)
  idMaps = dict() # {to_id,idMap}
  for to_id in options.to_id:
    idMap = dict()
    idMaps[to_id] = idMap
    for (from_id,ids) in id_dict.items():
      # limit the requests to 500 at a time
      idx = range(0,len(ids),500)
      idx.append(len(ids))
      for i in range(len(idx)-1):
        getIdMapping(ids[idx[i]:idx[i+1]],from_id,to_id,idMap=idMap,idMaps=idMaps)
        print ids
#Write output 
  #Output Table Header
  outputFile.write("\n#%-17s" % (options.from_id))
  for t in options.to_id:
    outputFile.write("%-18s" % t)
  outputFile.write("\n" + ("=" * 18) + ("=" * 18 * len(options.to_id)) + "\n")

  # Create an output-friendly matrix
  idArray = [[[] for x in range(len(options.to_id))] for x in range(len(accessions))]
  for a, acc in enumerate(accessions):
     idLength = 0
     for i, to_id in enumerate(options.to_id): # [[ids],[ids]]
        idArrayColumn = []
        idPairs = idMaps[to_id][acc] #{from_id:[IDs]} -> [from_id1,from_id2,from_id3]
        for from_id in idPairs:
            ids = idPairs[from_id]
            for id_ in ids:
                idArrayColumn.append("%s[%s]" % (id_,from_id))
        if idLength < len(idArrayColumn):
            idLength = len(idArrayColumn)
        idArray[a][i].extend(idArrayColumn)
     for y in range(idLength):
        outputFile.write("%-18s" % acc)
        for x in range(len(options.to_id)):
           try:
             outputFile.write("%-18s" % idArray[a][x][y])
           except:
             outputFile.write(" " * 18)
        outputFile.write("\n")
  # Output Matrix
  
                 

  #print idMaps
 # python uniprot_id_mapping.py -T ACC -T PDB_ID -T ENSEMBL_PRO_ID -T ENSEMBL_TRS_ID -t test-data/old-inputs/var.tsv
  stop = timeit.default_timer()
  outputFile.write("%s %s %-s\n\n" % ("run time: ", stop - start, "seconds"))

if __name__ == "__main__" : __main__()