Package csb :: Package bio :: Package io :: Module hhpred |
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# Source Code for Module csb.bio.io.hhpred

      1  """
      2  HHpred-related format parsers.
      3
      4  This module defines two HHpred format parsers: L{HHProfileParser} and
      5  L{HHOutputParser}. The first one is used to read HMM (*.hhm) files:
      6
      7      >>> HHProfileParser("profile.hhm", "profile.pdb").parse()
      8      <ProfileHMM>            # ProfileHMM object
      9
     10  while the latter parses HHsearch results files (*.hhr):
     11
     12      >>> HHOutputParser().parse_file("hits.hhr"):
     13      <HHpredHitList>        # collection of HHpredHit-s
     14
     15  See L{ProfileHMM}, L{HHpredHitList} and L{HHpredHit} from L{csb.bio.hmm}
     16  for details. For text serialization of HMM profiles, see L{HHMFileBuilder}
     17  in this module.
     18  """
     19
     20  import os
     21  import re
     22
     23  import csb.io
     24  import csb.bio.io
     25  import csb.bio.structure as structure
     26
     27  from csb.core import Enum, CollectionIndexError, ItemNotFoundError
     28  from csb.bio.sequence import Sequence, ProteinAlphabet, A3MAlignment
     29
     30  from csb.bio.hmm import State, Transition, ProfileHMM, HMMLayer, ProfileLength
     31  from csb.bio.hmm import HHpredHitList, HHpredHit, ScoreUnits, States, EVDParameters
     32  from csb.bio.hmm import StateNotFoundError, TransitionNotFoundError
     33
     34
     35 -class HHProfileFormatError(ValueError):
     36      pass
     37
     38 -class StructureFormatError(HHProfileFormatError):
     39      pass
     40
     41 -class HHOutputFormatError(HHProfileFormatError):
     42      pass
     43
     44
     45 -class HHProfileParser(object):
     46      """
     47      A class that is HHpred HMM format aware.
     48      Produces a L{ProfileHMM} object representation of a given HHpred profile
     49      HMM.
     50
     51      @param hhm_file: *.hhm file name to parse
     52      @type hhm_file: str
     53      @param pdb_file: an optional hhm structure file, containing the structural
     54                       data, associated with the HMM.
     55      @type pdb_file: str
     56      @note: This is NOT a regular PDB file! It must be specifically formatted
     57             for use with HHpred.
     58
     59      @raise IOError: if any of the files does not exist
     60      """
     61
     62 -    def __init__(self, hhm_file, pdb_file=None):
     63
     64          if not os.path.exists(hhm_file):
     65              raise IOError("Could not read HHM file {0}".format(hhm_file))
     66
     67          if pdb_file:
     68              if not os.path.exists(pdb_file):
     69                  raise IOError("Could not read structure file {0}".format(pdb_file))
     70
     71          self._file = hhm_file
     72          self._pdb = pdb_file
     73          self._properties = None
     74          self._sequences = None
     75          self._profile = None
     76
     77          self._chopped = False
     78          self._chop()
     79
     80 -    def format_structure(self, input_pdb, chain_id, output_file):
     81          """
     82          Format input_pdb as an HHpred pre-parsed structure file for the current
     83          HMM. Formatting means: only chain_id left in the PDB file, residue
     84          sequence numbers strictly corresponding to the HMM states' ranks.
     85
     86          @param input_pdb: source PDB file name
     87          @type input_pdb: str
     88          @param chain_id: source chain ID (which chain to pick from the
     89                           structure)
     90          @type chain_id: str
     91          @param output_file: save the output PDB file here
     92          @type output_file: str
     93
     94          @raise StructureFormatError: when the specified PDB chain does not
     95                                       correspond to the HMM.
     96          """
     97
     98          hmm = self.parse()
     99          s = csb.bio.io.StructureParser(input_pdb).parse_structure()
    100          chain = s.chains[chain_id]
    101
    102          if chain.length != hmm.layers.length:
    103              raise StructureFormatError(
    104                          "{0}: Incorrect number of residues".format(chain.entry_id))
    105
    106          for layer, residue in zip(hmm.layers, chain.residues):
    107
    108              if residue.type == ProteinAlphabet.UNK:
    109                  residue.type = layer.residue.type
    110
    111              if residue.type != layer.residue.type:
    112                  msg = "Expected residue of type {0} at position {1}"
    113                  raise StructureFormatError(msg.format(layer.residue.type, layer.rank))
    114
    115              residue.id = str(residue.rank), '$'
    116
    117          for residue in chain.residues:
    118              residue.id = str(residue.rank), None
    119
    120          new_structure = structure.Structure(hmm.id)
    121          new_structure.chains.append(chain.clone())
    122
    123          new_structure.to_pdb(output_file)
    124
    125 -    def parse(self, units=ScoreUnits.LogScales):
    126          """
    127          Parse the HMM profile.
    128
    129          @param units: also convert the profile score units to the specified
    130                        L{csb.bio.hmm.ScoreUnits} kind
    131          @type units: L{csb.core.EnumItem}
    132
    133          @return: a L{ProfileHMM} instance
    134          @rtype: L{ProfileHMM}
    135
    136          @raise HHProfileFormatError: when the hhm file is invalid/corrupt
    137          """
    138          assert self._chopped
    139
    140          hmm = ProfileHMM(units=units, scale=-1000.0, logbase=2)
    141          if self._profile:
    142              self._parse_profile(hmm, units)
    143          else:
    144              raise HHProfileFormatError('Missing HMM profile table.')
    145
    146          if self._properties:
    147              self._parse_properties(hmm)
    148          else:
    149              raise HHProfileFormatError('No profile properties found.')
    150
    151          if self._sequences:
    152              self._parse_sequences(hmm)
    153          else:
    154              raise HHProfileFormatError('No profile MSA and secondary structure found.')
    155
    156          if hmm.dssp:
    157              hmm._assign_secstructure()
    158
    159          if self._pdb:
    160              self._parse_structure(hmm)
    161
    162          return hmm
    163
    164 -    def _chop(self):
    165          """
    166          Chop the HHM file into pieces - HMM properties, secondary structure +
    167          MSA, HMM.
    168          """
    169
    170          try:
    171              with open(self._file, 'r') as f:
    172                  pr = csb.io.EntryReader(f, 'HHsearch', 'SEQ')
    173                  self._properties = pr.readall()[0].splitlines()
    174
    175              with open(self._file, 'r') as f:
    176                  sr = csb.io.EntryReader(f, '>', '#')
    177                  self._sequences = sr.readall()
    178
    179              with open(self._file, 'r') as f:
    180                  hr = csb.io.EntryReader(f, '#', '//')
    181                  self._profile = hr.readall()[0].splitlines()
    182
    183              self._chopped = True
    184
    185          except IndexError:
    186              raise HHProfileFormatError('Corrupt HHM file.')
    187
    188 -    def _parse_properties(self, hmm):
    189          """
    190          Parse the profile properties
    191
    192          @param hmm: the hmm object being constructed
    193          @type hmm: L{ProfileHMM}
    194          @return: the updated hmm
    195          @rtype: hmm
    196
    197          @raise NotImplementedError: if an unexpected data field is encountered
    198          """
    199          assert self._chopped
    200
    201          for line in self._properties:
    202
    203              if line.startswith('NAME '):
    204                  hmm.name = line[6:].strip()
    205
    206              elif line.startswith('FAM  '):
    207                  hmm.family = line[6:].strip()
    208
    209              elif line.startswith('LENG '):
    210                  m = re.search('(\d+)\D+(\d+)', line).groups()
    211                  m = tuple(map(int, m))
    212                  hmm.length = ProfileLength(m[0], m[1])
    213
    214              elif line.startswith('FILE '):
    215                  hmm.id = line[6:].strip()
    216
    217              elif line.startswith('HHsearch'):
    218                  hmm.version = float(line[9:])
    219
    220              elif line.startswith('NEFF '):
    221                  hmm.effective_matches = float(line[6:])
    222
    223              elif line.startswith('PCT  '):
    224                  if line[6:].strip().lower() == 'true':
    225                      hmm.pseudocounts = True
    226
    227              elif line.startswith('EVD  '):
    228                  lamda, mu = map(float, line[6:].split())
    229                  hmm.evd = EVDParameters(lamda, mu)
    230
    231              elif line[:5] in ('COM  ', 'DATE ', 'FILT '):
    232                  pass
    233              else:
    234                  raise NotImplementedError("Unexpected line: {0}.".format(line[0:5]))
    235
    236          if not hmm.id and hmm.name:
    237              hmm.id = hmm.name.split()[0]
    238          return hmm
    239
    240 -    def _parse_sequences(self, hmm):
    241          """
    242          Parse secondary structure and multiple alignment sections.
    243
    244          @param hmm: the hmm object being constructed
    245          @type hmm: L{ProfileHMM}
    246          @return: the updated hmm
    247          @rtype: hmm
    248          """
    249          assert self._chopped
    250
    251          psipred = None
    252          msa_entries = []
    253
    254          for entry in self._sequences:
    255
    256              header_token = entry[:8]
    257              if header_token in ['>ss_dssp', '>sa_dssp', '>ss_pred', '>ss_conf', '>Consens']:
    258
    259                  lines = entry.strip().splitlines()
    260                  seq = re.sub('\s+', '', ''.join(lines[1:]))
    261
    262                  if header_token == '>ss_dssp':
    263                      hmm.dssp = structure.SecondaryStructure(seq)
    264                  elif header_token == '>sa_dssp':
    265                      hmm.dssp_solvent = seq
    266                  elif header_token == '>ss_pred':
    267                      psipred = seq
    268                  elif header_token == '>ss_conf':
    269                      conf = seq
    270                      hmm.psipred = structure.SecondaryStructure(psipred, conf)
    271                  elif header_token == '>Consens':
    272                      hmm.consensus = Sequence('Consensus', 'Consensus', seq)
    273              else:
    274                  msa_entries.append(entry)
    275
    276          if msa_entries:
    277              msa = '\n'.join(msa_entries)
    278              hmm.alignment = A3MAlignment.parse(msa, strict=False)
    279
    280          return hmm
    281
    282 -    def _parse_profile(self, hmm, units=ScoreUnits.LogScales):
    283          """
    284          Parse the HMM profile.
    285
    286          @param hmm: the hmm object being constructed
    287          @type hmm: L{ProfileHMM}
    288          @return: the updated hmm
    289          @rtype: L{ProfileHMM}
    290
    291          @raise NotImplementedError: when an unknown transition string is
    292                                      encountered
    293          """
    294          assert self._chopped
    295
    296          # 0. Prepare start and end states
    297          hmm.start = State(States.Start)
    298          hmm.end = State(States.End)
    299
    300          residues = None
    301          background = {}
    302          tran_types = None
    303          tran_lines = []
    304          start_probs = None
    305
    306          lines = iter(self._profile)
    307          pattern = re.compile('^[A-Z\-]\s[0-9]+\s+')
    308
    309          if units == ScoreUnits.LogScales:
    310
    311              def parse_probability(v):
    312                  if v.strip() == '*':
    313                      return None
    314                  else:
    315                      return float(v)
    316          else:
    317
    318              def parse_probability(v):
    319                  if v.strip() == '*':
    320                      return None
    321                  else:
    322                      return hmm._convert(units, float(v),
    323                                          hmm.scale, hmm.logbase)
    324
    325          # 1. Create all layers (profile columns), create and attach their match states
    326
    327          while True:
    328              try:
    329                  line = next(lines)
    330              except StopIteration:
    331                  break
    332
    333              if line.startswith('NULL'):
    334                  try:
    335                      backprobs = tuple(map(parse_probability, line.split()[1:]))
    336
    337                      line = next(lines)
    338                      residues = line.split()[1:]
    339                      residues = [Enum.parse(ProteinAlphabet, aa) for aa in residues]
    340
    341                      for pos, aa in enumerate(residues):
    342                          background[aa] = backprobs[pos]
    343
    344                      line = next(lines)
    345                      tran_types = line.split()
    346
    347                      line = next(lines)
    348                      start_probs = list(map(parse_probability, line.split()))
    349                  except StopIteration:
    350                      break
    351
    352              elif re.match(pattern, line):
    353                  emrow = line
    354                  try:
    355                      tran_lines.append(next(lines))
    356                      #junkrow = next(lines)
    357                  except StopIteration:
    358                      break
    359
    360                  emprobs = emrow.split()
    361                  if len(emprobs) != 23:
    362                      raise HHProfileFormatError(
    363                              "Unexpected number of data fields: {0}".format(len(emprobs)))
    364
    365                  rank = int(emprobs[1])
    366                  residue = structure.ProteinResidue(
    367                      rank=rank, type=emprobs[0], sequence_number=rank, insertion_code=None)
    368                  if residue.type == ProteinAlphabet.GAP:
    369                      raise HHProfileFormatError("Layer {0} can't be represented by a gap".format(rank))
    370
    371                  new_layer = hmm.layers.append(HMMLayer(rank, residue))
    372                  if new_layer != rank:
    373                      raise HHProfileFormatError('Layer {0} defined as {1}'.format(new_layer, rank))
    374
    375                  match = State(States.Match, emit=Enum.members(ProteinAlphabet))
    376
    377                  match.rank = rank
    378                  match.background.set(background)
    379
    380                  for col, aa in enumerate(residues):
    381                      prob = parse_probability(emprobs[col + 2])
    382                      match.emission.append(aa, prob)
    383
    384                  hmm.layers[new_layer].append(match)
    385                  assert hmm.layers.last_index == match.rank
    386
    387          # 2. Append starting transitions: S -> M[1] and optionally S -> D[1] and S -> I[0].
    388          #    States D[1] and I[0] will be created if needed
    389          #    Note that [0] is not a real layer, I[0] is simply an insertion at the level of Start
    390          if len(hmm.layers) > 0:
    391
    392              first_match = hmm.layers[hmm.layers.start_index]
    393
    394              if start_probs[0] is None:
    395                  raise HHProfileFormatError("Transition Start > Match[1] is undefined")
    396
    397              start_tran = Transition(hmm.start, first_match[States.Match], start_probs[0])
    398              hmm.start.transitions.append(start_tran)
    399
    400              if start_probs[1] is not None and start_probs[3] is not None:  # Start -> I[0] -> M[1]
    401                  start_ins = State(States.Insertion, emit=Enum.members(ProteinAlphabet))
    402                  start_ins.rank = 0
    403                  start_ins.background.set(background)
    404                  start_ins.emission = start_ins.background
    405
    406                  hmm.start_insertion = start_ins
    407                  # Start -> I[0]
    408                  hmm.start.transitions.append(
    409                          Transition(hmm.start, hmm.start_insertion, start_probs[1]))
    410                  # I[0] -> M[1]
    411                  hmm.start_insertion.transitions.append(
    412                          Transition(hmm.start_insertion, first_match[States.Match], start_probs[3]))
    413                  # I[0] -> I[0]
    414                  if start_probs[4]:
    415                      hmm.start_insertion.transitions.append(
    416                          Transition(hmm.start_insertion, hmm.start_insertion, start_probs[4]))
    417
    418
    419              if start_probs[2] is None and start_probs[6] is not None:
    420                  # M->D is corrupt (*) at the Start layer, using D->D instead
    421                  start_probs[2] = start_probs[6]
    422
    423              if start_probs[2] is not None:  # Start -> D[1]
    424                  start_del = State(States.Deletion)
    425                  start_del.rank = 1
    426                  hmm.layers[1].append(start_del)
    427                  start_tran = Transition(hmm.start, first_match[States.Deletion], start_probs[2])
    428                  hmm.start.transitions.append(start_tran)
    429          else:
    430              start_tran = Transition(hmm.start, hmm.end, start_probs[0])
    431              hmm.start.transitions.append(start_tran)
    432
    433
    434          # 3. Append remaining transitions. I and D states will be created on demand.
    435
    436          for rank, fields in enumerate(tran_lines, start=hmm.layers.start_index):
    437              assert hmm.layers[rank][States.Match].rank == rank
    438
    439              ofields = fields.split()
    440              fields = tuple(map(parse_probability, ofields))
    441
    442              # 3a. Parse all Neff values and create I[i] and D[i] states if NeffX[i] is not None
    443              for col, neff in enumerate(tran_types[7:10], start=7):
    444
    445                  if fields[col] is not None:
    446                      neff_value = float(ofields[col]) / abs(hmm.scale)
    447
    448                      if neff == 'Neff':
    449                          hmm.layers[rank].effective_matches = neff_value
    450
    451                      elif neff == 'Neff_I':
    452                          hmm.layers[rank].effective_insertions = neff_value
    453
    454                          if States.Insertion not in hmm.layers[rank]:
    455                              insertion = State(States.Insertion, emit=Enum.members(ProteinAlphabet))
    456                              insertion.background.set(background)
    457                              insertion.emission.set(background)
    458                              insertion.rank = rank
    459                              hmm.layers[rank].append(insertion)
    460
    461                      elif neff == 'Neff_D':
    462                          hmm.layers[rank].effective_deletions = neff_value
    463
    464                          if States.Deletion not in hmm.layers[rank] and neff_value > 0:
    465                              deletion = State(States.Deletion)
    466                              deletion.rank = rank
    467                              hmm.layers[rank].append(deletion)
    468
    469              # 3b. Starting from the first layer, parse all transitions and build the HMM graph stepwise
    470              for col, tran in enumerate(tran_types):
    471                  probability = fields[col]
    472
    473                  if probability is not None:
    474                      try:
    475                          self._add_transition(hmm, rank, tran, probability)
    476                      except (CollectionIndexError, ItemNotFoundError) as ex:
    477                          msg = "Can't add transition {0} at {1}: {2.__class__.__name__}, {2!s}"
    478                          raise HHProfileFormatError(msg.format(tran, rank, ex))
    479
    480          return hmm
    481
    482 -    def _add_transition(self, hmm, rank, tran, probability):
    483
    484          match = hmm.layers[rank][States.Match]
    485          nextmatch = None
    486          if rank < hmm.layers.last_index:
    487              nextmatch = hmm.layers[rank + 1][States.Match]
    488          else:
    489              nextmatch = hmm.end
    490
    491          if tran == 'M->M':
    492              transition = Transition(match, nextmatch, probability)
    493              match.transitions.append(transition)
    494
    495          elif tran == 'M->I':
    496              insertion = hmm.layers[rank][States.Insertion]
    497              transition = Transition(match, insertion, probability)
    498              match.transitions.append(transition)
    499
    500          elif tran == 'M->D':
    501              deletion = State(States.Deletion)
    502              deletion.rank = rank + 1
    503              hmm.layers[rank + 1].append(deletion)
    504              transition = Transition(match, deletion, probability)
    505              match.transitions.append(transition)
    506
    507          elif tran == 'I->M':
    508              insertion = hmm.layers[rank][States.Insertion]
    509              transition = Transition(insertion, nextmatch, probability)
    510              insertion.transitions.append(transition)
    511
    512          elif tran == 'I->I':
    513              insertion = hmm.layers[rank][States.Insertion]
    514              selfloop = Transition(insertion, insertion, probability)
    515              insertion.transitions.append(selfloop)
    516
    517          elif tran == 'D->M':
    518              deletion = hmm.layers[rank][States.Deletion]
    519              transition = Transition(deletion, nextmatch, probability)
    520              deletion.transitions.append(transition)
    521
    522          elif tran == 'D->D':
    523              deletion = hmm.layers[rank][States.Deletion]
    524
    525              if States.Deletion not in hmm.layers[rank + 1]:
    526                  nextdeletion = State(States.Deletion)
    527                  nextdeletion.rank = rank + 1
    528                  hmm.layers[rank + 1].append(nextdeletion)
    529
    530              else:
    531                  nextdeletion = hmm.layers[rank + 1][States.Deletion]
    532                  assert match.transitions[States.Deletion].successor == nextdeletion
    533
    534              transition = Transition(deletion, nextdeletion, probability)
    535              deletion.transitions.append(transition)
    536
    537          else:
    538              if not tran.startswith('Neff'):
    539                  raise NotImplementedError('Unknown transition: {0}'.format(tran))
    540
    541 -    def _parse_structure(self, hmm):
    542          """
    543          Add structural information to an existing HMM.
    544
    545          @param hmm: the hmm object being constructed
    546          @type hmm: L{ProfileHMM}
    547          @return: the updated hmm
    548          @rtype: L{ProfileHMM}
    549
    550          @raise ValueError: if the structure file does not refer to the HMM
    551          @raise HHProfileFormatError: when a residue cannot be assigned to an HMM layer
    552          @raise NotImplementedError: when an unknown data field is encountered
    553                                      in the PDB file
    554          """
    555
    556          assert self._pdb
    557
    558          s = csb.bio.io.StructureParser(self._pdb).parse_structure()
    559
    560          if s.chains.length != 1:
    561              raise StructureFormatError("Must contain exactly one chain")
    562          if s.first_chain.length != hmm.layers.length:
    563              raise StructureFormatError("Incorrect number of residues")
    564
    565          chain = s.first_chain
    566          chain.compute_torsion()
    567
    568          for layer, residue in zip(hmm.layers, chain.residues):
    569
    570              if residue.type != layer.residue.type:
    571                  msg = "Expected residue of type {0} at position {1}"
    572                  raise StructureFormatError(msg.format(layer.residue.type, layer.rank))
    573
    574              layer.residue.torsion = residue.torsion.copy()
    575
    576              for atom in residue.items:
    577                  atom._residue = None    # atom.clone() is much, much slower
    578                  layer.residue.atoms.append(atom)
    579
    580          return hmm
    581
    582  HHpredProfileParser = HHProfileParser
    583
    584
    585 -class HHMFileBuilder(object):
    586      """
    587      Builder for HHpred's hhm files.
    588
    589      @param output: destination stream
    590      @type output: file
    591      """
    592
    593 -    def __init__(self, output):
    594
    595          if not hasattr(output, 'write'):
    596              raise TypeError(output)
    597
    598          self._out = output
    599
    600      @property
    601 -    def output(self):
    602          """
    603          Destination stream
    604          @rtype: stream
    605          """
    606          return self._out
    607
    608 -    def write(self, data):
    609          self._out.write(data)
    610
    611 -    def writeline(self, data):
    612          self.write(data)
    613          self.write('\n')
    614
    615 -    def add_hmm(self, hmm):
    616          """
    617          Append a new HMM to the destination stream.
    618
    619          @param hmm: profile HMM to serialize
    620          @type hmm: L{ProfileHMM}
    621          """
    622
    623          if hmm.score_units != ScoreUnits.LogScales:
    624              raise ValueError('Scores must be converted to LogScales first.')
    625
    626          self.writeline('''HHsearch {0.version}
    627  NAME  {0.name}
    628  FAM   {0.family}
    629  LENG  {0.length.matches} match states, {0.length.layers} columns in multiple alignment
    630  NEFF  {0.effective_matches}
    631  PCT   {0.pseudocounts}'''.format(hmm))
    632          if hmm.evd:
    633              self.writeline('EVD   {0.lamda}  {0.mu}'.format(hmm.evd))
    634
    635          self.writeline('SEQ')
    636          if hmm.dssp:
    637              self.writeline('>ss_dssp')
    638              self.writeline(hmm.dssp.to_string())
    639              if hmm.dssp_solvent:
    640                  self.writeline('>sa_dssp')
    641                  self.writeline(hmm.dssp_solvent)
    642          if hmm.psipred:
    643              self.writeline('>ss_pred')
    644              self.writeline(hmm.psipred.to_string())
    645              self.writeline('>ss_conf')
    646              confidence = [''.join(map(str, m.score)) for m in hmm.psipred]
    647              self.writeline(''.join(confidence))
    648
    649          if hmm.alignment:
    650              if hmm.consensus:
    651                  self.writeline(str(hmm.consensus))
    652              self.writeline(hmm.alignment.format().rstrip('\r\n'))
    653
    654          self.writeline('#')
    655
    656          first_match = hmm.layers[1][States.Match]
    657          null = [int(first_match.background[aa])
    658                  for aa in sorted(map(str, first_match.background))]
    659          self.writeline('NULL   {0}'.format('\t'.join(map(str, null))))
    660          self.writeline('HMM    {0}'.format(
    661              '\t'.join(sorted(map(str, first_match.emission)))))
    662
    663          tran_types = 'M->M    M->I    M->D    I->M    I->I    D->M    D->D'.split()
    664          self.writeline('       {0}'.format(
    665              '\t'.join(tran_types + 'Neff    Neff_I    Neff_D'.split())))
    666
    667          self.write("       ")
    668          for tran_type in tran_types:
    669              source_statekind = Enum.parse(States, tran_type[0])
    670              target_statekind = Enum.parse(States, tran_type[3])
    671              if source_statekind == States.Match:
    672                  try:
    673                      self.write("{0:<7}\t".format(
    674                          int(hmm.start.transitions[target_statekind].probability)))
    675                  except TransitionNotFoundError:
    676                      self.write("*\t")
    677              else:
    678                  self.write("*\t")
    679          self.writeline('*\t' * 3)
    680
    681          for layer in hmm.layers:
    682
    683              self.write("{0} {1:<5}".format(layer.residue.type, layer.rank))
    684              for aa in sorted(layer[States.Match].emission):
    685                  emission = layer[States.Match].emission[aa]
    686                  if emission is None:
    687                      emission = '*'
    688                  else:
    689                      emission = int(emission)
    690                  self.write("{0:<7}\t".format(emission))
    691              self.writeline("{0}".format(layer.rank))
    692
    693              self.write("       ")
    694              for tran_type in tran_types:
    695                  source_statekind = Enum.parse(States, tran_type[0])
    696                  target_statekind = Enum.parse(States, tran_type[3])
    697
    698                  if target_statekind == States.Match and layer.rank == hmm.layers.last_index:
    699                      target_statekind = States.End
    700
    701                  try:
    702                      state = layer[source_statekind]
    703                      self.write("{0:<7}\t".format(
    704                          int(state.transitions[target_statekind].probability)))
    705                  except StateNotFoundError:
    706                      self.write("*\t")
    707
    708              for data in (layer.effective_matches, layer.effective_insertions,
    709                           layer.effective_deletions):
    710                  if data is None:
    711                      data = '*'
    712                  else:
    713                      data = int(data * abs(hmm.scale))
    714                  self.write("{0:<7}\t".format(data))
    715
    716              self.writeline("\n")
    717
    718          self.writeline('//')
    719
    720
    721 -class HHOutputParser(object):
    722      """
    723      Parser for HHsearch result (*.hhr) files.
    724
    725      @param alignments: if set to False, the parser will skip the
    726                         alignments section of the file
    727      @type alignments: bool
    728      """
    729
    730 -    def __init__(self, alignments=True):
    731
    732          self._alignments = True
    733          self.alignments = alignments
    734
    735 -    def __repr__(self):
    736          return "<HHsearch Result Parser>"
    737
    738      @property
    739 -    def alignments(self):
    740          """
    741          True if hit alignments will be parsed
    742          @rtype: bool
    743          """
    744          return self._alignments
    745      @alignments.setter
    746 -    def alignments(self, value):
    747          self._alignments = bool(value)
    748
    749 -    def parse_file(self, hhr_file, header_only=False):
    750          """
    751          Parse all hits from this HHpred result file.
    752
    753          @param hhr_file: input HHR file name
    754          @type hhr_file: str
    755
    756          @return: parsed hits
    757          @rtype: HHpredHitList
    758
    759          @raise HHOutputFormatError: if the hhr file is corrupt
    760          """
    761
    762          with open(os.path.expanduser(hhr_file)) as stream:
    763              return self._parse(stream, header_only)
    764
    765 -    def parse_string(self, output, header_only=False):
    766          """
    767          Get all hits from an C{output} string.
    768
    769          @param output: HHpred standard output
    770          @type output: str
    771
    772          @return: parsed hits
    773          @rtype: HHpredHitList
    774
    775          @raise HHOutputFormatError: if the output is corrupt
    776          """
    777          stream = csb.io.MemoryStream()
    778          stream.write(output)
    779          stream.seek(0)
    780
    781          return self._parse(stream, header_only)
    782
    783 -    def _parse(self, stream, header_only):
    784
    785          qlen = None
    786          in_hits = False
    787          in_alis = False
    788          has_alis = False
    789          c_rank = 0
    790          header = {}
    791          hits = {}
    792          alis = {}
    793
    794          for line in stream:
    795
    796              if not in_hits and not in_alis:
    797
    798                  if line.replace(' ', '').startswith('NoHitProbE-value'):
    799                      in_hits = True
    800                      continue
    801                  elif line.strip() == '':
    802                      continue
    803                  else:  # parse header data (stuff above the hits table)
    804                      columns = line.strip().split(None, 1)
    805                      if len(columns) == 2:
    806
    807                          identifier, data = columns
    808                          if identifier in ('Query', 'Command'):
    809                              data = data.strip()
    810                          elif identifier == 'Neff':
    811                              data = float(data)
    812                          elif identifier in ('Searched_HMMs', 'Match_columns'):
    813                              data = int(data)
    814
    815                          header[identifier] = data
    816
    817                          if identifier == 'Match_columns':
    818                              qlen = data
    819
    820              if in_hits and not header_only:
    821                  if not line.strip():  # suboptimal way to handle block switch
    822                      in_hits = False
    823                      in_alis = True
    824                      if self.alignments:
    825                          continue
    826                      else:
    827                          break
    828                  elif line.strip() == 'Done':
    829                      in_hits = False
    830                      in_alis = False
    831                      break
    832
    833                  description = line[:34].split()
    834                  rank = int(description[0])
    835                  id = description[1]
    836
    837                  pos = line[85:94].strip()
    838                  start, end = map(int, pos.split('-'))
    839
    840                  qpos = line[75:84].strip()
    841                  qstart, qend = map(int, qpos.split('-'))
    842
    843                  probability = float(line[35:40]) / 100.0
    844
    845                  hit = HHpredHit(rank, id, start, end, qstart, qend, probability, qlen)
    846
    847                  hit.evalue = float(line[41:48])
    848                  hit.pvalue = float(line[49:56])
    849                  hit.score = float(line[57:63])
    850                  hit.ss_score = float(line[64:69])
    851
    852                  hit.slength = int(line[94:].replace('(', '').replace(')', ''))
    853
    854                  hits[hit.rank] = hit
    855                  alis[hit.rank] = {'q': [], 's': []}
    856
    857              elif in_alis and not header_only:
    858                  if line.startswith('Done'):
    859                      in_alis = False
    860                      break
    861
    862                  elif line.startswith('No '):
    863                      c_rank = int(line[3:])
    864                      if c_rank not in hits:
    865                          raise HHOutputFormatError('Alignment {0}. refers to a non-existing hit'.format(c_rank))
    866
    867                  elif line.startswith('>'):
    868                      hits[c_rank].name = line[1:].strip()
    869
    870                  elif line.startswith('Probab='):
    871                      for pair in line.split():
    872                          key, value = pair.split('=')
    873                          if key == 'Identities':
    874                              hits[c_rank].identity = float(
    875                                  value.replace('%', ''))
    876                          elif key == 'Similarity':
    877                              hits[c_rank].similarity = float(value)
    878                          elif key == 'Sum_probs':
    879                              hits[c_rank].prob_sum = float(value)
    880
    881                  elif line.startswith('Q ') and not line[:11].rstrip() in ('Q Consensus', 'Q ss_pred','Q ss_conf', 'Q ss_dssp'):
    882                      for residue in line[22:]:
    883                          if residue.isspace() or residue.isdigit():
    884                              break
    885                          else:
    886                              alis[c_rank]['q'].append(residue)
    887                              has_alis = True
    888
    889                  elif line.startswith('T ') and not line[:11].rstrip() in ('T Consensus', 'T ss_pred','T ss_conf', 'T ss_dssp'):
    890                      for residue in line[22:]:
    891                          if residue.isspace() or residue.isdigit():
    892                              break
    893                          else:
    894                              alis[c_rank]['s'].append(residue)
    895
    896          if self.alignments and has_alis:
    897              for rank in alis:
    898                  try:
    899                      hits[rank].add_alignment(alis[rank]['q'], alis[rank]['s'])
    900
    901                  except (KeyError, ValueError) as er:
    902                      raise HHOutputFormatError('Corrupt alignment at hit No {0}.\n {1}'.format(rank, er))
    903
    904          del alis
    905
    906          hits = HHpredHitList(hits.values())
    907
    908          hits.sort()
    909
    910          ## add data obtained from the header to the HHpredHitList
    911          for identifier, data in header.items():
    912              if identifier == 'Query':
    913                  hits.query_name = data
    914              elif identifier == 'Match_columns':
    915                  hits.match_columns = data
    916              elif identifier == 'No_of_seqs':
    917                  hits.no_of_seqs = data
    918              elif identifier == 'Neff':
    919                  hits.neff = data
    920              elif identifier == 'Searched_HMMs':
    921                  hits.searched_hmms = data
    922              elif identifier == 'Date':
    923                  hits.date = data
    924              elif identifier == 'Command':
    925                  hits.command = data
    926
    927          return hits
    928
    929  HHpredOutputParser = HHOutputParser
    930