예제 #1
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def download_family(name: str) -> str:
    return prody.fetchPfamMSA(name,
                              format='fasta',
                              outname=f'fs/{name}.fasta',
                              timeout=10)
예제 #2
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def evol_fetch(acc, **kwargs):

    import prody
    prody.fetchPfamMSA(acc, **kwargs)
예제 #3
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파일: Uniprot.py 프로젝트: yaz62/rhapsody
 def calcEvolProperties(self,
                        resid='all',
                        refresh=False,
                        folder=None,
                        max_cols=None,
                        max_seqs=25000,
                        **kwargs):
     ''' Computes Evol properties, i.e. Shannon entropy, Mutual
     Information and Direct Information, from Pfam Multiple
     Sequence Alignments, for a given residue.
     '''
     assert type(refresh) is bool
     # recover Pfam mapping (if not found already)
     self._searchPfam(refresh=refresh)
     if resid == 'all':
         PF_list = self.Pfam.keys()
     else:
         # get list of Pfam domains containing resid
         PF_list = [
             k for k in self.Pfam if any([
                 resid >= int(segment['start'])
                 and resid <= int(segment['end'])
                 for segment in self.Pfam[k]['locations']
             ])
         ]
         if len(PF_list) == 0:
             raise RuntimeError(
                 'No Pfam domain for resid {}.'.format(resid))
         if len(PF_list) > 1:
             LOGGER.warn('Residue {} is found in multiple '.format(resid) + \
                         '({}) Pfam domains.'.format(len(PF_list)))
     if folder is None:
         folder = SETTINGS.get('rhapsody_local_folder', './')
     # iterate over Pfam families
     for PF in PF_list:
         d = self.Pfam[PF]
         # skip if properties are pre-computed
         if not refresh and d.get('mapping') is not None:
             continue
         d['mapping'] = None
         d['ref_MSA'] = None
         d['entropy'] = np.nan
         d['MutInfo'] = np.nan
         d['DirInfo'] = np.nan
         try:
             LOGGER.info('Processing {}...'.format(PF))
             # fetch & parse MSA
             #               fname = PF + '_full.sth'
             #               fullname = os.path.join(folder, fname)
             #               if not os.path.isfile(fullname):
             #                   f = fetchPfamMSA(PF)
             #                   os.rename(f, fullname)
             #               msa = parseMSA(fullname, **kwargs)
             # fetch & parse MSA without saving downloaded MSA
             f = fetchPfamMSA(PF)
             msa = parseMSA(f, **kwargs)
             os.remove(f)
             # slice MSA to match all segments of the Uniprot sequence
             sliced_msa, indexes = self._sliceMSA(msa)
             #               if max_cols is not None and sliced_msa.numResidues() > max_cols:
             #                   raise Exception('Unable to compute DI: MSA has ' +\
             #                                   'too many columns (max: {}).'.format(max_cols))
             # get mapping between Uniprot sequence and Pfam domain
             d['mapping'] = self._mapUniprot2Pfam(PF, sliced_msa, indexes)
         except Exception as e:
             LOGGER.warn('{}: {}'.format(PF, e))
             d['mapping'] = str(e)
             continue
         try:
             # refine MSA ('seqid' param. is set as in PolyPhen-2)
             rowocc = 0.6
             while True:
                 sliced_msa = refineMSA(sliced_msa, rowocc=rowocc)
                 rowocc += 0.02
                 if sliced_msa.numSequences() <= max_seqs or rowocc >= 1:
                     break
             ref_msa = refineMSA(sliced_msa, seqid=0.94, **kwargs)
             d['ref_MSA'] = ref_msa
             # compute evolutionary properties
             d['entropy'] = calcShannonEntropy(ref_msa)
             d['MutInfo'] = buildMutinfoMatrix(ref_msa)
             # d['DirInfo'] = buildDirectInfoMatrix(ref_msa)
         except Exception as e:
             LOGGER.warn('{}: {}'.format(PF, e))
     return {k: self.Pfam[k] for k in PF_list}
예제 #4
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def evol_fetch(acc, **kwargs):

    import prody

    prody.fetchPfamMSA(acc, **kwargs)