Source code for indra.sources.hprd.processor

import logging
from numpy import nan
from collections import Counter
from protmapper import uniprot_client, ProtMapper
from indra.statements import *
from indra.databases import hgnc_client

logger = logging.getLogger(__name__)

# Map of HPRD PTM types to INDRA Statement types
_ptm_map = {
    'ADP-Ribosylation': None,
    'Acetylation': Acetylation,
    'Alkylation': None,
    'Amidation': None,
    'Carboxylation': None,
    'Deacetylation': Deacetylation,
    'Deacylation': None,
    'Deglycosylation': Deglycosylation,
    'Demethylation': Demethylation,
    'Dephosphorylation': Dephosphorylation,
    'Disulfide Bridge': None,
    'Farnesylation': Farnesylation,
    'Glucuronosyl transfer': None,
    'Glutathionylation': None,
    'Glycation': None,
    'Glycosyl phosphatidyl inositol GPI': None,
    'Glycosylation': Glycosylation,
    'Hydroxylation': Hydroxylation,
    'Methylation': Methylation,
    'Myristoylation': Myristoylation,
    'Neddylation': None,
    'Nitration': None,
    'Palmitoylation': Palmitoylation,
    'Phosphorylation': Phosphorylation,
    'Prenylation': None,
    'Proteolytic Cleavage': None,
    'S-Nitrosylation': None,
    'Sulfation': None,
    'Sumoylation': Sumoylation,
    'Transglutamination': None,
    'Ubiquitination': Ubiquitination,

[docs]class HprdProcessor(object): """Get INDRA Statements from HPRD data. See documentation for `indra.sources.hprd.api.process_flat_files.` Parameters ---------- id_df : pandas.DataFrame DataFrame loaded from the HPRD_ID_MAPPINGS.txt file. cplx_df : pandas.DataFrame DataFrame loaded from the PROTEIN_COMPLEXES.txt file. ptm_df : pandas.DataFrame DataFrame loaded from the POST_TRANSLATIONAL_MODIFICATIONS.txt file. ppi_df : pandas.DataFrame DataFrame loaded from the BINARY_PROTEIN_PROTEIN_INTERACTIONS.txt file. seq_dict : dict Dictionary mapping RefSeq IDs to protein sequences, loaded from the PROTEIN_SEQUENCES.txt file. motif_window : int Number of flanking amino acids to include on each side of the PTM target residue in the 'site_motif' annotations field of the Evidence for Modification Statements. Default is 7. Attributes ---------- statements : list of INDRA Statements INDRA Statements (Modifications and Complexes) produced from the HPRD content. id_df : pandas.DataFrame DataFrame loaded from HPRD_ID_MAPPINGS.txt file. seq_dict : Dictionary mapping RefSeq IDs to protein sequences, loaded from the PROTEIN_SEQUENCES.txt file. no_hgnc_for_egid: collections.Counter Counter listing Entrez gene IDs reference in the HPRD content that could not be mapped to a current HGNC ID, along with their frequency. no_up_for_hgnc : collections.Counter Counter with tuples of form (entrez_id, hgnc_symbol, hgnc_id) where the HGNC ID could not be mapped to a Uniprot ID, along with their frequency. no_up_for_refseq : collections.Counter Counter of RefSeq protein IDs that could not be mapped to any Uniprot ID, along with frequency. many_ups_for_refseq : collections.Counter Counter of RefSeq protein IDs that yielded more than one matching Uniprot ID. Note that in these cases, the Uniprot ID obtained from HGNC is used. invalid_site_pos : list of tuples List of tuples of form (refseq_id, residue, position) indicating sites of post translational modifications where the protein sequences provided by HPRD did not contain the given residue at the given position. off_by_one : list of tuples The subset of sites contained in `invalid_site_pos` where the given residue can be found at position+1 in the HPRD protein sequence, suggesting an off-by-one error due to numbering based on the protein with initial methionine cleaved. Note that no mapping is performed by the processor. motif_window : int Number of flanking amino acids to include on each side of the PTM target residue in the 'site_motif' annotations field of the Evidence for Modification Statements. Default is 7. """ def __init__(self, id_df, cplx_df=None, ptm_df=None, ppi_df=None, seq_dict=None, motif_window=7): if cplx_df is None and ptm_df is None and ppi_df is None: raise ValueError('At least one of cplx_df, ptm_df, or ppi_df must ' 'be specified.') if ptm_df is not None and not seq_dict: raise ValueError('If ptm_df is given, seq_dict must also be given.') self.statements = [] self.id_df = id_df self.seq_dict = seq_dict self.motif_window = motif_window # Keep track of the ID mapping issues encountered self.no_hgnc_for_egid = [] self.no_up_for_hgnc = [] self.no_up_for_refseq = [] self.many_ups_for_refseq = [] self.invalid_site_pos = [] self.off_by_one = [] # Do the actual processing if cplx_df is not None: self.get_complexes(cplx_df) if ptm_df is not None: self.get_ptms(ptm_df) if ppi_df is not None: self.get_ppis(ppi_df) # Tabulate IDs causing issues self.no_hgnc_for_egid = Counter(self.no_hgnc_for_egid) self.no_up_for_hgnc = Counter(self.no_up_for_hgnc) self.no_up_for_refseq = Counter(self.no_up_for_refseq) self.many_ups_for_refseq = Counter(self.many_ups_for_refseq)'For information on problematic entries encountered while ' 'processing, see HprdProcessor attributes: ' 'no_hgnc_for_egid, no_up_for_hgnc, no_up_for_refseq, ' 'many_ups_for_refseq, invalid_site_pos, and off_by_one.')
[docs] def get_complexes(self, cplx_df): """Generate Complex Statements from the HPRD protein complexes data. Parameters ---------- cplx_df : pandas.DataFrame DataFrame loaded from the PROTEIN_COMPLEXES.txt file. """ # Group the agents for the complex'Processing complexes...') for cplx_id, this_cplx in cplx_df.groupby('CPLX_ID'): agents = [] for hprd_id in this_cplx.HPRD_ID: ag = self._make_agent(hprd_id) if ag is not None: agents.append(ag) # Make sure we got some agents! if not agents: continue # Get evidence info from first member of complex row0 = this_cplx.iloc[0] isoform_id = '%s_1' % row0.HPRD_ID ev_list = self._get_evidence(row0.HPRD_ID, isoform_id, row0.PMIDS, row0.EVIDENCE, 'interactions') stmt = Complex(agents, evidence=ev_list) self.statements.append(stmt)
[docs] def get_ptms(self, ptm_df): """Generate Modification statements from the HPRD PTM data. Parameters ---------- ptm_df : pandas.DataFrame DataFrame loaded from the POST_TRANSLATIONAL_MODIFICATIONS.txt file. """'Processing PTMs...') # Iterate over the rows of the dataframe for ix, row in ptm_df.iterrows(): # Check the modification type; if we can't make an INDRA statement # for it, then skip it ptm_class = _ptm_map[row['MOD_TYPE']] if ptm_class is None: continue # Use the Refseq protein ID for the substrate to make sure that # we get the right Uniprot ID for the isoform sub_ag = self._make_agent(row['HPRD_ID'], refseq_id=row['REFSEQ_PROTEIN']) # If we couldn't get the substrate, skip the statement if sub_ag is None: continue enz_id = _nan_to_none(row['ENZ_HPRD_ID']) enz_ag = self._make_agent(enz_id) res = _nan_to_none(row['RESIDUE']) pos = _nan_to_none(row['POSITION']) if pos is not None and ';' in pos: pos, dash = pos.split(';') assert dash == '-' # As a fallback for later site mapping, we also get the protein # sequence information in case there was a problem with the # RefSeq->Uniprot mapping assert res assert pos motif_dict = self._get_seq_motif(row['REFSEQ_PROTEIN'], res, pos) # Get evidence ev_list = self._get_evidence( row['HPRD_ID'], row['HPRD_ISOFORM'], row['PMIDS'], row['EVIDENCE'], 'ptms', motif_dict) stmt = ptm_class(enz_ag, sub_ag, res, pos, evidence=ev_list) self.statements.append(stmt)
[docs] def get_ppis(self, ppi_df): """Generate Complex Statements from the HPRD PPI data. Parameters ---------- ppi_df : pandas.DataFrame DataFrame loaded from the BINARY_PROTEIN_PROTEIN_INTERACTIONS.txt file. """'Processing PPIs...') for ix, row in ppi_df.iterrows(): hprd_id_a = row['HPRD_ID_A'].strip() hprd_id_b = row['HPRD_ID_B'].strip() agA = self._make_agent(hprd_id_a) agB = self._make_agent(hprd_id_b) # If don't get valid agents for both, skip this PPI if agA is None or agB is None: continue isoform_id = '%s_1' % hprd_id_a ev_list = self._get_evidence( hprd_id_a, isoform_id, row['PMIDS'], row['EVIDENCE'], 'interactions') stmt = Complex([agA, agB], evidence=ev_list) self.statements.append(stmt)
def _make_agent(self, hprd_id, refseq_id=None): if hprd_id is None or hprd_id is nan: return None # Get the basic info (HGNC name/symbol, Entrez ID) from the # ID mappings dataframe try: egid = self.id_df.loc[hprd_id].EGID except KeyError:'HPRD ID %s not found in mappings table.' % hprd_id) return None if not egid:'No Entrez ID for HPRD ID %s' % hprd_id) return None # Get the HGNC ID hgnc_id = hgnc_client.get_hgnc_from_entrez(egid) # If we couldn't get an HGNC ID for the Entrez ID, this means that # the Entrez ID has been discontinued or replaced. if not hgnc_id: self.no_hgnc_for_egid.append(egid) return None # Get the (possibly updated) HGNC Symbol hgnc_name = hgnc_client.get_hgnc_name(hgnc_id) assert hgnc_name is not None # See if we can get a Uniprot ID from the HGNC symbol--if there is # a RefSeq ID we wil also try to use it to get an isoform specific # UP ID, but we will have this one to fall back on. But if we can't # get one here, then we skip the Statement up_id_from_hgnc = hgnc_client.get_uniprot_id(hgnc_id) if not up_id_from_hgnc: self.no_up_for_hgnc.append((egid, hgnc_name, hgnc_id)) return None # If we have provided the RefSeq ID, it's because we need to make # sure that we are getting the right isoform-specific ID (for sequence # positions of PTMs). Here we try to get the Uniprot ID from the # Refseq->UP mappings in the protmapper.uniprot_client. if refseq_id is not None: # Get the Uniprot IDs from the uniprot client up_ids = uniprot_client.get_ids_from_refseq(refseq_id, reviewed_only=True) # Nothing for this RefSeq ID (quite likely because the RefSeq ID # is obsolete; take the UP ID from HGNC if len(up_ids) == 0: self.no_up_for_refseq.append(refseq_id) up_id = up_id_from_hgnc # More than one reviewed entry--no thanks, we'll take the one from # HGNC instead elif len(up_ids) > 1: self.many_ups_for_refseq.append(refseq_id) up_id = up_id_from_hgnc # We got a unique, reviewed UP entry for the RefSeq ID else: up_id = up_ids[0] # If it's the canonical isoform, strip off the '-1' if up_id.endswith('-1'): up_id = up_id.split('-')[0] # For completeness, get the Refseq ID from the HPRD ID table else: refseq_id = self.id_df.loc[hprd_id].REFSEQ_PROTEIN up_id = up_id_from_hgnc # Make db_refs, return Agent db_refs = {'HGNC': hgnc_id, 'UP': up_id, 'EGID': egid, 'REFSEQ_PROT': refseq_id} return Agent(hgnc_name, db_refs=db_refs) def _get_evidence(self, hprd_id, isoform_id, pmid_str, evidence_type, info_type, motif_dict=None): pmids = pmid_str.split(',') ev_list = [] if motif_dict is None: motif_dict = {} for pmid in pmids: ev_type_list = [] if evidence_type is nan \ else evidence_type.split(';') # Add the annotations with the site motif info if relevant annotations = {'evidence': ev_type_list} annotations.update(motif_dict) ev = Evidence(source_api='hprd', source_id=_hprd_url(hprd_id, isoform_id, info_type), pmid=pmid, annotations=annotations) ev_list.append(ev) return ev_list def _get_seq_motif(self, refseq_id, residue, pos_str): seq = self.seq_dict[refseq_id] pos_1ix = int(pos_str) pos_0ix = pos_1ix - 1 if seq[pos_0ix] != residue: self.invalid_site_pos.append((refseq_id, residue, pos_str)) if seq[pos_0ix + 1] == residue: self.off_by_one.append((refseq_id, residue, pos_str)) motif, respos = \ ProtMapper.motif_from_position_seq(seq, pos_1ix + 1, self.motif_window) return {'site_motif': {'motif': motif, 'respos': respos, 'off_by_one': True}} else: return {} else: # The index of the residue at the start of the window motif, respos = ProtMapper.motif_from_position_seq(seq, pos_1ix, self.motif_window) return {'site_motif': {'motif': motif, 'respos': respos, 'off_by_one': False}}
def _hprd_url(hprd_id, isoform_id, info_type): if info_type == 'interactions': return ('' '&isoform_name=Isoform_1' % (hprd_id, isoform_id)) elif info_type == 'ptms': isoform_num = isoform_id.split('_')[1] return ('' '&isoform_name=Isoform_%s' % (hprd_id, isoform_id, isoform_num)) else: raise ValueError('info_type must be either interactions or ptms.') def _nan_to_none(val): return None if val is nan else val