Source code for indra.sources.tas.processor

__all__ = ['TasProcessor']

import logging
from indra.statements import Inhibition, Agent, Evidence
from indra.statements.validate import assert_valid_db_refs
from indra.ontology.standardize import standardize_name_db_refs, \
from indra.databases import hgnc_client, chembl_client, lincs_client

logger = logging.getLogger(__name__)

CLASS_MAP = {'1': 'Kd < 100nM', '2': '100nM < Kd < 1uM',
             '3': '1uM < Kd < 10uM', '10': 'Kd > 10uM'}

lincs_client_obj = lincs_client.LincsClient()

[docs]class TasProcessor(object): """A processor for the Target Affinity Spectrum data table.""" def __init__(self, data, affinity_class_limit=2, named_only=False, standardized_only=False): self._data = data self.affinity_class_limit = affinity_class_limit self.named_only = named_only self.standardized_only = standardized_only self.statements = [] for row in data: # Skip rows that are above the affinity class limit if int(row['tas']) > affinity_class_limit: continue self._process_row(row) return def _process_row(self, row): drugs = self._extract_drugs(row['compound_ids'], row['lspci_id']) prot = self._extract_protein(row['entrez_gene_symbol'], row['entrez_gene_id']) evidences = self._make_evidences(row['tas'], row['references']) # NOTE: there are several entries in this data set that refer to # non-human Entrez genes, e.g. # # We skip these for now because resources for Entrez-based # mappings for non-human genes are not integrated, and would cause # pre-assembly issues. if 'HGNC' not in prot.db_refs: return for drug in drugs: self.statements.append(Inhibition(drug, prot, evidence=evidences)) def _extract_drugs(self, compound_ids, lspci_id): drugs = [] for id_ in compound_ids.split('|'): db_refs = {'LSPCI': lspci_id} if id_.startswith('CHEMBL'): db_refs['CHEMBL'] = id_ elif id_.startswith('HMSL'): db_refs['HMS-LINCS'] = id_.split('HMSL')[1] else: logger.warning('Unhandled ID type: %s' % id_) # Name standardization finds correct names but because # ChEMBL is incomplete as a local resource, we don't # universally standardize its names, instead, we look # it up explicitly when necessary. name, db_refs = standardize_name_db_refs(db_refs) if name is None: # This is one way to detect that the drug could not be # standardized beyond just its name so in the # standardized_only condition, we skip this drug if self.standardized_only: continue elif 'HMS-LINCS' in db_refs: name = \ lincs_client_obj.get_small_molecule_name( db_refs['HMS-LINCS']) elif 'CHEMBL' in db_refs: name = chembl_client.get_chembl_name(db_refs['CHEMBL']) # If name is still None, we just use the ID as the name if name is None: # With the named_only restriction, we skip drugs without # a proper name. if self.named_only: continue name = id_ assert_valid_db_refs(db_refs) drugs.append(Agent(name, db_refs=db_refs)) drugs = list({agent.matches_key(): agent for agent in drugs}.values()) return drugs def _extract_protein(self, name, gene_id): db_refs = {'EGID': gene_id} hgnc_id = hgnc_client.get_hgnc_from_entrez(gene_id) if hgnc_id is not None: db_refs['HGNC'] = hgnc_id return get_standard_agent(name, db_refs=db_refs) def _make_evidences(self, class_min, references): evidences = [] for reference in references.split('|'): pmid, source_id, text_refs = None, None, None annotations = {'class_min': CLASS_MAP[class_min]} ref, id_ = reference.split(':') if ref == 'pubmed': pmid = id_ text_refs = {'PMID': pmid} elif ref == 'doi': text_refs = {'DOI': id_} else: source_id = reference ev = Evidence(source_api='tas', source_id=source_id, pmid=pmid, annotations=annotations, epistemics={'direct': True}, text_refs=text_refs) evidences.append(ev) return evidences