Assembling everything known about a particular gene

Assume you are interested in collecting all mechanisms that a particular gene is involved in. Using INDRA, it is possible to collect everything curated about the gene in pathway databases and then read all the accessible literature discussing the gene of interest. This knowledge is aggregated as a set of INDRA Statements which can then be assembled into several different model and network formats and possibly shared online.

For the sake of this example, assume that the gene of interest is H2AX.

It is important to use the standard HGNC gene symbol of the gene throughout the example (this information is available on or - abritrary synonyms will not work!

Collect mechanisms from PathwayCommons and the BEL Large Corpus

We first collect Statements from the PathwayCommons database via INDRA’s BioPAX API and then collect Statements from the BEL Large Corpus via INDRA’s BEL API.

from import GeneNetwork

gn = GeneNetwork(['H2AX'])
biopax_stmts = gn.get_biopax_stmts()
bel_stmts = gn.get_bel_stmts()

at this point biopax_stmts and bel_stmts are two lists of INDRA Statements.

Collect a list of publications that discuss the gene of interest

We next use INDRA’s literature client to find PubMed IDs (PMIDs) that discuss the gene of interest. To find articles that are annotated with the given gene, INDRA first looks up the Entrez ID corresponding to the gene name and then finds associated publications.

from indra import literature

pmids = literature.pubmed_client.get_ids_for_gene('H2AX')

The variable pmids now contains a list of PMIDs associated with the gene.

Get the abstracts corresponding to the publications

Next we use INDRA’s literature client to fetch the abstracts corresponding to the PMIDs we have just collected. The client also returns other content types, like xml, for full text (if available). Here we cut the list of PMIDs short to just the first 10 IDs that contain abstracts to make the processing faster.

from indra import literature

paper_contents = {}
for pmid in pmids:
    content, content_type = literature.get_full_text(pmid, 'pmid')
    if content_type == 'abstract':
        paper_contents[pmid] = content
    if len(paper_contents) == 10:

We now have a dictionary called paper_contents which stores the content for each PMID we looked up. While the abstracts are in plain text format, some content is sometimes returned in different either PMC NXML or Elsevier XML format. To process XML from different sources, some example are: INDRA Reach API or the INDRA Elsevier client.

Read the content of the publications

We next run the REACH reading system on the publications. Here we assume
that the REACH web service is running locally and is available at http://localhost:8080 (the default web service endpoints for processing text and nxml are available as importable variables e.g., local_text_url. To get started wtih this, see method 1 listed in <INDRA Reach API documentation.
from indra.sources import reach

literature_stmts = []
for pmid, content in paper_contents.items():
    rp = reach.process_text(content, url=reach.local_text_url)
    literature_stmts += rp.statements
print('Got %d statements' % len(literature_stmts))

The list literature_stmts now contains the results of all the statements that were read.

Combine all statements and run pre-assembly

from import assemble_corpus as ac

stmts = biopax_stmts + bel_stmts + literature_stmts

stmts = ac.map_grounding(stmts)
stmts = ac.map_sequence(stmts)
stmts = ac.run_preassembly(stmts)

At this point stmts contains a list of Statements with grounding, having been mapped according to INDRA’s built in grounding map and disambiguation features, amino acid sites having been mapped, duplicates combined, and hierarchically subsumed variants of statements hidden. It is possible to run other assembly steps and filters on the results such as to keep only human genes, remove Statements with ungrounded genes, or to keep only certain types of interactions. You can find more assembly steps that can be included in your pipeline in the Assemble Corpus documentation. You can also read more about the pre-assembly process in the preassembly module documentation and in the GitHub documentation

Assemble the statements into a network model

CX Network Model

We can assemble the statements into e.g., a CX network model:

from import CxAssembler
from indra.databases import ndex_client

cxa = CxAssembler(stmts)
cx_str = cxa.make_model()

We can now upload this network to the Network Data Exchange (NDEx).

ndex_cred = {'user': 'myusername', 'password': 'xxx'}
network_id = ndex_client.create_network(cx_str, ndex_cred)

IndraNet Model

Another network model that can assembled is the IndraNet graph which is a light-weight networkx derived object.

from indra.assemblers.indranet import IndraNetAssembler
indranet_assembler = IndraNetAssembler(statements=stmts)
indranet = indranet_assembler.make_model()

Since the IndraNet class is a child class of a networkx Graph, one can use networkx’s algorithms:

import networkx as nx
paths = nx.single_source_shortest_path(G=indranet, source='H2AX',

Executable PySB Model

An executable PySB model can be assembled with the PySB assembler:

from indra.assemblers.pysb import PysbAssembler
pysb = PysbAssembler(statements=stmts)
pysb_model = pysb.make_model()

Read more about PySB models in the PySB documentation and look into the natural language modeling tutorial which uses PySB models.

Read more about all assembly output formats in the README and in the module references.