INDRA (Integrated Network and Dynamical Reasoning Assembler) generates executable models of pathway dynamics from natural language (using the TRIPS and REACH reading systems), and BioPAX and BEL sources (including the Pathway Commons database and NDEx).
Reference: From word models to executable models of signal transduction using automated assembly, Molecular Systems Biology (2017)
Documentation is available at http://indra.readthedocs.io.
For detailed installation instructions, see the documentation.
INDRA works with both Python 2 and 3 (tested with 2.7 and 3.5).
The preferred way to install INDRA is by pointing pip to the source repository as
$ pip install git+https://github.com/sorgerlab/indra.git
or by cloning the repository and then using pip to install the package as
$ git clone https://github.com/sorgerlab/indra.git $ cd indra $ pip install .
You can also install INDRA by cloning this repository and running setup.py as
$ git clone https://github.com/sorgerlab/indra.git $ cd indra $ python setup.py install
Releases of INDRA are also available on PyPI, you can install the latest release as
$ pip install indra
However, releases will usually be behind the latest code available in this repository.
INDRA depends on a few standard Python packages (e.g. rdflib, requests, pysb). These packages are installed by either setup method (using pip or running setup.py install). For certain modules and use cases, other dependencies may be needed, which are described in detail in the documentation.
In this example INDRA assembles a PySB model from the natural language description of a mechanism via the TRIPS reading web service.
from indra.sources import trips from indra.assemblers import PysbAssembler pa = PysbAssembler() # Process a natural language description of a mechanism trips_processor = trips.process_text('MEK2 phosphorylates ERK1 at Thr-202 and Tyr-204') # Collect extracted mechanisms in PysbAssembler pa.add_statements(trips_processor.statements) # Assemble the model model = pa.make_model(policies='two_step')
INDRA also provides an interface for the REACH natural language processor. In this example, a full paper from PubMed Central is processed. The paper’s PMC ID is PMC3717945.
from indra.sources import reach # Process the neighborhood of BRAF and MAP2K1 reach_processor = reach.process_pmc('3717945') # At this point, reach_processor.statements contains a list of INDRA statements # extracted from the PMC paper.
Next we look at an example of reading the 10 most recent PubMed abstracts on BRAF and collecting the results in INDRA statements.
from indra.sources import reach from indra.literature import pubmed_client # Search for 10 most recent abstracts in PubMed on 'BRAF' pmids = pubmed_client.get_ids('BRAF', retmax=10) all_statements =  for pmid in pmids: abs = pubmed_client.get_abstract(pmid) if abs is not None: reach_processor = reach.process_text(abs) if reach_processor is not None: all_statements += reach_processor.statements # At this point, the all_statements list contains all the statements # extracted from the 10 abstracts.
The next example shows querying the BEL large corpus network through NDEx for a neighborhood of a given list of proteins using their HGNC gene names.
from indra.sources import bel # Process the neighborhood of BRAF and MAP2K1 bel_processor = bel.process_ndex_neighborhood(['BRAF', 'MAP2K1']) # At this point, bel_processor.statements contains a list of INDRA statements # extracted from the neihborhood query.
Next, we look at an example of querying the Pathway Commons database for paths between two lists of proteins. Note: see installation notes above for installing jnius, which is required for using the BioPAX API of INDRA.
from indra.sources import biopax # Process the neighborhood of BRAF and MAP2K1 biopax_processor = biopax.process_pc_pathsfromto(['BRAF', 'RAF1'], ['MAP2K1', 'MAP2K2']) # At this point, biopax_processor.statements contains a list of INDRA # Statements extracted from the paths-from-to query.