Getting started with INDRA¶
Importing INDRA and its modules¶
INDRA can be imported and used in a Python script or interactively in a Python shell. Note that similar to some other packages (e.g scipy), INDRA doesn’t automatically import all its submodules, so import indra is not enough to access its submodules. Rather, one has to explicitly import each submodule that is needed. For example to access the BEL API, one has to
from indra.sources import bel
Similarly, each model output assembler has its own submodule under indra.assemblers with the assembler class accessible at the submodule level, so they can be imported as, for instance,
from indra.assemblers.pysb import PysbAssembler
To get a detailed overview of INDRA’s submodule structure, take a look at the INDRA modules reference.
Basic usage examples¶
Here we show some basic usage examples of the submodules of INDRA. More complex usage examples are shown in the Tutorials section.
Reading a sentence with TRIPS¶
In this example, we read a sentence via INDRA’s TRIPS submodule to produce an INDRA Statement.
from indra.sources import trips sentence = 'MAP2K1 phosphorylates MAPK3 at Thr-202 and Tyr-204' trips_processor = trips.process_text(sentence)
The trips_processor object has a statements attribute which contains a list of INDRA Statements extracted from the sentence.
Reading a PubMed Central article with REACH¶
from indra.sources import reach reach_processor = reach.process_pmc('3717945')
The reach_processor object has a statements attribute which contains a list of INDRA Statements extracted from the paper.
Getting the neighborhood of proteins from the BEL Large Corpus¶
In this example, we search the neighborhood of the KRAS and BRAF proteins in the BEL Large Corpus.
from indra.sources import bel bel_processor = bel.process_pybel_neighborhood(['KRAS', 'BRAF'])
The bel_processor object has a statements attribute which contains a list of INDRA Statements extracted from the queried neighborhood.
Constructing INDRA Statements manually¶
It is possible to construct INDRA Statements manually or in scripts. The following is a basic example in which we instantiate a Phosphorylation Statement between BRAF and MAP2K1.
from indra.statements import Phosphorylation, Agent braf = Agent('BRAF') map2k1 = Agent('MAP2K1') stmt = Phosphorylation(braf, map2k1)
Assembling a PySB model and exporting to SBML¶
In this example, assume that we have already collected a list of INDRA Statements from any of the input sources and that this list is called stmts. We will instantiate a PysbAssembler, which produces a PySB model from INDRA Statements.
from indra.assemblers.pysb import PysbAssembler pa = PysbAssembler() pa.add_statements(stmts) model = pa.make_model()
Here the model variable is a PySB Model object representing a rule-based executable model, which can be further manipulated, simulated, saved and exported to other formats.
For instance, exporting the model to SBML format can be done as
sbml_model = pa.export_model('sbml')
which gives an SBML model string in the sbml_model variable, or as
which writes the SBML model into the model.sbml file. Other formats for export that are supported include BNGL, Kappa and Matlab. For a full list, see the PySB export module.
Exporting Statements as an IndraNet Graph¶
In this example we again assume that there already exists a variable called stmts, containing a list of statements. We will import the IndraNetAssembler that produces an IndraNet object, which is a networkx MultiDiGraph representations of the statements, each edge representing a statement and each node being an agent.
from indra.assemblers.indranet import IndraNetAssembler indranet_assembler = IndraNetAssembler(statements=stmts) indranet = indranet_assembler.make_model()
The indranet object is an instance of a child class of a networkx graph object, making all networkx graph methods available for the indranet object. Each edge in the has an edge dictionary with meta data from the statement.
The indranet graph has methods to map it to other graph types. Here we export it to a signed graph which is represents directed edges with positive or negative polarity signs:
signed_graph = indranet.to_signed_graph()
Read more about the IndraNetAssembler in the documentation.
For a longer example of using INDRA in an end-to-end pipeline, from getting content from different sources to assembling different output models, see the tutorial “Assembling everything known about a particular gene”.
More tutorials are available in the tutorials section.