INDRA documentation
INDRA (the Integrated Network and Dynamical Reasoning Assembler) assembles information about causal mechanisms into a common format that can be used to build several different kinds of predictive and explanatory models. INDRA was originally developed for molecular systems biology and is currently being generalized to other domains.
In molecular biology, sources of mechanistic information include pathway databases, natural language descriptions of mechanisms by human curators, and findings extracted from the literature by text mining.
Mechanistic information from multiple sources is de-duplicated, standardized and assembled into sets of Statements with associated evidence. Sets of Statements can then be used to assemble both executable rule-based models (using PySB) and a variety of different types of network models.
- License and funding
- Installation
- Getting started with INDRA
- INDRA modules reference
- INDRA Statements (
indra.statements)- General information and statement types
- Agents (
indra.statements.agent) - Concepts (
indra.statements.concept) - Evidence (
indra.statements.evidence) - Context (
indra.statements.context) - Input/output, serialization (
indra.statements.io) - Validation (
indra.statements.validate) - Resource access (
indra.statements.resources) - Utils (
indra.statements.util)
- Processors for knowledge input (
indra.sources) - Database clients (
indra.databases)- identifiers.org mappings and URLs (
indra.databases.identifiers) - Bioregistry mappings and URLs (
indra.databases.bioregistry_client) - HGNC client (
indra.databases.hgnc_client) - UniProt client (
indra.databases.uniprot_client) - ChEBI client (
indra.databases.chebi_client) - Cell type context client (
indra.databases.context_client) - NDEx client (
indra.databases.ndex_client) - cBio portal client (
indra.databases.cbio_client) - ChEMBL client (
indra.databases.chembl_client) - LINCS client (
indra.databases.lincs_client) - MeSH client (
indra.databases.mesh_client) - GO client (
indra.databases.go_client) - PubChem client (
indra.databases.pubchem_client) - miRBase client (
indra.databases.mirbase_client) - Experimental Factor Ontology (EFO) client (
indra.databases.efo_client) - Human Phenotype Ontology (HP) client (
indra.databases.hp_client) - Disease Ontology (DOID) client (
indra.databases.doid_client) - Infectious Disease Ontology client (
indra.databases.ido_client) - Taxonomy client (
indra.databases.taxonomy_client) - DrugBank client (
indra.databases.drugbank_client) - Enyzme Class client (
indra.databases.ec_client) - OBO client (
indra.databases.obo_client) - OWL client (
indra.databases.owl_client) - Biolookup client (
indra.databases.biolookup_client) - MONDO client (
indra.databases.mondo_client) - MGI client (
indra.databases.mgi_client) - RGD client (
indra.databases.rgd_client)
- identifiers.org mappings and URLs (
- Literature clients (
indra.literature)get_full_text()id_lookup()- Pubmed client (
indra.literature.pubmed_client) - Pubmed Central client (
indra.literature.pmc_client) - bioRxiv client (
indra.literature.biorxiv_client) - CrossRef client (
indra.literature.crossref_client) - COCI client (
indra.literature.coci_client) - Elsevier client (
indra.literature.elsevier_client) - NewsAPI client (
indra.literature.newsapi_client) - Adeft Tools (
indra.literature.adeft_tools)
- INDRA Ontologies (
indra.ontology) - Preassembly (
indra.preassembler)- Preassembler (
indra.preassembler) - Refinement filter classes and functions (
indra.preassembler.refinement) - Custom preassembly functions (
indra.preassembler.custom_preassembly) - Entity grounding mapping and standardization (
indra.preassembler.grounding_mapper) - Site curation and mapping (
indra.preassembler.sitemapper)
- Preassembler (
- Belief prediction (
indra.belief) - Mechanism Linker (
indra.mechlinker) - Assemblers of model output (
indra.assemblers)- Executable PySB models (
indra.assemblers.pysb.assembler) - Cytoscape networks (
indra.assemblers.cx.assembler) - Natural language (
indra.assemblers.english.assembler) - Node-edge graphs (
indra.assemblers.graph.assembler) - SIF / Boolean networks (
indra.assemblers.sif.assembler) - MITRE “index cards” (
indra.assemblers.index_card.assembler) - SBGN output (
indra.assemblers.sbgn.assembler) - Cytoscape JS networks (
indra.assemblers.cyjs.assembler) - Tabular output (
indra.assemblers.tsv.assembler) - HTML browsing and curation (
indra.assemblers.html.assembler) - BMI wrapper for PySB-assembled models (
indra.assemblers.pysb.bmi_wrapper) - PyBEL graphs (
indra.assemblers.pybel.assembler) - Kami models (
indra.assemblers.kami.assembler) - IndraNet Graphs (
indra.assemblers.indranet)
- Executable PySB models (
- Explanation (
indra.explanation) - Assembly Pipeline (
indra.pipeline) - Tools (
indra.tools)- Run assembly components in a pipeline (
indra.tools.assemble_corpus) - Fix common invalidities in Statements (
indra.tools.fix_invalidities) - Analyze ontology graph for issues (
indra.tools.analyze_ontology) - Annotate websites with INDRA through hypothes.is (
indra.tools.hypothesis_annotator) - Build a network from a gene list (
indra.tools.gene_network) - Build an executable model from a fragment of a large network (
indra.tools.executable_subnetwork) - Build a model incrementally over time (
indra.tools.incremental_model) - The RAS Machine (
indra.tools.machine)
- Run assembly components in a pipeline (
- Resource files
- Util (
indra.util)
- INDRA Statements (
- Tutorials
- Using natural language to build models
- The Statement curation interface
- Assembling everything known about a particular gene
- Collect mechanisms from PathwayCommons and the BEL Large Corpus
- Collect a list of publications that discuss the gene of interest
- Get the abstracts corresponding to the publications
- Read the content of the publications
- Combine all statements and run pre-assembly
- Assemble the statements into a network model
- REST API