MetaBase

The MetaBase API provides you access to MetaBase, which is the knowledge base behind all systems biology products of Clarivate Analytics, including MetaCore, MetaDrug, Key Pathway Advisor, and others. MetaBase contains over 6 million experimental findings on protein-protein, protein-nucleic acids, and protein-compound interactions. MetaBase also includes thousands of established canonical signaling and metabolic pathways, ligand-receptor binding data for drugs and bioactive compounds, drug targets and diseases, and other information.

All mentioned above makes it the most comprehensive manually curated database of mammalian biology data available today in both industry and academia.

MetaBase API can help you to

  • Understand the relations between genes, products, and genetic aberrations
  • Obtain the information about how pairs of molecules interact with each other in various biological chain reactions
  • Understand the biology of the disease you are working on and select suitable biomarkers for further research
  • Discover novel targets: identification of genes which are associated with specific diseases
  • Combine different knowledge areas to design your workflows, such as interaction networks for the particular disease

The MetaBase API allows a wide range of users like bioinformaticians, scientists, or developers, to search and retrieve information for different MetaBase™ terms and the relations between them. There are 12 main knowledge areas currently available in the MetaBase API:

  • Biological Entities:
    • Genes: covering information about Human, Mouse, and Rat genes based on NCBI Gene information.
    • RNAs: covering information about different types of RNAs (mRNA, microRNA, other non-coding RNAs).
    • Proteins: covering information about different proteins, protein groups, and complexes and their relations for a distinct variety of organisms, primary Human, Mouse, and Rat.
  • Genetic Aberrations: covering information about human genetic aberrations, including SNPs, gene rearrangements, locus changes.
  • Epigenetic Modifications: covering information about methylated, acetylated and other types of epigenetic modifications for human genes.
  • Chemical Compounds: covering molecular entities like xenobiotic compounds (such as drugs), endogenous compounds and nutrients, metabolites of xenobiotics.
  • Molecular Interactions: covering information about interactions (obtained from scientific articles) with detailed information on the actions/effects that occur between different molecular objects.
  • Reactions: covering information about a distinct type of relationship between database objects, showing either transformation of an object to another one or changing the object localization.
  • Diseases: a controlled vocabulary of MeSH Disease terms with a thesaurus to facilitate searching.
  • Biomarker-Disease Associations: covering the relationship between diseases and human gene variants, gene expression/activity differences, epigenetic modifications, and metabolite (chemical compounds) abundance.
  • Pathway Maps: covering information about small subnetworks representing complete biochemical pathways or signaling cascades in a commonly accepted sense with human-readable layouts and related images.
  • Prebuilt Networks: covering information about networks manually created to represent biological pathways and processes in normal and disease states and organized in several ontologies (Disease Biomarker, Toxicity, Metabolic, etc.)
  • Canonical Pathways: covering information about automatically generated linear sequences of interactions derived either from pathway maps or curated networks.
  • Toxicity Associations: covering information about how toxicants cause adverse effects on living organisms and how they affect proteins/RNAs/endogenous compounds in mammalian systems.
  • Articles (PubMed): providing the list of all annotated scientific publications and all the related information to it, such as detailed interaction annotations, biomarker-disease associations, or toxicity associations.

Content Structure

Biomarker-Disease associations and Toxicity

Interactions and Reactions

Pathway Maps, Networks and Canonical Pathways

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