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Curated and predicted mappings between biomedical identifiers in different namespaces

Project description


Check mappings PyPI PyPI - Python Version PyPI - License DOI Code style: black Powered by the Bioregistry

Biomappings is a repository of community curated and predicted equivalences and related mappings between named biological entities that are not available from primary sources. It's also a place where anyone can contribute curations of predicted mappings or their own novel mappings. Ultimately, we hope that primary resources will integrate these mappings and distribute them themselves.

Mappings are stored in a simple TSV file that looks like this:

💾 Data

The data are available through the following four files on the biopragmatics/biomappings GitHub repository.

Curated Description Link
Yes Human-curated true mappings src/biomappings/resources/mappings.tsv
Yes Human-curated non-trivial false (i.e., incorrect) mappings src/biomappings/resources/incorrect.tsv
Yes Mappings that have been checked but not yet decided src/biomappings/resources/unsure.tsv
No Automatically predicted mappings src/biomappings/resources/predictions.tsv

The primary and derived data in this repository are both available under the CC0 1.0 Universal License.

Predictions are generated by scripts in the scripts/ folder. Each uses the utilities from the biomappings.resources module to programmatically interact with the mappings files, e.g., to add predictions.

🥒 Derived

The mappings are distributed in the Simple Standard for Sharing Ontology Mappings (SSSOM) format (here). The positive mappings are also available as a network through NDEx.

Equivalences and related mappings that are available from the OBO Foundry and other primary sources can be accessed through Inspector Javert's Xref Database on Zenodo which was described in this blog post.

📊 Summary

Summary statistics of the manually curated mappings and predicted mappings are automatically generated nightly and deployed as a website with GitHub Actions to

Summary statistics

🙏 Contributing

We welcome contributions in the form of curations to any of the four primary TSV files in this repository via a pull request to the main Biomappings repository at

Predicted mappings can be curated by moving a row in the predictions.tsv file into either the positive mappings file (mappings.tsv), negative mappings file (incorrect.tsv), or the unsure mappings file (unsure.tsv). Additionally, the confidence column should be removed, a type column should be added with the value manually_reviewed, and the source column should be changed from the prediction script's URI to your ORCiD identifier written as a CURIE (e.g., orcid:0000-0003-1307-2508).

Novel mappings can be curated by adding a full row to the positive mappings file (mappings.tsv) following the format of the previous lines.

While Biomappings is generally able to use any predicate written as a compact URI (CURIE), it's preferred to use predicates from the Simple Knowledge Organization System (SKOS) to denote hierarchical relationships. The three most common predicates that are useful for curating mappings are:

Predicate Description
skos:exactMatch The two terms can be used interchangeably
skos:broadMatch The object term is a super-class of the subject
skos:narrowMatch The object term is a sub-class of the subject

Online via GitHub Web Interface

GitHub has an interface for editing files directly in the browser. It will take care of creating a branch for you and creating a pull request. After logging into GitHub, click one of the following links to be brought to the editing interface:

This has the caveat that you can only edit one file at a time. It's possible to navigate to your own forked version of the repository after, to the correct branch (will not be the default one), then edit other files in the web interface as well. However, if you would like to do this, then it's probably better to see the following instructions on contributing locally.

✍️ Local via a Text Editor

  1. Fork the repository at, clone locally, and make a new branch (see below)
  2. Edit one or more of the resource files (mappings.tsv, incorrect.tsv, unsure.tsv, predictions.tsv)
  3. Commit to your branch, push, and create a pull request back to the upstream repository.

🌐 Local via the Web Curation Interface

Rather than editing files locally, this repository also comes with a web-based curation interface. Install the code in development mode with the web option (which installs flask and flask-bootstrap) using:

$ git clone git+
$ cd biomappings
$ pip install -e .[web]

The web application can be run with:

$ biomappings web

It can be accessed by navigating to http://localhost:5000/ in your browser. After you do some curations, the web application takes care of interacting with the git repository from which you installed biomappings via the "commit and push" button.

Note if you've installed biomappings via PyPI, then running the web curation interface doesn't make much sense, since it's non-trivial for most users to find the location of the resources within your Python installation's site-packages folder, and you won't be able to contribute them back.

Curation Attribution

There are three places where curators of Biomappings are credited:

  1. ORCiD identifiers of curators are stored in each mapping
  2. The summary website groups and counts contributions curator
  3. A curation leaderboard is automatically uploaded to APICURON.

⬇️ Installation

The most recent release can be installed from PyPI with:

$ pip install biomappings

The most recent code and data can be installed directly from GitHub with:

$ pip install git+

To install in development mode, use the following:

$ git clone git+
$ cd biomappings
$ pip install -e .

💪 Usage

There are three main functions exposed from biomappings. Each loads a list of dictionaries with the mappings in each.

import biomappings

true_mappings = biomappings.load_mappings()

false_mappings = biomappings.load_false_mappings()

predictions = biomappings.load_predictions()

Alternatively, you can use the above links to the TSVs on GitHub in with the library or programming language of your choice.

The data can also be loaded as networkx graphs with the following functions:

import biomappings

true_graph = biomappings.get_true_graph()

false_graph = biomappings.get_false_graph()

predictions_graph = biomappings.get_predictions_graph()

👋 Attribution

⚖️ License

Code is licensed under the MIT License. Data are licensed under the CC0 License.

🎁 Support

Biomappings was developed by the INDRA Lab, a part of the Laboratory of Systems Pharmacology and the Harvard Program in Therapeutic Science (HiTS) at Harvard Medical School.

💰 Funding

The development of the Bioregistry is funded by the DARPA Young Faculty Award W911NF2010255 (PI: Benjamin M. Gyori).

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