Assembly and reasoning over semantic mappings at scale for biomedical data integration

TL;DR

This paper introduces SeMRA, a tool that helps integrate biomedical data by connecting identifiers from different resources using a graph-based approach.

Contribution

SeMRA introduces a scalable system for assembling and reasoning over semantic mappings across biomedical identifiers.

Findings

SeMRA integrates 43.4 million mappings from 127 sources covering 445 ontologies and databases.

The system enables connecting identifier spaces previously unmapped through inferred relationships.

Benchmarks demonstrate successful integration of disease and cell type resources.

Abstract

Hundreds of resources assign identifiers to biomedical concepts including genes, small molecules, biological processes, diseases, and cell types. Often, these resources overlap by assigning identifiers to the same or related concepts. This creates a data interoperability bottleneck, as integrating data sets and knowledge bases that use identifiers for the same concepts from different resources requires such identifiers to be mapped to each other. However, available mappings are incomplete and fragmented across individual resources, motivating their large-scale integration.

We developed SeMRA, a software tool that integrates mappings from multiple sources into a graph data structure. Using graph algorithms, it infers missing mappings implied by available ones while keeping track of provenance and confidence. This allows connecting identifier spaces between which direct mapping was previously not possible. SeMRA implements a customizable workflow that takes a declarative specification as input describing sources to integrate with additional configuration parameters. We used SeMRA to produce the SeMRA Raw Mappings Database, an aggregation of 43.4 million mappings from 127 sources that jointly cover identifiers from 445 ontologies and databases. We also describe benchmarks on specific use cases such as integrating mappings between resources cataloging diseases and cell types.

The code is available under the MIT license at https://github.com/biopragmatics/semra. The SeMRA Raw Mappings Database assembled by SeMRA is available at https://doi.org/10.5281/zenodo.11082038.

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