ORCO: Ollivier-Ricci Curvature-Omics-an unsupervised method for analyzing robustness in biological systems.

Bioinformatics (Oxford, England) Pub Date : 2025-02-28 DOI:10.1093/bioinformatics/btaf093

Anish K Simhal, Corey Weistuch, Kevin Murgas, Daniel Grange, Jiening Zhu, Jung Hun Oh, Rena Elkin, Joseph O Deasy

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Abstract

Motivation: Although recent advanced sequencing technologies have improved the resolution of genomic and proteomic data to better characterize molecular phenotypes, efficient computational tools to analyze and interpret large-scale omic data are still needed.

Results: To address this, we have developed a network-based bioinformatic tool called Ollivier-Ricci curvature for omics (ORCO). ORCO incorporates omics data and a network describing biological relationships between the genes or proteins and computes Ollivier-Ricci curvature (ORC) values for individual interactions. ORC is an edge-based measure that assesses network robustness. It captures functional cooperation in gene signaling using a consistent information-passing measure, which can help investigators identify therapeutic targets and key regulatory modules in biological systems. ORC has identified novel insights in multiple cancer types using genomic data and in neurodevelopmental disorders using brain imaging data. This tool is applicable to any data that can be represented as a network.

Availability: ORCO is an open-source Python package and is publicly available on GitHub at https://github.com/aksimhal/ORC-Omics.

Supplementary information: Code and notebooks are available at github.com/aksimhal/ORC-Omics.

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Bioinformatics (Oxford, England)

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