AI Article Synopsis
- Recent advancements in sequencing tech have improved how we analyze genomic and proteomic data, but we still lack efficient computational tools for large-scale data analysis.
- To fill this gap, a new bioinformatics tool called Ollivier-Ricci curvature-omics (ORCO) has been developed, which incorporates gene interactions and omic data into a biological network.
- ORCO calculates Ollivier-Ricci curvature (ORC) values to assess network robustness and gene signaling changes, and it's an open-source Python package available on GitHub for public use.
Article Abstract
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 the large-scale omic data are still needed. To address this, we have developed a network-based bioinformatic tool called Ollivier-Ricci curvature-omics (ORCO). ORCO incorporates gene interaction information with omic data into a biological network, and computes Ollivier-Ricci curvature (ORC) values for individual interactions. ORC, an edge-based measure, indicates network robustness and captures global gene signaling changes in functional cooperation using a consistent information passing measure, thereby helping identify therapeutic targets and regulatory modules in biological systems. This tool can be applicable to any data that can be represented as a network. ORCO is an open-source Python package and publicly available on GitHub at https://github.com/aksimhal/ORC-Omics.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11482976 | PMC |
| http://dx.doi.org/10.1101/2024.10.06.616915 | DOI Listing |
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