Simhal Anish K, Weistuch Corey, Murgas Kevin, Grange Daniel, Zhu Jiening, Oh Jung Hun, Elkin Rena, Deasy Joseph O
Memorial Sloan Kettering Cancer Center, Department of Medical Physics, New York, NY, USA.
Stony Brook University, Department of Biomedical Informatics, Stony Brook, NY, USA.
bioRxiv. 2024 Oct 11:2024.10.06.616915. doi: 10.1101/2024.10.06.616915.
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.
尽管最近的先进测序技术提高了基因组和蛋白质组数据的分辨率，以便更好地表征分子表型，但仍需要高效的计算工具来分析和解释大规模的组学数据。为了解决这一问题，我们开发了一种基于网络的生物信息学工具，称为奥利维耶 - 里奇曲率组学（ORCO）。ORCO将基因相互作用信息与组学数据整合到一个生物网络中，并为各个相互作用计算奥利维耶 - 里奇曲率（ORC）值。ORC是一种基于边的度量，它表明网络的稳健性，并使用一致的信息传递度量来捕捉功能合作中的全局基因信号变化，从而有助于识别生物系统中的治疗靶点和调控模块。该工具可应用于任何可以表示为网络的数据。ORCO是一个开源的Python包，可在GitHub上公开获取，网址为https://github.com/aksimhal/ORC-Omics。
