Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany.
Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany.
Mol Syst Biol. 2021 Jan;17(1):e9730. doi: 10.15252/msb.20209730.
Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.
多组学数据集可以提供超越单一组学的分子见解。最近已经开发了各种工具来整合这些数据集，但从这些数据集中系统地提取机制假设的策略有限。在这里，我们提出了 COSMOS（多组学空间的因果导向搜索），这是一种整合磷酸化蛋白质组学、转录组学和代谢组学数据集的方法。COSMOS 将信号转导、代谢和基因调控网络的广泛先验知识与计算方法相结合，以估计转录因子和激酶的活性以及网络级别的因果推理。COSMOS 为跨多组学数据集的实验观察提供了机制假设。我们将 COSMOS 应用于从 11 名透明细胞肾细胞癌 (ccRCC) 患者的健康组织和癌症组织中获得的转录组学、磷酸化蛋白质组学和代谢组学数据集。COSMOS 能够捕获多个组学层内和层间的相关串扰，例如已知的 ccRCC 药物靶点。我们期望我们免费提供的方法将广泛用于从多组学研究中提取机制见解。
