Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses.

Dugourd, Aurelien, Kuppe, Christoph, Sciacovelli, Marco, Gjerga, Enio, Gabor, Attila, Emdal, Kristina B, Vieira, Vitor, Bekker-Jensen, Dorte B, Kranz, Jennifer, Bindels, Eric MJ, Costa, Ana SH, Sousa, Abel, Beltrao, Pedro, Rocha, Miguel, Olsen, Jesper V, Frezza, Christian, Kramann, Rafael, Saez-Rodriguez, Julio (January 2021) Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses. Molecular Systems Biology, 17 (1). e9730. ISSN 1744-4292

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URL: https://www.ncbi.nlm.nih.gov/pubmed/33502086
DOI: 10.15252/msb.20209730

Abstract

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.

Item Type: Paper
Subjects: bioinformatics > computational biology
organs, tissues, organelles, cell types and functions > organs types and functions > kidney
CSHL Authors:
Communities: CSHL labs > Pappin lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: January 2021
Date Deposited: 12 Jul 2021 20:41
Last Modified: 12 Jul 2021 20:41
PMCID: PMC7838823
URI: https://repository.cshl.edu/id/eprint/40275

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