Multi-omics protein-coding units as massively parallel Bayesian networks: Empirical validation of causality structure.

Zenere A, Rundquist O, Gustafsson M, Altafini C

iScience 25 (4) 104048 [2022-04-15; online 2022-03-11]

In this article we use high-throughput epigenomics, transcriptomics, and proteomics data to construct fine-graded models of the "protein-coding units" gathering all transcript isoforms and chromatin accessibility peaks associated with more than 4000 genes in humans. Each protein-coding unit has the structure of a directed acyclic graph (DAG) and can be represented as a Bayesian network. The factorization of the joint probability distribution induced by the DAGs imposes a number of conditional independence relationships among the variables forming a protein-coding unit, corresponding to the missing edges in the DAGs. We show that a large fraction of these conditional independencies are indeed verified by the data. Factors driving this verification appear to be the structural and functional annotation of the transcript isoforms, as well as a notion of structural balance (or frustration-free) of the corresponding sample correlation graph, which naturally leads to reduction of correlation (and hence to independence) upon conditioning.

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PubMed 35355520

DOI 10.1016/j.isci.2022.104048

Crossref 10.1016/j.isci.2022.104048

pmc: PMC8958332
pii: S2589-0042(22)00318-2

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