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

ABSTRACT: 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

SUBMITTER: Zenere A 

PROVIDER: S-EPMC8958332 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature