Multi-omics protein-coding units as massively parallel Bayesian networks: Empirical validation of causality structure.
Bioinformatics
Mathematical biosciences
Omics
Systems biology
Journal
iScience
ISSN: 2589-0042
Titre abrégé: iScience
Pays: United States
ID NLM: 101724038
Informations de publication
Date de publication:
15 Apr 2022
15 Apr 2022
Historique:
received:
24
08
2021
revised:
17
01
2022
accepted:
08
03
2022
entrez:
31
3
2022
pubmed:
1
4
2022
medline:
1
4
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 35355520
doi: 10.1016/j.isci.2022.104048
pii: S2589-0042(22)00318-2
pmc: PMC8958332
doi:
Types de publication
Journal Article
Langues
eng
Pagination
104048Informations de copyright
© 2022 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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