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Multiomics data integration unveils core transcriptional regulatory networks governing cell-type identity.

ABSTRACT: A plethora of computational approaches have been proposed for reconstructing gene regulatory networks (GRNs) from gene expression data. However, gene regulatory processes are often too complex to predict from the transcriptome alone. Here, we present a computational method, Moni, that systematically integrates epigenetics, transcriptomics, and protein-protein interactions to reconstruct GRNs among core transcription factors and their co-factors governing cell identity. We applied Moni to 57 datasets of human cell types and lines and demonstrate that it can accurately infer GRNs, thereby outperforming state-of-the-art methods.

SUBMITTER: Jung S

PROVIDER: S-EPMC7445234 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Multiomics data integration unveils core transcriptional regulatory networks governing cell-type identity.

Jung Sascha S Del Sol Antonio A

NPJ systems biology and applications 20200824 1

A plethora of computational approaches have been proposed for reconstructing gene regulatory networks (GRNs) from gene expression data. However, gene regulatory processes are often too complex to predict from the transcriptome alone. Here, we present a computational method, Moni, that systematically integrates epigenetics, transcriptomics, and protein-protein interactions to reconstruct GRNs among core transcription factors and their co-factors governing cell identity. We applied Moni to 57 data ...[more]

PMID: 32839455

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