A Practical Guide to Inferring Multi-Omics Networks in Plant Systems.
Gene regulatory networks
Multi-omics
Network inference
Plant signaling
Proteomics
Transcriptomics
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2023
2023
Historique:
medline:
11
9
2023
pubmed:
8
9
2023
entrez:
8
9
2023
Statut:
ppublish
Résumé
The inference of gene regulatory networks can reveal molecular connections underlying biological processes and improve our understanding of complex biological phenomena in plants. Many previous network studies have inferred networks using only one type of omics data, such as transcriptomics. However, given more recent work applying multi-omics integration in plant biology, such as combining (phospho)proteomics with transcriptomics, it may be advantageous to integrate multiple omics data types into a comprehensive network prediction. Here, we describe a state-of-the-art approach for integrating multi-omics data with gene regulatory network inference to describe signaling pathways and uncover novel regulators. We detail how to download and process transcriptomics and (phospho)proteomics data for network inference, using an example dataset from the plant hormone signaling field. We provide a step-by-step protocol for inference, visualization, and analysis of an integrative multi-omics network using currently available methods. This chapter serves as an accessible guide for novice and intermediate bioinformaticians to analyze their own datasets and reanalyze published work.
Identifiants
pubmed: 37682479
doi: 10.1007/978-1-0716-3354-0_15
doi:
Substances chimiques
Plant Growth Regulators
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
233-257Subventions
Organisme : NIGMS NIH HHS
ID : R01 GM120316
Pays : United States
Informations de copyright
© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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