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A Practical Guide to Inferring Multi-Omics Networks in Plant Systems.

Natalie M ClarkBhavna HurgobinDior R KelleyMathew G LewseyJustin W Walley
Published in: Methods in molecular biology (Clifton, N.J.) (2023)
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.
Keyphrases
  • single cell
  • rna seq
  • electronic health record
  • big data
  • mass spectrometry
  • signaling pathway
  • randomized controlled trial
  • oxidative stress
  • machine learning
  • deep learning
  • genome wide
  • induced apoptosis