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Modeling Transcriptional Regulation pp 191–202Cite as

Dynamic Regulatory Event Mining by iDREM in Large-Scale Multi-omics Datasets During Biotic and Abiotic Stress in Plants

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 2328))

Abstract

The system-wide complexity of genome regulation encoding the organism phenotypic diversity is well understood. However, a major challenge persists about the appropriate method to describe the systematic dynamic genome regulation event utilizing enormous multi-omics datasets. Here, we describe Interactive Dynamic Regulatory Events Miner (iDREM) which reconstructs gene-regulatory networks from temporal transcriptome, proteome, and epigenome datasets during stress to envisage “master” regulators by simulating cascades of temporal transcription-regulatory and interactome events. The iDREM is a Java-based software that integrates static and time-series transcriptomics and proteomics datasets, transcription factor (TF)–target interactions, microRNA (miRNA)–target interaction, and protein–protein interactions to reconstruct temporal regulatory network and identify significant regulators in an unsupervised manner. The hidden Markov model detects specialized manipulated pathways as well as genes to recognize statistically significant regulators (TFs/miRNAs) that diverge in temporal activity. This method can be translated to any biotic or abiotic stress in plants and animals to predict the master regulators from condition-specific multi-omics datasets including host–pathogen interactions for comprehensive understanding of manipulated biological pathways.

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Acknowledgments

N.K. and J.L. were supported by the National Science Foundation award (IOS-1557796 and IOS-2038872). Work in K.P.M. laboratory is supported by a NSF-CAREER (IOS-1350244 and IOS-2038872) award.

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Authors and Affiliations

  1. Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA

    Bharat Mishra, Nilesh Kumar, Jinbao Liu & Karolina M. Pajerowska-Mukhtar

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  1. Bharat Mishra
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  2. Nilesh Kumar
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  3. Jinbao Liu
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  4. Karolina M. Pajerowska-Mukhtar
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Corresponding author

Correspondence to Karolina M. Pajerowska-Mukhtar .

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  1. Department of Biology, University of Alabama, Birmingham, AL, USA

    SHAHID MUKHTAR

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Mishra, B., Kumar, N., Liu, J., Pajerowska-Mukhtar, K.M. (2021). Dynamic Regulatory Event Mining by iDREM in Large-Scale Multi-omics Datasets During Biotic and Abiotic Stress in Plants. In: MUKHTAR, S. (eds) Modeling Transcriptional Regulation. Methods in Molecular Biology, vol 2328. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1534-8_12

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  • DOI: https://doi.org/10.1007/978-1-0716-1534-8_12

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