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Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data

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Reverse Engineering of Regulatory Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2719))

Abstract

Growth is regulated by gene expression variation at different developmental stages of biological processes such as cell differentiation, disease progression, or drug response. In cancer, a stage-specific regulatory model constructed to infer the dynamic expression changes in genes contributing to tissue growth or proliferation is referred as a dynamic growth regulatory network (dGRN). Over the past decade, gene expression data has been widely used for reconstructing dGRN by computing correlations between the differentially expressed genes (DEGs). A wide variety of pipelines are available to construct the GRNs using DEGs and the choice of a particular method or tool depends on the nature of the study. In this protocol, we have outlined a step-by-step guide for the analysis of DEGs using RNA-Seq data, beginning from data acquisition, pre-processing, map** to reference genome, and construction of a correlation-based co-expression network to further downstream analysis. We have also outlined the steps for the inclusion of publicly available interaction/regulation information into the dGRN followed by relevant topological inferences. This tutorial has been designed in a way that early researchers can refer to for an easy and comprehensive glimpse of methodologies used in the inference of dGRN using transcriptomics data.

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Acknowledgments

The authors thank Arindam Ghosh for reviewing the earlier version of the manuscript and for his valuable comments. AC is grateful to the University Grants Commission (India) for providing financial assistance to carry out the research work.

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Authors and Affiliations

  1. Centre of Bioinformatics, Institute of Interdisciplinary Studies, University of Allahabad, Prayagraj, India

    Aparna Chaturvedi & Anup Som

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  1. Aparna Chaturvedi
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  2. Anup Som
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Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Jalpaiguri Govt. Engineering College, Jalpaiguri, West Bengal, India

    Sudip Mandal

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Chaturvedi, A., Som, A. (2024). Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data. In: Mandal, S. (eds) Reverse Engineering of Regulatory Networks. Methods in Molecular Biology, vol 2719. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3461-5_4

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  • DOI: https://doi.org/10.1007/978-1-0716-3461-5_4

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3460-8

  • Online ISBN: 978-1-0716-3461-5

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