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A Guide to MethylationToActivity: A Deep Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes in Individual Tumors

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Computational Epigenomics and Epitranscriptomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2624))

Abstract

Genome-wide DNA methylomes have contributed greatly to tumor detection and subclassification. However, interpreting the biological impact of the DNA methylome at the individual gene level remains a challenge. MethylationToActivity (M2A) is a pipeline that uses convolutional neural networks to infer H3K4me3 and H3K27ac enrichment from DNA methylomes and thus infer promoter activity. It was shown to be highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers. The following will present a user-friendly guide through the model pipeline.

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Author notes

  1. Authors Karissa Dieseldorff Jones and Daniel Putnam have equally contributed to this chapter.

Authors and Affiliations

  1. Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Karissa Dieseldorff Jones, Daniel Putnam & Xiang Chen

  2. Department of Tumor Cell Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Justin Williams

Authors
  1. Karissa Dieseldorff Jones
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  2. Daniel Putnam
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  3. Justin Williams
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  4. Xiang Chen
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Corresponding author

Correspondence to Xiang Chen .

Editor information

Editors and Affiliations

  1. Genoscope, Évry, France

    Pedro H. Oliveira

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© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Dieseldorff Jones, K., Putnam, D., Williams, J., Chen, X. (2023). A Guide to MethylationToActivity: A Deep Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes in Individual Tumors. In: Oliveira, P.H. (eds) Computational Epigenomics and Epitranscriptomics. Methods in Molecular Biology, vol 2624. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2962-8_6

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  • DOI: https://doi.org/10.1007/978-1-0716-2962-8_6

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2961-1

  • Online ISBN: 978-1-0716-2962-8

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