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Recent Development of Computational Methods in the Field of Epitranscriptomics

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Handbook of Statistical Bioinformatics

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Abstract

The RNA epigenetics gold rush in the past few years has brought reversible RNA modifications (epitranscriptomics) into the spotlight as an important mechanism of gene regulation due to their important functions in various biological systems. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) is so far the most widely used technique in RNA modification studies to survey the epitranscriptome. The unique characteristics of MeRIP-seq data posted new challenges to computational methods. In this chapter, we will introduce the biology of epitranscriptomics, the MeRIP-seq, and other genome-wide assays to survey RNA modifications. Then, we will discuss the challenges in analyzing MeRIP-seq data as well as recent progress in computational methods to tackle these challenges. Further, we survey bioinformatic resources that could be useful in epitranscriptomics studies.

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Authors and Affiliations

  1. State Key Laboratory for Conservation and Utilization of Bio-resource and School of Life Sciences, Yunnan University, Yunnan, China

    Zijie Zhang

  2. Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA

    Shun Liu & Mengjie Chen

  3. Department of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA

    Shun Liu & Chuan He

  4. Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA

    Shun Liu & Chuan He

  5. Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL, USA

    Chuan He

  6. Department of Human Genetics, The University of Chicago, Chicago, IL, USA

    Mengjie Chen

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  1. Zijie Zhang
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  2. Shun Liu
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  3. Chuan He
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  4. Mengjie Chen
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Correspondence to Mengjie Chen .

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Editors and Affiliations

  1. Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, Taiwan

    Henry Horng-Shing Lu

  2. Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany

    Bernhard Schölkopf

  3. Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA

    Martin T. Wells

  4. Department of Biostatistics, Yale University, New Haven, CT, USA

    Hongyu Zhao

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Zhang, Z., Liu, S., He, C., Chen, M. (2022). Recent Development of Computational Methods in the Field of Epitranscriptomics. In: Lu, H.HS., Schölkopf, B., Wells, M.T., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-65902-1_15

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