Computational methods for intron retention identification and quantification

Schmitz, Ulf, Monteuuis, Geoffray, Petrova, Veronika, Shah, Jaynish S., and Rasko, John E.J. (2021) Computational methods for intron retention identification and quantification. In: Wolkenhauer, Olaf, Cai, Yudong, and Rozman, Damjana, (eds.) Systems Medicine: integrative, qualitative and computational approaches. Academic Press, London Wall, United Kingdom, pp. 63-74.

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Alternative splicing is a ubiquitous process that increases transcriptomic and proteomic complexity across the animal kingdom. Intron retention (IR) is a particular form of alternative splicing that is different from the other forms as it only increases transcriptomic complexity but rarely directly affects the proteome. IR has long been neglected as it was considered a mis-splicing event and was referred to as transcriptional noise. However, recent reports have attributed a pivotal role to IR in normal physiology and diseases.

Studying IR comes with specific technical and analytical requirements, that enable a robust detection and quantification of this phenomenon. Advances in sequencing technologies and the development of IR calling and quantification software have facilitated numerous novel insights into the complex life of introns.

In this chapter, we describe computational methods for the analysis of IR events, their characteristics and conservation, the regulation of IR, and downstream consequences. We also introduce experimental approaches that are used in IR research.

Item ID: 68975
Item Type: Book Chapter (Research - B1)
ISBN: 978-0-12-816078-7
Keywords: Aberrant splicing; Alternative splicing; Epigenetics; Gene isoforms; Gene regulation; Intron retention; Isoform detection; RNA sequencing; Transcript quantification; Transcriptomic complexity; Transcriptomics
Copyright Information: Copyright © 2021 Elsevier Inc. All rights reserved
Date Deposited: 31 Aug 2021 02:54
FoR Codes: 31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310204 Genomics and transcriptomics @ 40%
31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310201 Bioinformatic methods development @ 40%
31 BIOLOGICAL SCIENCES > 3105 Genetics > 310505 Gene expression (incl. microarray and other genome-wide approaches) @ 20%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280102 Expanding knowledge in the biological sciences @ 100%
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