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Intron Retention (IR) is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced in most cases. IR has been gaining increasing attention in recent years because of its recognized association with gene expression regulation and complex diseases. Continuous efforts have been dedicated to the development of IR detection methods. These methods differ in their metrics to quantify retention propensity, performance to detect IR events, functional enrichment of detected IRs, and computational speed. A systematic experimental comparison would be valuable to the selection and use of existing methods. In this work, we conduct an experimental comparison of existing IR detection methods. Considering the unavailability of a gold standard dataset of intron retention, we compare the IR detection performance on simulation datasets. Then, we compare the IR detection results with real RNA-Seq data. We also describe the use of differential analysis methods to identify disease-associated IRs and compare differential IRs along with their Gene Ontology enrichment, which is illustrated on an Alzheimer’s disease RNA-Seq dataset. We discuss key principles and features of existing approaches and outline their differences. This systematic analysis provides helpful guidance for interrogating transcriptomic data from the point of view of IR.


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A Comparison of Computational Approaches for Intron Retention Detection

Show Author's information Jiantao ZhengCuixiang LinZhenpeng WuHong-Dong Li( )
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China

† Jiantao Zheng and Cuixiang Lin contributed equally to this paper.

Abstract

Intron Retention (IR) is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced in most cases. IR has been gaining increasing attention in recent years because of its recognized association with gene expression regulation and complex diseases. Continuous efforts have been dedicated to the development of IR detection methods. These methods differ in their metrics to quantify retention propensity, performance to detect IR events, functional enrichment of detected IRs, and computational speed. A systematic experimental comparison would be valuable to the selection and use of existing methods. In this work, we conduct an experimental comparison of existing IR detection methods. Considering the unavailability of a gold standard dataset of intron retention, we compare the IR detection performance on simulation datasets. Then, we compare the IR detection results with real RNA-Seq data. We also describe the use of differential analysis methods to identify disease-associated IRs and compare differential IRs along with their Gene Ontology enrichment, which is illustrated on an Alzheimer’s disease RNA-Seq dataset. We discuss key principles and features of existing approaches and outline their differences. This systematic analysis provides helpful guidance for interrogating transcriptomic data from the point of view of IR.

Keywords:

alternative splicing, intron retention, gene expression, RNA-Seq
Received: 22 April 2021 Revised: 09 August 2021 Accepted: 20 August 2021 Published: 27 December 2021 Issue date: March 2022
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Publication history

Received: 22 April 2021
Revised: 09 August 2021
Accepted: 20 August 2021
Published: 27 December 2021
Issue date: March 2022

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© The author(s) 2022

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61772556, 61972185, U1909208, 61972423, and 61832019), 111 Project (No. B18059), and Hunan Provincial Science and Technology Program (No. 2018WK4001).

The results published here are in part based on data obtained from the AD Knowledge Portal (https://adknowledgeportal.org). Support for these studies was provided by the NIH U01 AG046139. We thank Drs. Jada Lewis, Karen Duff, David Westaway, and David Borchelt for generating these lines of transgenic mice and providing us access to them.

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