A Comparison of Computational Approaches for Intron Retention Detection
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. Contin...
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Tsinghua University Press
2022-03-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2021.9020014 |
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| author | Jiantao Zheng Cuixiang Lin Zhenpeng Wu Hong-Dong Li |
| author_facet | Jiantao Zheng Cuixiang Lin Zhenpeng Wu Hong-Dong Li |
| author_sort | Jiantao Zheng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ac8b38b7c2464ec398003b5661848b38 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-ac8b38b7c2464ec398003b5661848b382025-02-02T23:47:26ZengTsinghua University PressBig Data Mining and Analytics2096-06542022-03-0151153110.26599/BDMA.2021.9020014A Comparison of Computational Approaches for Intron Retention DetectionJiantao Zheng0Cuixiang Lin1Zhenpeng Wu2Hong-Dong Li3Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaIntron 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.https://www.sciopen.com/article/10.26599/BDMA.2021.9020014alternative splicingintron retentiongene expressionrna-seq |
| spellingShingle | Jiantao Zheng Cuixiang Lin Zhenpeng Wu Hong-Dong Li A Comparison of Computational Approaches for Intron Retention Detection Big Data Mining and Analytics alternative splicing intron retention gene expression rna-seq |
| title | A Comparison of Computational Approaches for Intron Retention Detection |
| title_full | A Comparison of Computational Approaches for Intron Retention Detection |
| title_fullStr | A Comparison of Computational Approaches for Intron Retention Detection |
| title_full_unstemmed | A Comparison of Computational Approaches for Intron Retention Detection |
| title_short | A Comparison of Computational Approaches for Intron Retention Detection |
| title_sort | comparison of computational approaches for intron retention detection |
| topic | alternative splicing intron retention gene expression rna-seq |
| url | https://www.sciopen.com/article/10.26599/BDMA.2021.9020014 |
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