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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Autor*in:	Jiantao Zheng [verfasserIn] Cuixiang Lin [verfasserIn] Zhenpeng Wu [verfasserIn] Hong-Dong Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	alternative splicing intron retention gene expression rna-seq

Übergeordnetes Werk:	In: Big Data Mining and Analytics - Tsinghua University Press, 2022, 5(2022), 1, Seite 15-31
Übergeordnetes Werk:	volume:5 ; year:2022 ; number:1 ; pages:15-31

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.26599/BDMA.2021.9020014

Katalog-ID:	DOAJ021975582

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520			\|a 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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spelling	10.26599/BDMA.2021.9020014 doi (DE-627)DOAJ021975582 (DE-599)DOAJac8b38b7c2464ec398003b5661848b38 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Jiantao Zheng verfasserin aut A Comparison of Computational Approaches for Intron Retention Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. alternative splicing intron retention gene expression rna-seq Electronic computers. Computer science Cuixiang Lin verfasserin aut Zhenpeng Wu verfasserin aut Hong-Dong Li verfasserin aut In Big Data Mining and Analytics Tsinghua University Press, 2022 5(2022), 1, Seite 15-31 (DE-627)1734023988 20960654 nnns volume:5 year:2022 number:1 pages:15-31 https://doi.org/10.26599/BDMA.2021.9020014 kostenfrei https://doaj.org/article/ac8b38b7c2464ec398003b5661848b38 kostenfrei https://www.sciopen.com/article/10.26599/BDMA.2021.9020014 kostenfrei https://doaj.org/toc/2096-0654 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2022 1 15-31
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allfieldsSound	10.26599/BDMA.2021.9020014 doi (DE-627)DOAJ021975582 (DE-599)DOAJac8b38b7c2464ec398003b5661848b38 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Jiantao Zheng verfasserin aut A Comparison of Computational Approaches for Intron Retention Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. alternative splicing intron retention gene expression rna-seq Electronic computers. Computer science Cuixiang Lin verfasserin aut Zhenpeng Wu verfasserin aut Hong-Dong Li verfasserin aut In Big Data Mining and Analytics Tsinghua University Press, 2022 5(2022), 1, Seite 15-31 (DE-627)1734023988 20960654 nnns volume:5 year:2022 number:1 pages:15-31 https://doi.org/10.26599/BDMA.2021.9020014 kostenfrei https://doaj.org/article/ac8b38b7c2464ec398003b5661848b38 kostenfrei https://www.sciopen.com/article/10.26599/BDMA.2021.9020014 kostenfrei https://doaj.org/toc/2096-0654 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2022 1 15-31
language	English
source	In Big Data Mining and Analytics 5(2022), 1, Seite 15-31 volume:5 year:2022 number:1 pages:15-31
sourceStr	In Big Data Mining and Analytics 5(2022), 1, Seite 15-31 volume:5 year:2022 number:1 pages:15-31
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institution	findex.gbv.de
topic_facet	alternative splicing intron retention gene expression rna-seq Electronic computers. Computer science
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container_title	Big Data Mining and Analytics
authorswithroles_txt_mv	Jiantao Zheng @@aut@@ Cuixiang Lin @@aut@@ Zhenpeng Wu @@aut@@ Hong-Dong Li @@aut@@
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callnumber-first	Q - Science
author	Jiantao Zheng
spellingShingle	Jiantao Zheng misc QA75.5-76.95 misc alternative splicing misc intron retention misc gene expression misc rna-seq misc Electronic computers. Computer science A Comparison of Computational Approaches for Intron Retention Detection
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topic_title	QA75.5-76.95 A Comparison of Computational Approaches for Intron Retention Detection alternative splicing intron retention gene expression rna-seq
topic	misc QA75.5-76.95 misc alternative splicing misc intron retention misc gene expression misc rna-seq misc Electronic computers. Computer science
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title	A Comparison of Computational Approaches for Intron Retention Detection
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title_full	A Comparison of Computational Approaches for Intron Retention Detection
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title_auth	A Comparison of Computational Approaches for Intron Retention Detection
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.
abstractGer	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.
abstract_unstemmed	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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title_short	A Comparison of Computational Approaches for Intron Retention Detection
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