Improved methods for RNAseq-based alternative splicing analysis

Abstract The robust detection of disease-associated splice events from RNAseq data is challenging due to the potential confounding effect of gene expression levels and the often limited number of patients with relevant RNAseq data. Here we present a novel statistical approach to splicing outlier det...

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Autores principales:	Rebecca F. Halperin, Apurva Hegde, Jessica D. Lang, Elizabeth A. Raupach, C4RCD Research Group, Christophe Legendre, Winnie S. Liang, Patricia M. LoRusso, Aleksandar Sekulic, Jeffrey A. Sosman, Jeffrey M. Trent, Sampathkumar Rangasamy, Patrick Pirrotte, Nicholas J. Schork
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a3220a92cf2645288aad59ed2f183636
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spelling	oai:doaj.org-article:a3220a92cf2645288aad59ed2f1836362021-12-02T14:42:20ZImproved methods for RNAseq-based alternative splicing analysis10.1038/s41598-021-89938-22045-2322https://doaj.org/article/a3220a92cf2645288aad59ed2f1836362021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89938-2https://doaj.org/toc/2045-2322Abstract The robust detection of disease-associated splice events from RNAseq data is challenging due to the potential confounding effect of gene expression levels and the often limited number of patients with relevant RNAseq data. Here we present a novel statistical approach to splicing outlier detection and differential splicing analysis. Our approach tests for differences in the percentages of sequence reads representing local splice events. We describe a software package called Bisbee which can predict the protein-level effect of splice alterations, a key feature lacking in many other splicing analysis resources. We leverage Bisbee’s prediction of protein level effects as a benchmark of its capabilities using matched sets of RNAseq and mass spectrometry data from normal tissues. Bisbee exhibits improved sensitivity and specificity over existing approaches and can be used to identify tissue-specific splice variants whose protein-level expression can be confirmed by mass spectrometry. We also applied Bisbee to assess evidence for a pathogenic splicing variant contributing to a rare disease and to identify tumor-specific splice isoforms associated with an oncogenic mutation. Bisbee was able to rediscover previously validated results in both of these cases and also identify common tumor-associated splice isoforms replicated in two independent melanoma datasets.Rebecca F. HalperinApurva HegdeJessica D. LangElizabeth A. RaupachC4RCD Research GroupChristophe LegendreWinnie S. LiangPatricia M. LoRussoAleksandar SekulicJeffrey A. SosmanJeffrey M. TrentSampathkumar RangasamyPatrick PirrotteNicholas J. SchorkNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rebecca F. Halperin Apurva Hegde Jessica D. Lang Elizabeth A. Raupach C4RCD Research Group Christophe Legendre Winnie S. Liang Patricia M. LoRusso Aleksandar Sekulic Jeffrey A. Sosman Jeffrey M. Trent Sampathkumar Rangasamy Patrick Pirrotte Nicholas J. Schork Improved methods for RNAseq-based alternative splicing analysis
description	Abstract The robust detection of disease-associated splice events from RNAseq data is challenging due to the potential confounding effect of gene expression levels and the often limited number of patients with relevant RNAseq data. Here we present a novel statistical approach to splicing outlier detection and differential splicing analysis. Our approach tests for differences in the percentages of sequence reads representing local splice events. We describe a software package called Bisbee which can predict the protein-level effect of splice alterations, a key feature lacking in many other splicing analysis resources. We leverage Bisbee’s prediction of protein level effects as a benchmark of its capabilities using matched sets of RNAseq and mass spectrometry data from normal tissues. Bisbee exhibits improved sensitivity and specificity over existing approaches and can be used to identify tissue-specific splice variants whose protein-level expression can be confirmed by mass spectrometry. We also applied Bisbee to assess evidence for a pathogenic splicing variant contributing to a rare disease and to identify tumor-specific splice isoforms associated with an oncogenic mutation. Bisbee was able to rediscover previously validated results in both of these cases and also identify common tumor-associated splice isoforms replicated in two independent melanoma datasets.
format	article
author	Rebecca F. Halperin Apurva Hegde Jessica D. Lang Elizabeth A. Raupach C4RCD Research Group Christophe Legendre Winnie S. Liang Patricia M. LoRusso Aleksandar Sekulic Jeffrey A. Sosman Jeffrey M. Trent Sampathkumar Rangasamy Patrick Pirrotte Nicholas J. Schork
author_facet	Rebecca F. Halperin Apurva Hegde Jessica D. Lang Elizabeth A. Raupach C4RCD Research Group Christophe Legendre Winnie S. Liang Patricia M. LoRusso Aleksandar Sekulic Jeffrey A. Sosman Jeffrey M. Trent Sampathkumar Rangasamy Patrick Pirrotte Nicholas J. Schork
author_sort	Rebecca F. Halperin
title	Improved methods for RNAseq-based alternative splicing analysis
title_short	Improved methods for RNAseq-based alternative splicing analysis
title_full	Improved methods for RNAseq-based alternative splicing analysis
title_fullStr	Improved methods for RNAseq-based alternative splicing analysis
title_full_unstemmed	Improved methods for RNAseq-based alternative splicing analysis
title_sort	improved methods for rnaseq-based alternative splicing analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a3220a92cf2645288aad59ed2f183636
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_version_	1718389706826186752

Improved methods for RNAseq-based alternative splicing analysis

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