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Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems
BACKGROUND: Alternative Splicing (AS) as a post-transcription regulation mechanism is an important application of RNA-seq studies in eukaryotes. A number of software and computational methods have been developed for detecting AS. Most of the methods, however, are designed and tested on animal data,...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4271460/ https://www.ncbi.nlm.nih.gov/pubmed/25511303 http://dx.doi.org/10.1186/s12859-014-0364-4 |
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author | Liu, Ruolin Loraine, Ann E Dickerson, Julie A |
author_facet | Liu, Ruolin Loraine, Ann E Dickerson, Julie A |
author_sort | Liu, Ruolin |
collection | PubMed |
description | BACKGROUND: Alternative Splicing (AS) as a post-transcription regulation mechanism is an important application of RNA-seq studies in eukaryotes. A number of software and computational methods have been developed for detecting AS. Most of the methods, however, are designed and tested on animal data, such as human and mouse. Plants genes differ from those of animals in many ways, e.g., the average intron size and preferred AS types. These differences may require different computational approaches and raise questions about their effectiveness on plant data. The goal of this paper is to benchmark existing computational differential splicing (or transcription) detection methods so that biologists can choose the most suitable tools to accomplish their goals. RESULTS: This study compares the eight popular public available software packages for differential splicing analysis using both simulated and real Arabidopsis thaliana RNA-seq data. All software are freely available. The study examines the effect of varying AS ratio, read depth, dispersion pattern, AS types, sample sizes and the influence of annotation. Using a real data, the study looks at the consistences between the packages and verifies a subset of the detected AS events using PCR studies. CONCLUSIONS: No single method performs the best in all situations. The accuracy of annotation has a major impact on which method should be chosen for AS analysis. DEXSeq performs well in the simulated data when the AS signal is relative strong and annotation is accurate. Cufflinks achieve a better tradeoff between precision and recall and turns out to be the best one when incomplete annotation is provided. Some methods perform inconsistently for different AS types. Complex AS events that combine several simple AS events impose problems for most methods, especially for MATS. MATS stands out in the analysis of real RNA-seq data when all the AS events being evaluated are simple AS events. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0364-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4271460 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42714602015-01-02 Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems Liu, Ruolin Loraine, Ann E Dickerson, Julie A BMC Bioinformatics Research Article BACKGROUND: Alternative Splicing (AS) as a post-transcription regulation mechanism is an important application of RNA-seq studies in eukaryotes. A number of software and computational methods have been developed for detecting AS. Most of the methods, however, are designed and tested on animal data, such as human and mouse. Plants genes differ from those of animals in many ways, e.g., the average intron size and preferred AS types. These differences may require different computational approaches and raise questions about their effectiveness on plant data. The goal of this paper is to benchmark existing computational differential splicing (or transcription) detection methods so that biologists can choose the most suitable tools to accomplish their goals. RESULTS: This study compares the eight popular public available software packages for differential splicing analysis using both simulated and real Arabidopsis thaliana RNA-seq data. All software are freely available. The study examines the effect of varying AS ratio, read depth, dispersion pattern, AS types, sample sizes and the influence of annotation. Using a real data, the study looks at the consistences between the packages and verifies a subset of the detected AS events using PCR studies. CONCLUSIONS: No single method performs the best in all situations. The accuracy of annotation has a major impact on which method should be chosen for AS analysis. DEXSeq performs well in the simulated data when the AS signal is relative strong and annotation is accurate. Cufflinks achieve a better tradeoff between precision and recall and turns out to be the best one when incomplete annotation is provided. Some methods perform inconsistently for different AS types. Complex AS events that combine several simple AS events impose problems for most methods, especially for MATS. MATS stands out in the analysis of real RNA-seq data when all the AS events being evaluated are simple AS events. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0364-4) contains supplementary material, which is available to authorized users. BioMed Central 2014-12-16 /pmc/articles/PMC4271460/ /pubmed/25511303 http://dx.doi.org/10.1186/s12859-014-0364-4 Text en © Liu et al.; licensee BioMed Central Ltd. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Liu, Ruolin Loraine, Ann E Dickerson, Julie A Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems |
title | Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems |
title_full | Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems |
title_fullStr | Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems |
title_full_unstemmed | Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems |
title_short | Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems |
title_sort | comparisons of computational methods for differential alternative splicing detection using rna-seq in plant systems |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4271460/ https://www.ncbi.nlm.nih.gov/pubmed/25511303 http://dx.doi.org/10.1186/s12859-014-0364-4 |
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