Campus Units
Electrical and Computer Engineering, Bioinformatics and Computational Biology
Document Type
Article
Publication Version
Published Version
Publication Date
12-16-2014
Journal or Book Title
BMC Bioinformatics
Volume
15
First Page
364
DOI
10.1186/s12859-014-0364-4
Abstract
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.
Rights
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.
Copyright Owner
Liu et al.
Copyright Date
2014
Language
en
File Format
application/pdf
Recommended Citation
Liu, Ruolin; Loraine, Ann E.; and Dickerson, Julie, "Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems" (2014). Electrical and Computer Engineering Publications. 75.
https://lib.dr.iastate.edu/ece_pubs/75
Included in
Bioinformatics Commons, Computational Biology Commons, Systems and Communications Commons
Comments
This article is from BMC Bioinformatics 15 (2014): 364, doi:10.1186/s12859-014-0364-4. Posted with permission.