ArticleBMC bioinformatics2014
Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- Incorporating exon-exon junction reads enhances differential splicing detection.BMC bioinformatics · 2025Article
- A novel splicing graph allows a direct comparison between exon-based and splice junction-based approaches to alternative splicing detection.Briefings in bioinformatics · 2025Article
- Selecting differential splicing methods: Practical considerations for short-read RNA sequencing.F1000Research · 2025Review
- betAS: intuitive analysis and visualization of differential alternative splicing using beta distributions.RNA (New York, N.Y.) · 2024Article
- A Method to Quantitatively Examine Heat Stress-Induced Alternative Splicing in Plants by RNA-Seq and RT-PCR.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Alternative splicing analysis benchmark with DICAST.NAR genomics and bioinformatics · 2023Article
- An Overview of Molecular Basis and Genetic Modification of Floral Organs Genes: Impact of Next-Generation Sequencing.Molecular biotechnology · 2023Review
- Comprehensive and scalable quantification of splicing differences with MntJULiP.Genome biology · 2022Article
- NBBt-test: a versatile method for differential analysis of multiple types of RNA-seq data.Scientific reports · 2022Article
- ASpediaFI: Functional Interaction Analysis of Alternative Splicing Events.Genomics, proteomics & bioinformatics · 2022Article
- Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics.Frontiers in plant science · 2022Review
- FACS-Based Neuronal Cell Type-Specific RNA Isolation and Alternative Splicing Analysis.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Alternative RNA splicing in stem cells and cancer stem cells: Importance of transcript-based expression analysis.World journal of stem cells · 2021Review
- Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers.BMC bioinformatics · 2021Article
- Quantifying splice-site usage: a simple yet powerful approach to analyze splicing.NAR genomics and bioinformatics · 2021Article
- Computing the Role of Alternative Splicing in Cancer.Trends in cancer · 2021Review
- Alternative splicing: Human disease and quantitative analysis from high-throughput sequencing.Computational and structural biotechnology journal · 2021Review
- Article
- Systematic evaluation of differential splicing tools for RNA-seq studies.Briefings in bioinformatics · 2020Article
- ADAR-deficiency perturbs the global splicing landscape in mouse tissues.Genome research · 2020Article
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3 authors.
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Abstract
backgroundAlternative 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.
resultsThis 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.
conclusionsNo 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.