Comparison of software packages for detecting differential expression in RNA-seq studies
Open accessAbstract
RNA-sequencing (RNA-seq) has rapidly become a popular tool to characterize transcriptomes. A fundamental research problem in many RNA-seq studies is the identification of reliable molecular markers that show differential expression between distinct sample groups. Together with the growing popularity of RNA-seq, a number of data analysis methods and pipelines have already been developed for this task. Currently, however, there is no clear consensus about the best practices yet, which makes the choice of an appropriate method a daunting task especially for a basic user without a strong statistical or computational background. To assist the choice, we perform here a systematic comparison of eight widely used software packages and pipelines for detecting differential expression between sample groups in a practical research setting and provide general guidelines for choosing a robust pipeline. In general, our results demonstrate how the data analysis tool utilized can markedly affect the outcome of the data analysis, highlighting the importance of this choice.
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Recent independent comparison studies have demonstrated that the choice of the method (or even the version of a software package) can markedly affect the outcome of the analysis and that no single method is likely to perform favorably for all datasets.
“Recent independent comparison studies have demonstrated that the choice of the method (or even the version of a software package) can markedly affect the outcome of the analysis and that no single method is likely to perform favorably for all datasets [56, 63, 64]”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [64]
All differential expression methods require caution when the number of replicates is very small or for very lowly expressed genes.
“First, specific caution is needed with all the methods when the number of replicate samples is very small or for genes that are expressed at very low levels [55, 64, 209].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [64]
Because simulations rely on specific distributions or limited datasets and spike-in datasets have only technical replicates with minimal variation, comparisons have been complemented with real datasets with true biological replicates.
“As simulations typically rely on specific statistical distributions or on limited experimental datasets and as spike-in datasets represent only technical replicates with minimal variation, comparisons using simulated datasets have been complemented with more practical comparisons in real datasets with true biological replicates [64, 210, 211].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [64]
limma performs well under many circumstances and is the fastest to run among the tools.
“Among the tools, limma has been shown to perform well under many circumstances and it is also the fastest to run [56, 63, 64].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [64]
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- Authors
- Seyednasrollah, F., Laiho, A., & Elo, L. L.
- Affiliations
- University of Turku (FI)
- Corresponding author
- Elo, L. L.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-12-02
- Volume
- 16
- Issue
- 1
- Pages
- 59-70
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC4293378
- OpenAlex
- W2123579165
- Cited by
- 421
- References
- 27
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 18 · 2023: 35 · 2022: 34 · 2021: 39 · 2020: 41 · 2019: 41 · 2018: 39 · 2017: 55 · 2016: 52 · 2015: 32 · 2014: 16 · 2013: 1
- FWCI
- 15.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Briefings in Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Academy of Finland
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- differential gene expression, RNA-seq, software comparison, bioinformatics pipeline, molecular markers, gene expression profiling
- MeSH terms
- Animals, Data Interpretation, Statistical, Humans, Software, Sequence Analysis, RNA, Mice, Transcriptome
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