ArticleRNA (New York, N.Y.)2016
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?
Article in RNA (New York, N.Y.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 500 papers.
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Who cites it
500 citing papers in PubMed.
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- Heart-Specific Spinal and Vagal Afferents: Transcriptomic Signatures and Optogenetically Modulated Functional Coupling With Cardiomyocytes.Comprehensive Physiology · 2026Article
- Multi-Omics-Guided Design and Safety Engineering of Nucleic Acid Therapeutics: From Molecular Perturbation to Predictive Toxicology and Precision Translation.Chemical biology & drug design · 2026Review
- Transcriptomic profiling of pediatric asthma by diseases state and severity.Respiratory research · 2026Article
- ROS-Centered Transcriptomic Regulatory Networks Linking Salinity Stress, Antioxidant Defense and Processability Traits inCurrent issues in molecular biology · 2026Review
- Enhancing validation of case-control omics signatures through "minimalist" single-subject analysis (N-of-1 trials): proof of concept in sepsis.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Dehydrocostus lactone attenuates hepatic steatosis by regulating fatty acid oxidation and lipid metabolism: integrated transcriptomic and metabolomic analysis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
440 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
Abstract
RNA-seq is now the technology of choice for genome-wide differential gene expression experiments, but it is not clear how many biological replicates are needed to ensure valid biological interpretation of the results or which statistical tools are best for analyzing the data. An RNA-seq experiment with 48 biological replicates in each of two conditions was performed to answer these questions and provide guidelines for experimental design. With three biological replicates, nine of the 11 tools evaluated found only 20%-40% of the significantly differentially expressed (SDE) genes identified with the full set of 42 clean replicates. This rises to >85% for the subset of SDE genes changing in expression by more than fourfold. To achieve >85% for all SDE genes regardless of fold change requires more than 20 biological replicates. The same nine tools successfully control their false discovery rate at ≲5% for all numbers of replicates, while the remaining two tools fail to control their FDR adequately, particularly for low numbers of replicates. For future RNA-seq experiments, these results suggest that at least six biological replicates should be used, rising to at least 12 when it is important to identify SDE genes for all fold changes. If fewer than 12 replicates are used, a superior combination of true positive and false positive performances makes edgeR and DESeq2 the leading tools. For higher replicate numbers, minimizing false positives is more important and DESeq marginally outperforms the other tools.
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