subSeq: Determining Appropriate Sequencing Depth Through Efficient Read Subsampling
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Motivation: Next-generation sequencing experiments, such as RNA-Seq, play an increasingly important role in biological research. One complication is that the power and accuracy of such experiments depend substantially on the number of reads sequenced, so it is important and challenging to determine the optimal read depth for an experiment or to verify whether one has adequate depth in an existing experiment. Results: By randomly sampling lower depths from a sequencing experiment and determining where the saturation of power and accuracy occurs, one can determine what the most useful depth should be for future experiments, and furthermore, confirm whether an existing experiment had sufficient depth to justify its conclusions. We introduce the subSeq R package, which uses a novel efficient approach to perform this subsampling and to calculate informative metrics at each depth. Availability and Implementation: The subSeq R package is available at http://github.com/StoreyLab/subSeq/. Contact: dgrtwo@princeton.edu or jstorey@princeton.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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Increasing sequencing depth can improve statistical power for lowly expressed genes, and for any given sample there is a sequencing level at which further power improvement is best achieved by increasing replicate number.
“Increasing sequencing depth also can improve statistical power for lowly expressed genes [10, 194], and for any given sample there exists a level of sequencing at which power improvement is best achieved by increasing the number of replicates [195].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [194]
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A survey of best practices for RNA-seq data analysis
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- Authors
- Robinson, D. G., & Storey, J. D.
- Affiliations
- Princeton University (US)
- Corresponding author
- Robinson, David G.
- Storey, John D.
- ORCID
- Storey, John D.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-09-03
- Volume
- 30
- Issue
- 23
- Pages
- 3424-3426
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC4296149
- OpenAlex
- W2150136034
- Cited by
- 57
- References
- 18
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 6 · 2023: 4 · 2022: 4 · 2021: 2 · 2020: 7 · 2019: 4 · 2018: 2 · 2017: 5 · 2016: 6 · 2015: 4 · 2014: 1
- FWCI
- 2.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (R01HG002913)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- sequencing depth, RNA-seq, statistical power, next-generation sequencing
- MeSH terms
- Animals, Software, Sequence Analysis, RNA, Rats, High-Throughput Nucleotide Sequencing
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