The Sequence Read Archive: a decade more of explosive growth
Open accessAbstract
The Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra/) stores raw sequencing data and alignment information to enhance reproducibility and facilitate new discoveries through data analysis. Here we note changes in storage designed to increase access and highlight analyses that augment metadata with taxonomic insight to help users select data. In addition, we present three unanticipated applications of taxonomic analysis.
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The Short Read Archive of biological sequence data holds over 14 petabytes of information, which is more than 1,000 times larger than the dataset used to train ChatGPT.
“For instance, the Short Read Archive of biological sequence data holds over 14 petabytes of information,112 which is more than 1,000 times larger than the dataset used to train ChatGPT.113”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [112]
The Introduction describes the Sequence Read Archive as holding data at petabyte scale. The Figure 1 caption (following the Conclusion) reports approximately 25.6 petabase pairs, not over 14 petabytes of stored information. The ChatGPT comparison has a separate citation (113) and is not attributed to this source. (LLM, conf=0.82)
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Cites
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How to build the virtual cell with artificial intelligence: Priorities and opportunities
Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393 · see citing works
“For instance, the Short Read Archive of biological sequence data holds over 14 petabytes of information, 112 which is more than 1,000 times larger than the dataset used to train ChatGPT.”
Generative modeling of single-cell gene expression for dose-dependent chemical perturbations
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- Authors
- Katz, K., Shutov, O., Lapoint, R., Kimelman, M., Brister, J. R., & O’Sullivan, C.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Katz, Kenneth
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-11-24
- Volume
- 50
- Issue
- D1
- Pages
- D387-D390
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC8728234
- OpenAlex
- W3216588755
- Cited by
- 424
- References
- 13
- Citations by year
- 2026: 103 · 2025: 113 · 2024: 111 · 2023: 72 · 2022: 23 · 2021: 1 · 2020: 1
- FWCI
- 16.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- Locations
- Nucleic Acids Research (publishedVersion, public-domain)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- Sequence Read Archive, raw sequencing data, data storage, data access, metadata, taxonomic analysis, reproducibility, data curation
- MeSH terms
- Metadata, SARS-CoV-2, Bacteria, Base Sequence, Phylogeny, Software, Viruses, Reproducibility of Results, Sequence Analysis, RNA, Internet, Databases, Genetic, High-Throughput Nucleotide Sequencing
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