ArticleScientific data2024
Improving data archiving practices in ancient genomics.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Lessons learned: Recommendations for reproducible paleogenomic data analyses.American journal of human genetics · 2025Review
- Comprehensive review of understanding ancient dietary habits using modern analytical techniques.Food chemistry. Molecular sciences · 2025Review
- A molecular specimen bank for contemporary and future study captures landscape-scale biodiversity baselines before Klamath River dam removal.Scientific reports · 2025Article
- Pre-processing of paleogenomes: mitigating reference bias and postmortem damage in ancient genome data.Genome biology · 2025Article
- Ancient DNA data hold insights into past organisms and ecosystems - handle them with more care.Nature · 2024Article
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Authors and funding
1 author.
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Abstract
Ancient DNA is producing a rich record of past genetic diversity in humans and other species. However, unless the primary data is appropriately archived, its long-term value will not be fully realised. I surveyed publicly archived data from 42 recent ancient genomics studies. Half of the studies archived incomplete datasets, preventing accurate replication and representing a loss of data of potential future use. No studies met all criteria that could be considered best practice. Based on these results, I make six recommendations for data producers: (1) archive all sequencing reads, not just those that aligned to a reference genome, (2) archive read alignments too, but as secondary analysis files, (3) provide correct experiment metadata on samples, libraries and sequencing runs, (4) provide informative sample metadata, (5) archive data from low-coverage and negative experiments, and (6) document archiving choices in papers, and peer review these. Given the reliance on destructive sampling of finite material, ancient genomics studies have a particularly strong responsibility to ensure the longevity and reusability of generated data.
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Registered trials
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