Lessons learned: Recommendations for reproducible paleogenomic data analyses.

Abstract

Paleogenomics is an increasingly data-rich research discipline that has become hugely influential in our understanding of the population history of humans and many other species. Given its reliance on destructive sampling to extract ancient DNA (aDNA) from skeletal remains and other organic sources, minimum reporting standards are crucial to inform the review process, improve transparency and reproducibility, increase the usability of research findings, and maximize the value gained from finite and unique samples. Additionally, paleogenomics researchers routinely face choices that can meaningfully impact results and influence conclusions, including decisions regarding sample usage and parameter options for data processing and analyses. From our collective experience as a paleogenomics research group extensively interacting with other researchers in the field, we identified critical information for key analytical areas required for reproducible research. Our recommendations are compatible with findable, accessible, interoperable, and reusable (FAIR) and collective benefit, authority to control, responsibility, and ethics (CARE) frameworks for data management and include the use of standardized data formats, amendments to standard metadata formats, and the provision of a reporting checklist detailing sample preparation and analytical workflows. Developing detailed documentation and clear reporting of analytical workflows will ensure that transparent, robust, and reproducible conclusions are routinely achieved to warrant confidence in paleogenomics research outcomes.