Comparison of Illumina NovaSeq 6000, GeneMind SURFSeq 5000, Salus Evo, and MGI DNBSEQ-G400 for Ancient DNA Whole-Genome Sequencing

Abstract

Background: Ancient DNA (aDNA) research is one of the biological fields that has been transformed by the development of next-generation sequencing (NGS). To date, Illumina sequencing has dominated aDNA research. However, its high costs are driving the adoption of alternative sequencing platforms. Combining data generated on different platforms may introduce artifacts arising from platform-specific errors. This study systematically compared the performance of four sequencing platforms (Illumina NovaSeq 6000, GeneMind SURFSeq 5000, Salus Evo, and MGI DNBSEQ-G400) for whole-genome aDNA sequencing using the same set of six samples and libraries. Methods: Single-stranded libraries, prepared with and without enzymatic damage repair, were generated from six human aDNA specimens recovered from Phanagoria polis and sequenced on all four platforms. Data were subsampled to equal read counts per library, mapped to the human reference genome, and compared using standard NGS quality metrics. Population genetic analyses, including principal component analysis (PCA) and ADMIXTURE, were performed to assess potential platform-specific biases. Results: All platforms produced raw sequencing data of acceptable quality. Only minor differences were observed among platforms in standard NGS metrics. The MGI platform showed a shift toward longer sequenced fragment lengths compared with Illumina and the Illumina-like platforms. Post-mortem damage patterns, particularly C>T substitutions, were highly consistent across all platforms. PCA and ADMIXTURE analyses revealed no evidence of platform-specific bias: results from all platforms clustered tightly together, and platform choice had no significant effect on ancestry component estimates. Conclusions: Our findings demonstrate that the GeneMind, Salus, and MGI sequencing platforms are comparable to Illumina for paleogenomic research. Moreover, aDNA datasets generated on these platforms can be combined for downstream analyses without introducing detectable bias. However, the fragment-size shift observed on the MGI platform warrants caution and adaptation when working with highly degraded or low-endogenous-content samples.