DipSkmer: Reference-free population genomics with diploid genome skims.

DipSkmer provides more accurate estimates of genetic diversity (θ) for diploid genome skims, outperforming existing methods like Mash and Skmer at low genetic distances.

• Why it matters: Accurate, cost-effective monitoring of genetic diversity is crucial for conservation and ecological studies, but current methods struggle with diploid heterozygosity and low-coverage data, limiting their utility.
• What they did: The authors developed DipSkmer by applying coalescent theory to model the impact of heterozygosity, coverage, and sequencing error on the Jaccard index, enabling estimation of θ from genome skims in diploid samples.
• The result: Analyses of simulated and empirical datasets show DipSkmer surpasses existing alignment-free methods at low genetic distances (< 2%) and closely matches the accuracy of reference-based tools like ANGSD, facilitating improved population genetic analyses.