Zavala, E. I. | bioRxiv Science

bioRxiv2025

sedimix: A workflow for the analysis of hominin nuclear DNA sequences from sediments

SummarySediment DNA--the ability to extract DNA from archaeological sediments-- is an exciting new frontier in ancient DNA research, offering the potential to study individuals at a given archaeological site without destructive sampling. In recent years, several studies have demonstrated the promise of this approach by recovering hominin DNA from prehistoric sediments, including those dating back to the Middle or Late Pleistocene. However, a lack of open-source workflows for analysis of hominin sediment DNA samples poses a challenge for data processing and reproducibility of findings across studies. Here we introduce a snakemake workflow, sedimix, for processing genomic sequences from archaeological sediment DNA samples to identify hominin sequences and generate relevant summary statistics to assess the reliability of the pipeline. By performing simulations and comparing to published studies, we show that sedimix has high sensitivity and precision. sedimix offers a reliable and adaptable framework to aid in the analysis of sediment DNA datasets and improve reproducibility across studies. Availability and Implementationsedimix is available as an open-source software with the associated code and user manual available at https://github.com/jierui-cell/sedimix ContactJierui Xu (jierui.xu@berkeley.edu) Supplementary informationSupplementary data are available at Bioinformatics online

genetics↗

bioRxiv2024

Benchmarking for genotyping and imputation using degraded DNA for forensic applications across diverse populations

Advancements in sequencing and laboratory technologies have enabled forensic genetic analysis on increasingly low quality and degraded DNA samples. However, existing computational methods applied to genotyping and imputation for generating DNA profiles from degraded DNA have not been tested for forensic applications. Here we simulated sequencing data of varying qualities- coverage, fragment lengths, and deamination patterns-from forty individuals of diverse genetic ancestries. We used this dataset to test the performance of commonly used genotype and imputation methods (SAMtools, GATK, ATLAS, Beagle, and GLIMPSE) on five different SNP panels (MPS- plex, FORCE, two extended kinship panels, and the Human Origins array) that are used for forensic and population genetics applications. For genome mapping and variant calling with degraded DNA, we find use of parameters and methods (such as ATLAS) developed for ancient DNA analysis provides a marked improvement over conventional standards used for next generation sequencing analysis. We find that ATLAS outperforms GATK and SAMtools, achieving over 90% genotyping accuracy for the four largest SNP panels with coverages greater than 10X. For lower coverages, decreased concordance rates are correlated with increased rates of heterozygosity. Genotype refinement and imputation improve the accuracy at lower coverages by leveraging population reference data. For all five SNP panels, we find that using a population reference panel representative of worldwide populations (e.g., the 1000 Genomes Project) results in increased genotype accuracies across genetic ancestries, compared to ancestry-matched population reference panels. Importantly, we find that the low SNP density of commonly used forensics SNP panels can impact the reliability and performance of genotype refinement and imputation. This highlights a critical trade-off between enhancing privacy by using panels with fewer SNPs and maintaining the effectiveness of genomic tools. We provide benchmarks and recommendations for analyzing degraded DNA from diverse populations with widely used genomic methods in forensic casework. HighlightsO_LIBiallelic SNP panels: >92% genotyping accuracy for 10X data with ATLAS C_LIO_LIDegraded DNA impacts accuracy under sequencing depth of 10X coverage C_LIO_LIHigher accuracies across genetic ancestries achieved with a diverse reference panel C_LIO_LILeveraging population reference data is not applicable for small SNP panels C_LIO_LITrade-off between genotype accuracy and privacy when considering SNP panel size C_LI

genetics↗