Biology subjects

Ramsoe, A. D.

Publications and source records attributed to Ramsoe, A. D..

3 recordsLinked to original sources

bioRxiv2026

Forensic likelihood ratios for shotgun sequencing data from DNA mixtures

Shotgun sequencing of DNA mixtures has the potential to become an important tool in forensic genetics, particularly for degraded samples or complex mixtures. However, the analysis of shotgun-sequenced mixtures is challenged by several confounding factors, including sequencing errors, population structure, the potential presence of relatives, linkage, and linkage disequilibrium. Additionally, the statistical methods currently available for such data are limited. Here, we develop likelihood ratios that summarize the evidence for the presence of a suspects DNA in a mixture, assuming genotypes of the suspect and the victim are available, while accounting for DNA from unknown contributors modeled either as general population contamination or as a single contaminating individual. We show that these likelihood ratios are not robust to model assumptions and can falsely support the presence of the suspects DNA when the true contributor is even a distant relative. We therefore propose a combined evidence score, {Lambda}: the likelihood ratio minimized over contamination models, reported only when the evidence favors the suspect over a hypothesized sibling by at least a factor of C (default C = 10) under at least one contamination model, and otherwise set to 1 (log {Lambda} = 0). In simulations based on 1000 Genomes data, {Lambda} retains nearly all power to detect true contributors at mixture proportions as low as 5% while eliminating the false positives produced by model mis-specification, population structure, and by untested relatives. The methods are implemented in the open-source program DNAMIXTURE, providing the forensic and population genetics community a resource for robustly identifying small individual contributions to a DNA mixture.

bioinformatics↗

bioRxiv2025

Environmental DNA Reveals Reykjavik's Human and Ecological History

Iceland was among the last large islands settled by humans, with colonization (Landnam) in the late 9th century CE (Common Era) and is often portrayed as an ecological disaster driven by the Norse settlers. Here, we revisit this narrative through environmental DNA (eDNA) and multiproxy analyses of sediment cores from Lake Tjornin in central Reykjavik, one of Icelands earliest and longest-occupied settlements. Originally a marine embayment, Tjornin became a freshwater lake around 660 CE. Our record reveals a human presence decades before the long-accepted arrival date of 877 CE, marked by the Landnam volcanic tephra. Early settlement brought livestock, barley cultivation, and other introduced taxa that enhanced nutrient cycling and unexpectedly increased local biodiversity. Contrary to the conventional view of rapid deforestation, eDNA shows that birch and willow expanded during the settlement period, likely supported by deliberate management. Pronounced ecological and land use shifts occurred after 1200 CE, but these were coeval with the Little Ice Age cooling, compounded by volcanic eruptions, storm surges, and plague, rather than anthropogenic degradation. Crop cultivation ceased, arboreal taxa retracted, and grazing pressure maintained open landscapes. Even more profound ecological changes came after c. 1750 CE with urbanization and industrialization, as wastewater discharge, heavy-metal pollution, and fossil fuel use reshaped Tjornins ecosystem. These findings challenge the prevailing model of Norse-induced environmental collapse, revealing instead a dynamic human-environment relationship shaped by both cultural practices and external stressors. By applying eDNA to a long-occupied urban catchment, we demonstrate the power of genomic methods to refine settlement chronologies, reassess ecological baselines and changes, and integrate natural and cultural histories. This approach offers a model for revisiting human-environment interactions in urban centers worldwide.

genomics↗

bioRxiv2023

Revisiting the Briggs ancient DNA damage model: a fast regression method to estimate postmortem damage

MotivationOne essential initial step in the analysis of ancient DNA is to authenticate its ancientness to ensure reliable conclusions. That is, meticulously assessing whether next-generation sequencing reads exhibit ancient characteristics, with a particular focus on the postmortem damage (PMD) signal induced by cytosine deamination in the fragments termini. We present a novel statistical method implementation in a fast multithreaded program ngsBriggs that enables the rapid quantification of PMD by calculation of the Briggs ancient damage model parameters (Briggs parameters). ResultsUsing a fast multinomial regression approach, ngsBriggs accurately models the Briggs parameters, quantifying the PMD signal from single and double-stranded DNA regions. We revisit and extend the original Briggs model, with ngsBriggs modeling PMD signals for contemporary sequencing platforms. Furthermore, ngsBriggs asserts itself as a reliable and consistent tool, by accurately estimating the Briggs parameters across a variety of contamination levels. The classification accuracy of ngsBriggs significantly exceeds the current tool available when discerning ancient-from modern sequencing reads to decontaminate samples. Our novel method and implementation ngsBriggs outperforms existing tools regarding computational speed and accuracy, establishing its practicality and usability. Our tool, ngsBriggs offers a practical and accurate toolset for researchers seeking to authenticate ancient DNA and improve the quality of their data. Availabilityhttps://github.com/lz398/metadamage_briggs

bioinformatics↗