Biology subjects

Light-Maka, I.

Publications and source records attributed to Light-Maka, I..

4 recordsLinked to original sources

bioRxiv2026

AncientMetagenomeDir dating metadataset highlights need for standardised radiocarbon reporting in ancient DNA

Ancient DNA is a valuable data source for the understanding of our past. However, to effectively interpret this data, it is essential to know the age of the samples from which the DNA is obtained. Although the field of palaeogenomics has been recognised for its robust open data sharing practices, dating information associated with analysed samples is not reported consistently across palaeogenomic studies, nor is it included as metadata in most genetic data repositories. Here, we describe the addition of standardised precise dating information for ancient microbial genomes into the AncientMetagenomeDir metadata repository of published ancient metagenomic samples. This extension currently includes dating information for over 700 ancient microbial genomic datasets, of which 333 are dated using historical, contextual, or stratigraphic methods, and 405 are radiocarbon dated. We quantitatively assess the quality of radiocarbon date reporting and find that, despite established reporting conventions, radiocarbon dating information is often reported inconsistently across ancient metagenomic studies. This new resource provides ancient microbial researchers with standardised dating information that facilitates more accurate and consistent analysis of metagenomic sequencing data. The dataset also highlights the need for greater standardisation of radiocarbon date reporting in original publications in order to allow effective reuse of this and future ancient microbial data.

bioinformatics↗

bioRxiv2025

High-accuracy SNV calling for bacterial isolates using deep learning with AccuSNV

Accurate detection of mutations within bacterial species is critical for fundamental studies of microbial evolution, reconstructing transmission events, and identifying antimicrobial resistance mutations. While many tools have been developed to identify single nucleotide variants (SNVs) from whole-genome sequencing, they often suffer from high false positive rates due to the complexity of bacterial genomes and the need for different filtering cutoffs across sample types and sequencing depths. As datasets increase in size, the manual filtering required for high accuracy presents a significant obstacle. Here, we present AccuSNV, a novel deep learning-based tool for high-precision and automated bacterial SNV calling. Unlike traditional methods that process one sample at a time, AccuSNV leverages a convolutional neural network (CNN) that integrates alignment information across multiple samples, enhancing precision through learned across-sample patterns. We evaluated AccuSNV against seven popular SNV calling tools using simulated data from six bacterial species with varied sequencing depths, numbers of isolates, mutations, and divergence levels. To further validate its real-world utility, we tested AccuSNV on multiple curated bacterial datasets containing reported SNVs. In both simulated and real-world scenarios, AccuSNV consistently achieved the best performance. Moreover, AccuSNV provides comprehensive user-friendly downstream analysis modules and outputs, including mutation annotation information, phylogenetic inference, dN/dS calculations, and optional manual filtering. Together with the automated deep learning-based calling, these features make AccuSNV broadly accessible to users with different levels of computational expertise.

bioinformatics↗

bioRxiv2025

Probing the zooarchaeological record across time and space for ancient pathogens

Zoonoses are among the greatest threats to human health, with many zoonotic pathogens believed to have emerged during prehistory. Palaeomicrobiological investigations of the zooarchaeological record hold potential to uncover the reservoirs, host ranges, and host adaptations of zoonotic pathogens but face challenges in identifying promising specimens and pathogen DNA preservation. We performed palaeopathological and genetic examinations of 346 skeletal elements from domesticated and wild animals collected from 34 Eurasian sites dating across the last six millennia. We identified 68 signatures of ancient (opportunistic) pathogens, including the important zoonotic pathogen Salmonella enterica, and found support that palaeopathological lesions provide guidance for specimen selection. For two pathogen species, Erysipelothrix rhusiopathiae and Streptococcus lutetiensis, we confirmed their ancient authenticity using phylogenetics, showcasing an approach to explore the relationship between ancient low-coverage genomes and their modern-day relatives. Our work presents a pathway to understanding prehistoric zoonotic diseases by integrating zooarchaeological, palaeopathological, and genetic data.

microbiology↗

bioRxiv2025

Bronze Age Yersinia pestis genome from sheep sheds light on hosts and evolution of a prehistoric plague lineage

Most human pathogens are of zoonotic origin. Many emerged during prehistory, coinciding with domestication providing more opportunities for spillover from original host species. However, we lack direct evidence linking past animal reservoirs and human infections. Here we present a Yersinia pestis genome recovered from a 3rd millennium BCE domesticated sheep from the Eurasian Steppe belonging to the Late Neolithic Bronze Age (LNBA) lineage, until now exclusively identified in ancient humans across Eurasia. We show that this ancient lineage underwent ancestral gene decay paralleling extant lineages, but evolved under distinct selective pressures contributing to its lack of geographic differentiation. We collect evidence supporting a scenario where the LNBA lineage, unable to efficiently transmit via fleas, spread from an unidentified reservoir to humans via sheep and likely other domesticates. Collectively, our results connect prehistoric livestock with infectious disease in humans and showcase the power of moving paleomicrobiology into the zooarcheological record.

genetics↗