aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow

作者：Zoé Pochon, Nora Bergfeldt, Emrah Kırdök, Mário Vicente, Thijessen Naidoo, Tom van der Valk, N. Ezgi Altınışık, Maja Krzewińska, Love Dalén, Anders Götherström, Claudio Mirabello, Per Unneberg, Nikolay Oskolkov · 发表于：Genome biology · 年份：2023 · DOI：10.1186/s13059-023-03083-9 · 被引用次数：57 · 研究领域：Genomics and Phylogenetic Studies、Metabolomics and Mass Spectrometry Studies、Forensic and Genetic Research

Analysis of microbial data from archaeological samples is a growing field with great potential for understanding ancient environments, lifestyles, and diseases. However, high error rates have been a challenge in ancient metagenomics, and the availability of computational frameworks that meet the demands of the field is limited. Here, we propose aMeta, an accurate metagenomic profiling workflow for ancient DNA designed to minimize the amount of false discoveries and computer memory requirements. Using simulated data, we benchmark aMeta against a current state-of-the-art workflow and demonstrate its superiority in microbial detection and authentication, as well as substantially lower usage of computer memory.