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

Abstract

AbstractAnalysis of microbial data from archaeological samples is a rapidly growing field with a great potential for understanding ancient environments, lifestyles and disease spread in the past. However, high error rates have been a long-standing challenge in ancient metagenomics analysis. This is also complicated by a limited choice of ancient microbiome specific computational frameworks that meet the growing computational demands of the field. Here, we propose aMeta, an accurate ancient Metagenomic profiling workflow designed primarily to minimize the amount of false discoveries and computer memory requirements. Using simulated ancient metagenomic samples, we benchmark aMeta against a current state-of-the-art workflow, and demonstrate its superior sensitivity and specificity in both microbial detection and authentication, as well as substantially lower usage of computer memory. aMeta is implemented as a Snakemake workflow to facilitate use and reproducibility.