MAT-classifier: A memory-efficient pipeline for accurate genus level profiling from ancient metagenomic data

Background: Advances in sequencing technology have expanded opportunities to recover microbial DNA from ancient samples and reconstruct past environments and host-microbe interactions. However, the field remains constrained by computational challenges and accuracy problems, as rare ancient microbial DNA must be distinguished from abundant modern contaminants. Moreover, existing pipelines demand substantial computational resources, particularly memory, limiting their accessibility. Results: Here, we present MAT-classifier, a genus-level profiling workflow for detecting ancient microbial taxa from metagenomic projects, designed to reduce computational requirements while increasing accuracy. Unlike its counterparts, MAT-classifier first consolidates candidate references at the genus level and then performs independent alignments using conventional short-read aligners instead of metagenomic aligners. Using simulated datasets, we showed that this approach achieves more accurate classification of ancient taxa while requiring substantially less memory and shorter runtime than a modern counterpart, the aMeta pipeline. Benchmarking on multiple empirical ancient datasets further confirmed its low memory footprint and practical utility. Conclusions: MAT-classifier provides a reliable, computationally efficient, and accessible framework for ancient microbiome profiling. It lowers computational barriers while maintaining robust classification performance, facilitating broader application of ancient microbial DNA analysis.