Run-length compressed metagenomic read classification with SMEM-finding and tagging

Metagenomic read classification is a fundamental task in computational biology, yet it remains challenging due to the scale, diversity, and complexity of sequencing datasets. We propose a novel, run-length compressed index based on the move structure that enables efficient multi-class metagenomic classification in $O\left(r\right)$ space, where $r$ is the number of character runs in the BWT of the reference text. Our method identifies all super-maximal exact matches (SMEMs) of length at least $L$ between a read and the reference dataset and associates each SMEM with one class identifier using a sampled tag array. A consensus algorithm then compacts these SMEMs with their class identifier into a single classification per read. We are the first to perform run-length compressed read classification based on full SMEMs instead of semi-SMEMs. We evaluate our approach on both long and short reads in two conceptually distinct datasets: a large bacterial pan-genome with few metagenomic classes and a smaller 16S rRNA gene database spanning thousands of genera or classes. Our method consistently outperforms SPUMONI 2 in accuracy and runtime while maintaining the same asymptotic memory complexity of $O\left(r\right)$ . Compared to Cliffy, we demonstrate better memory efficiency while achieving superior accuracy on the simpler dataset and comparable performance on the more complex one. Overall, our implementation carefully balances accuracy, runtime, and memory usage, offering a versatile solution for metagenomic classification across diverse datasets. The open-source C++11 implementation is available at https://github.com/biointec/tagger under the AGPL-3.0 license.