MeTAline: enabling reproducible and scalable metagenomic analyses

The taxonomic and functional characterization of microbial communities inhabiting a given niche can elucidate associations between the microbiota and relevant variables, including health and disease. As compared to metabarcoding, shotgun metagenomic sequencing, which analyzes all DNA present in a sample, offers superior taxonomic resolution and additionally enables the inference of functional capabilities encoded within the microbial community of interest. However, this approach requires the use of diverse computational tools and substantial computational resources. Here, we present MeTAline, a bioinformatics pipeline for the analysis of shotgun metagenomics data. Implemented in Snakemake, MeTAline provides an efficient and reproducible workflow encompassing read trimming and filtering, host read removal, taxonomic classification via both <i>k</i>-mer and gene marker-based methodologies, and extensive functional annotation. Containerization in Docker and Singularity ensures ease of installation, portability, and reproducibility. Finally, the pipeline's architecture supports high parallelization, rendering it suitable for both local and high-performance computing environments. MeTAline is freely available at https://github.com/Gabaldonlab/meTAline under an open-source GNU GPL v3.0 license.