MAGqual: A standalone pipeline to assess the quality of metagenome-assembled genomes

Abstract

Metagenomics, the whole genome sequencing of microbial communities, has provided insight into complex ecosystems. It has facilitated the discovery of novel microorganisms, explained community interactions, and found applications in various fields. Advances in high-throughput and third-generation sequencing technologies have further fuelled its popularity. Nevertheless, managing the vast data produced and addressing variable dataset quality remain ongoing challenges. Another challenge arises from the number of assembly and binning strategies used across studies. Comparing datasets and analysis tools is complex as it requires a measure of metagenome quality. The inherent limitations of metagenomic sequencing, which often involves sequencing complex communities means community members are challenging to interrogate with traditional culturing methods leading to many lacking reference sequences. The MIMAG standards (Bowers et al., 2017) aim to provide a method to assess metagenome quality for comparison but have not been widely adopted. To bridge this gap, the MAGqual pipeline outlined here offers an accessible way to evaluate metagenome quality and generate metadata on a large scale. MAGqual is built in Snakemake to ensure readability and scalability and its open-source nature promotes accessibility, community development, and ease of updates. Here, we introduce the pipeline MAGqual (metagenome-assembled genome qualifier) and demonstrate its effectiveness at determining metagenomic dataset quality when compared to the MIMAG standards. MAGqual is built in Snakemake, R, and Python and is available under the MIT License on GitHub at https://github.com/ac1513/MAGqual.