AsgeneDB: a curated orthology arsenic metabolism gene database and computational tool for metagenome annotation

Arsenic (As) is the most ubiquitous toxic metalloid in nature. Microbe-mediated As metabolism plays an important role in global As biogeochemical processes, greatly changing its toxicity and bioavailability. While metagenomic sequencing may advance our understanding of the As metabolism capacity of microbial communities in different environments, accurate metagenomic profiling of As metabolism remains challenging due to low coverage and inaccurate definitions of As metabolism gene families in public orthology databases. Here we developed a manually curated As metabolism gene database (AsgeneDB) comprising 400 242 representative sequences from 59 As metabolism gene families, which are affiliated with 1653 microbial genera from 46 phyla. AsgeneDB achieved 100% annotation sensitivity and 99.96% annotation accuracy for an artificial gene dataset. We then applied AsgeneDB for functional and taxonomic profiling of As metabolism in metagenomes from various habitats (freshwater, hot spring, marine sediment and soil). The results showed that AsgeneDB substantially improved the mapping ratio of short reads in metagenomes from various environments. Compared with other databases, AsgeneDB provides more accurate, more comprehensive and faster analysis of As metabolic genes. In addition, we developed an R package, Asgene, to facilitate the analysis of metagenome sequencing data. Therefore, AsgeneDB and the associated Asgene package will greatly promote the study of As metabolism in microbial communities in various environments.

Automated summary

Arsenic is a widely distributed toxic metalloid in nature. Microbe-mediated arsenic metabolism plays a crucial role in global arsenic biogeochemical cycles. However, accurate metagenomic profiling of arsenic metabolism in microbial communities is challenging. In this study, we developed a manually curated As metabolism gene database (AsgeneDB) and applied it for functional and taxonomic profiling of arsenic metabolism in metagenomes from various environments. Our results demonstrated that AsgeneDB significantly improved the mapping ratio and provided more accurate and comprehensive analysis of arsenic metabolic genes compared to other databases.