Song Xinwei, Li Yiqun, Stirling Erinne, Zhao Kankan, Wang Binhao, Zhu Yongguan, Luo Yongming, Xu Jianming, Ma Bin
Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310000, China.
Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Zhejiang University, Hangzhou 310000, China.
NAR Genom Bioinform. 2022 Nov 1;4(4):lqac080. doi: 10.1093/nargab/lqac080. eCollection 2022 Dec.
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.
砷（As）是自然界中分布最广泛的有毒类金属。微生物介导的砷代谢在全球砷生物地球化学过程中发挥着重要作用，极大地改变了其毒性和生物利用度。虽然宏基因组测序可能会增进我们对不同环境中微生物群落砷代谢能力的理解，但由于公共直系同源数据库中砷代谢基因家族的覆盖率低和定义不准确，准确的砷代谢宏基因组分析仍然具有挑战性。在此，我们开发了一个人工整理的砷代谢基因数据库（AsgeneDB），该数据库包含来自59个砷代谢基因家族的400242条代表性序列，这些序列隶属于46个门的1653个微生物属。对于一个人工基因数据集，AsgeneDB的注释灵敏度达到100%，注释准确率达到99.96%。然后，我们将AsgeneDB应用于来自各种栖息地（淡水、温泉、海洋沉积物和土壤）的宏基因组中砷代谢的功能和分类分析。结果表明，AsgeneDB显著提高了来自各种环境的宏基因组中短读段的映射率。与其他数据库相比，AsgeneDB能对砷代谢基因进行更准确、更全面且更快的分析。此外，我们开发了一个R包Asgene，以促进宏基因组测序数据的分析。因此，AsgeneDB和相关的Asgene包将极大地推动对各种环境中微生物群落砷代谢的研究。
