MCycDB: A curated database for comprehensively profiling methane cycling processes of environmental microbiomes

• Published in: Molecular Ecology Resources
• Main Authors: Qian, Lu, Yu, Xiaoli, Zhou, Jiayin, Gu, Hang, Ding, Jijuan, Peng, Yisheng, He, Qiang, Tian, Yun, Liu, Jihua, Wang, Shanquan, Wang, Cheng, Shu, Longfei, Yan, Qingyun, He, Jianguo, Liu, Guangli, Tu, Qichao, He, Zhili
• Other Authors: National Key Research and Development Program of China, National Natural Science Foundation of China
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2022
• Subjects: permafrost
• Online Access: http://dx.doi.org/10.1111/1755-0998.13589; https://onlinelibrary.wiley.com/doi/pdf/10.1111/1755-0998.13589; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/1755-0998.13589

Abstract Methane is a critical greenhouse gas with significant impacts on environmental and global change. However, CH 4 cycling processes and coupling mechanisms with the biogeochemical cycling of carbon, nitrogen, sulfur and metals in the environment remain elusive. To fill such knowledge gaps, we constructed a manually curated methane cycling database (MCycDB) for comprehensive and accurate analysis of methane cycling microbial communities. MCycDB contains 298 methane cycling gene families covering 10 methane metabolism pathways with 610,208 representative sequences, and associated reference sequences from the NCBI RefSeq database with 48 phyla and 2,197 genera, and five phyla and 100 genera for bacteria and archaea, respectively. Also, homologous groups from public orthology databases were identified and included in MCycDB to reduce false positive assignments. We applied MCycDB to profile methane cycling gene families and associated taxonomic groups from various environments. Gene families involved in methanogenesis were abundant in hot spring sediment and less abundant in freshwater, whereas the ones involved in aerobic oxidation of methane were abundant in permafrost and peatland. This study demonstrates that MCycDB is a useful tool for studying microbially‐driven methane cycling processes with high specificity, coverage and accuracy.