Extreme climatic activities, such as typhoons, are widely known to disrupt our natural environment. In particular, studies have revealed that typhoon-induced perturbations can result in several long-term effects on various ecosystems. In this study, we have conducted a 2-year metagenomic survey to investigate the microbial and viral community dynamics associated with environmental changes and seasonal variations in an enclosed freshwater reservoir subject to episodic typhoons. We found that the microbial community structure and the associated metagenomes continuously changed, where microbial richness increased after typhoon events and decreased during winter. Among the environmental factors that influenced changes in the microbial community, precipitation was considered to be the most significant. Similarly, the viral community regularly showed higher relative abundances and diversity during summer in comparison to winter, with major variations happening in several viral families including Siphoviridae, Myoviridae, Podoviridae and Microviridae. Interestingly, we also found that the precipitation level was associated with the terrestrial viral abundance in the reservoir. In contrast to the dynamic microbial community (L-divergence 0.73 ± 0.25), we found that microbial metabolic profiles were relatively less divergent (L-divergence 0.24±0.04) at the finest metabolic resolution. This study provides for the first time a glimpse at the microbial and viral community dynamics of a subtropical freshwater ecosystem, adding a comprehensive set of new knowledge to aquatic environments.
Benchmarking results showed that ViQuaS outperformed three other previously published methods named ShoRAH, QuRe and PredictHaplo, with improvements of at least 3.1-53.9% in recall, 0-12.1% in precision and 0-38.2% in F-score in terms of strain sequence assembly and improvements of at least 0.006-0.143 in KL-divergence and 0.001-0.035 in root mean-squared error in terms of strain frequency estimation, over the next-best algorithm under various simulation settings. We also applied ViQuaS on a real read set derived from an in vitro human immunodeficiency virus (HIV)-1 population, two independent datasets of foot-and-mouth-disease virus derived from the same biological sample and a real HIV-1 dataset and demonstrated better results than other methods available.
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