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Donglin Wang

Publications and source records attributed to Donglin Wang.

2 recordsLinked to original sources
PubMed2026

Unraveling critical role of photosynthetic bacteria in sustaining aquatic microbial community stability and function through large-scale genomic data analyses.

The application of photosynthetic bacteria (PB) in water remediation has demonstrated exceptional advantages in terms of high efficiency and low-carbon benefits. However, the limited understanding of PB across natural aquatic environments has constrained the rational development of this strategy. Here, we analyzed 3198 genomic sequencing samples from seven types of natural aquatic ecosystems to investigate the distribution and functions of 42 PB genera commonly used in water remediation. The results showed that the average abundance of the targeted PB reached 9.83 %, with the highest value of 14.93 % observed in River, while Lake harbored the greatest PB genus diversity. PB genera exhibited high sensitivity to salinity, with Rhodoferax dominating freshwater habitats, whereas Rhodovulum was predominant in marine environments. Notably, co-occurrence network analysis revealed that PB were closely associated with microbial community stability and optimized interspecific interactions. Aquatic microbial communities with high PB abundance were characterized by efficient division-of-labor modules, accompanied by enhanced PB-associated functional potential for carbon fixation, denitrification, and sulfur oxidation. In summary, this study systematically elucidates the regional biogeographical patterns and ecological roles of PB in natural aquatic environments, providing a comprehensive scientific basis and theoretical guidance for the development and practical application of PB-based water remediation technologies.

Bacteria↗
PubMed2025

GiantHunter: accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree search.

MOTIVATION: Nucleocytoplasmic large DNA viruses (NCLDVs) are notable for their large genomes and extensive gene repertoires, which contribute to their widespread environmental presence and critical roles in processes such as host metabolic reprogramming and nutrient cycling. Metagenomic sequencing has emerged as a powerful tool for uncovering novel NCLDVs in environmental samples. However, identifying NCLDV sequences in metagenomic data remains challenging due to their high genomic diversity, limited reference genomes, and shared regions with other microbes. Existing alignment-based and machine learning methods struggle with achieving optimal trade-offs between sensitivity and precision. RESULTS: In this work, we present GiantHunter, a reinforcement learning-based tool for identifying NCLDVs from metagenomic data. By employing a Monte Carlo tree search strategy, GiantHunter dynamically selects representative non-NCLDV sequences as the negative training data, enabling the model to establish a robust decision boundary. Benchmarking on rigorously designed experiments shows that GiantHunter achieves high precision while maintaining competitive sensitivity, improving the F1-score by 10% and reducing computational cost by 90% compared to the second-best method. To demonstrate its real-world utility, we applied GiantHunter to 60 metagenomic datasets collected from six cities along the Yangtze River, located both upstream and downstream of the Three Gorges Dam. The results reveal significant differences in NCLDV diversity correlated with proximity to the dam, likely influenced by reduced flow velocity caused by the dam. These findings highlight GiantHunter's potential to advance our understanding of NCLDVs and their ecological roles in diverse environments. AVAILABILITY AND IMPLEMENTATION: The source code of GiantHunter is available via: https://github.com/FuchuanQu/GiantHunter.

Metagenomics↗