Coskuner-Weber Orkid, Alpsoy Semih, Yolcu Ozgur, Teber Egehan, de Marco Ario, Shumka Spase
Turkish-German University, Molecular Biotechnology, Sahinkaya Caddesi, No. 106, Beykoz, Istanbul 34820, Turkey.
Turkish-German University, Molecular Biotechnology, Sahinkaya Caddesi, No. 106, Beykoz, Istanbul 34820, Turkey.
Comput Biol Chem. 2025 Oct;118:108444. doi: 10.1016/j.compbiolchem.2025.108444. Epub 2025 Apr 2.
The burgeoning field of aquaculture has become a pivotal contributor to global food security and economic growth, presently surpassing capture fisheries in aquatic animal production as evidenced by recent statistics. However, the dense fish populations inherent in aquaculture systems exacerbate abiotic stressors and promote pathogenic spread, posing a risk to sustainability and yield. This study delves into the transformative potential of metagenomics, a method that directly retrieves genetic material from environmental samples, in elucidating microbial dynamics within aquaculture ecosystems. Our findings affirm that metagenomics, bolstered by tools in big data analytics, bioinformatics, and machine learning, can significantly enhance the precision of microbial assessment and pathogen detection. Furthermore, we explore quantum computing's emergent role, which promises unparalleled efficiency in data processing and model construction, poised to address the limitations of conventional computational techniques. Distinct from metabarcoding, metagenomics offers an expansive, unbiased profile of microbial biodiversity, revolutionizing our capacity to monitor, predict, and manage aquaculture systems with high accuracy and adaptability. Despite the challenges of computational demands and variability in data standardization, this study advocates for continued technological integration, thereby fostering resilient and sustainable aquaculture practices in a climate of escalating global food requirements.
蓬勃发展的水产养殖领域已成为全球粮食安全和经济增长的关键贡献者，最近的统计数据表明，目前水产养殖在水生动物产量方面已超过捕捞渔业。然而，水产养殖系统中固有的高密度鱼群加剧了非生物应激源并促进了病原体传播，对可持续性和产量构成风险。本研究深入探讨了宏基因组学的变革潜力，这是一种直接从环境样本中获取遗传物质的方法，用于阐明水产养殖生态系统中的微生物动态。我们的研究结果证实，借助大数据分析、生物信息学和机器学习工具的宏基因组学可以显著提高微生物评估和病原体检测的精度。此外，我们探讨了量子计算的新兴作用，它有望在数据处理和模型构建方面实现无与伦比的效率，有望解决传统计算技术的局限性。与元条形码不同，宏基因组学提供了一个广泛的、无偏见的微生物生物多样性概况，彻底改变了我们以高精度和适应性监测、预测和管理水产养殖系统的能力。尽管存在计算需求和数据标准化变异性的挑战，但本研究主张持续进行技术整合，从而在全球粮食需求不断增加的情况下促进有弹性和可持续的水产养殖实践。
