【摘要】 肠道病毒对肠道微生物群系的种群结构、细菌性状乃至人体健康都有十分重要的影响,但相比肠道细菌,人们对其的研究和了解仍然很缺乏.高通量测序技术以及机器学习、深度学习等方法的快速发展,为从组学途径深入研究肠道病毒提供了契机.本文针对当前肠道病毒组学领域以噬菌体、真核病毒等为对象的高通量数据,总结并分析了近年来数据挖掘和分析的共性方法和技术的发展,梳理了一系列相关的生物信息学方法和技术,其中大多适用于基于宏基因组或宏病毒组两种策略的病毒组学分析.同时,针对目前实际生物学问题和临床问题的复杂性,人工智能方法在生物信息学领域的广泛运用,以及未来三代测序技术可能的广泛使用,讨论了病毒组学数据挖掘与数据分析方法面临的问题和挑战.更多还原

【Abstract】 The gut virome plays a very important role in the microbial community structure, the bacterial traits, and even the human health.However, it is still poorly understood compared to the bacterial metagenome among the gut microbiome. The rapid development of high-throughput sequencing technologies, machine learning, deep learning, and other methods provides an opportunity for in-depth study of gut virome. With a special focus on the high-throughput genomic data of bacteriophages and eukaryotic viruses, this paper reviewed those general character key technologies of data mining and data analysis in current gut virome research, most of which are applied in both viral metagenomes and metaviromes. In view of the complexity of biological problems and clinical trials, as well as the application of third-generation sequencing and artificial intelligence methods, we also discussed the challenges and opportunities for these tools and techniques in gut virome.更多还原