肠道病毒组学数据挖掘与分析方法的进展及挑战  

Data mining and analysis techniques for gut virome:the prospects and challenges

作　　者：江小青 李墨 尹衡闯 郭倩[1] 谭洁[1] 吴姝芳 王春晖[1] 朱怀球[1] JIANG XiaoQing;LI Mo;YIN HengChuang;GUO Qian;TAN Jie;WU ShuFang;WANG ChunHui;ZHU HuaiQiu(Department of Biomedical Engineering,College of Future Technology,and Center for Quantitative Biology Peking University,Beijing 100871,China)

机构地区：[1]北京大学未来技术学院生物医学工程系,北京大学定量生物学中心,北京100871

出　　处：《中国科学：生命科学》2023年第5期647-659,共13页Scientia Sinica(Vitae)

基　　金：国家自然科学基金(批准号:32070667,31671366);国家重点研发计划重点专项(批准号:2021YFC2300300,2017YFC1200205)资助。

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.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.

关 键 词：肠道病毒组 宏基因组 组学分析 数据挖掘 

分 类 号：R373[医药卫生—病原生物学]