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利用下一代测序数据和生物信息学方法解析人类微生物组。

Deciphering the human microbiome using next-generation sequencing data and bioinformatics approaches.

作者信息

Kim Yihwan, Koh InSong, Rho Mina

机构信息

Department of Biomedical Informatics, Hanyang University, Seoul, Republic of Korea.

Department of Biomedical Informatics, Hanyang University, Seoul, Republic of Korea; Department of Physiology, Hanyang University, Seoul, Republic of Korea.

出版信息

Methods. 2015 Jun;79-80:52-9. doi: 10.1016/j.ymeth.2014.10.022. Epub 2014 Oct 28.

Abstract

The human microbiome is one of the key factors affecting the host immune system and metabolic functions that are not encoded in the human genome. Culture-independent analysis of the human microbiome using metagenomics approach allows us to investigate the compositions and functions of the human microbiome. Computational methods analyze the microbial community by using specific marker genes or by using shotgun sequencing of the entire microbial community. Taxonomy profiling is conducted by using the reference sequences or by de novo clustering of the specific region of sequences. Functional profiling, which is mainly based on the sequence similarity, is more challenging since about half of ORFs predicted in the metagenomic data could not find homology with known protein families. This review examines computational methods that are valuable for the analysis of human microbiome, and highlights the results of several large-scale human microbiome studies. It is becoming increasingly evident that dysbiosis of the gut microbiome is strongly associated with the development of immune disorder and metabolic dysfunction.

摘要

人类微生物组是影响宿主免疫系统和代谢功能的关键因素之一,这些功能并非由人类基因组编码。使用宏基因组学方法对人类微生物组进行非培养分析,使我们能够研究人类微生物组的组成和功能。计算方法通过使用特定的标记基因或对整个微生物群落进行鸟枪法测序来分析微生物群落。分类学分析通过使用参考序列或对序列的特定区域进行从头聚类来进行。功能分析主要基于序列相似性,更具挑战性,因为宏基因组数据中预测的约一半开放阅读框无法与已知蛋白质家族找到同源性。本综述研究了对人类微生物组分析有价值的计算方法,并突出了几项大规模人类微生物组研究的结果。越来越明显的是,肠道微生物组的失调与免疫紊乱和代谢功能障碍的发展密切相关。

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