Metatranscriptomics for the Human Microbiome and Microbial Community Functional Profiling.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yancong Zhang, Kelsey N Thompson, Tobyn Branck, Yan Yan, Long H Nguyen, Eric A Franzosa, Curtis Huttenhower
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引用次数: 25

Abstract

Shotgun metatranscriptomics (MTX) is an increasingly practical way to survey microbial community gene function and regulation at scale. This review begins by summarizing the motivations for community transcriptomics and the history of the field. We then explore the principles, best practices, and challenges of contemporary MTX workflows: beginning with laboratory methods for isolation and sequencing of community RNA, followed by informatics methods for quantifying RNA features, and finally statistical methods for detecting differential expression in a community context. In thesecond half of the review, we survey important biological findings from the MTX literature, drawing examples from the human microbiome, other (nonhuman) host-associated microbiomes, and the environment. Across these examples, MTX methods prove invaluable for probing microbe-microbe and host-microbe interactions, the dynamics of energy harvest and chemical cycling, and responses to environmental stresses. We conclude with a review of open challenges in the MTX field, including making assays and analyses more robust, accessible, and adaptable to new technologies; deciphering roles for millions of uncharacterized microbial transcripts; and solving applied problems such as biomarker discovery and development of microbial therapeutics.

人类微生物组和微生物群落功能分析的超转录组学。
散弹枪亚转录组学(MTX)是一种越来越实用的大规模调查微生物群落基因功能和调控的方法。本文首先概述了社区转录组学研究的动机和该领域的历史。然后,我们探讨了当代MTX工作流程的原则、最佳实践和挑战:从分离和测序社区RNA的实验室方法开始,接着是量化RNA特征的信息学方法,最后是检测社区背景下差异表达的统计方法。在这篇综述的后半部分,我们调查了MTX文献中重要的生物学发现,从人类微生物组、其他(非人类)宿主相关微生物组和环境中提取了例子。在这些例子中,MTX方法在探测微生物-微生物和宿主-微生物相互作用、能量收集和化学循环的动力学以及对环境胁迫的反应方面证明是无价的。最后,我们回顾了MTX领域面临的挑战,包括使检测和分析更强大、更容易获取和适应新技术;解读数百万未表征的微生物转录物的作用;并解决应用问题,如生物标志物的发现和微生物疗法的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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