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当前微生物组分析面临的挑战和最佳实践方案。

Current challenges and best-practice protocols for microbiome analysis.

机构信息

Weihenstephan-Triesdorf University of Applied Sciences and Technical University of Munich, TUM Campus Straubing for Biotechnology and Sustainability, Straubing, Germany.

出版信息

Brief Bioinform. 2021 Jan 18;22(1):178-193. doi: 10.1093/bib/bbz155.

Abstract

Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. To simplify and standardize the computational analysis, we provide a set of best-practice workflows for 16S rRNA and metagenomic sequencing data (available at https://github.com/grimmlab/MicrobiomeBestPracticeReview).

摘要

使用下一代测序技术分析不同物种和环境的微生物组,显著提高了我们对环境微生物的代谢、生理和生态作用的理解。然而,微生物组的分析受到实验条件(例如测序错误和基因组重复)以及计算密集且繁琐的下游分析(例如质量控制、组装、分类和统计分析)的影响。此外,新测序技术和方案的引入导致了新方法的大量涌现,这也会立即影响分析结果。本工作旨在综述 16S rRNA 测序和鸟枪法及长读长宏基因组学的最重要工作流程,并提供关于实验设计、样品处理、测序、组装、分类、注释和可视化的最佳实践方案。为了简化和标准化计算分析,我们为 16S rRNA 和宏基因组测序数据提供了一组最佳实践工作流程(可在 https://github.com/grimmlab/MicrobiomeBestPracticeReview 上获得)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23d1/7820839/e4bcba5138cf/bbz155f1.jpg

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