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环境序列分析导论:使用MEGAN进行宏基因组学分析

Introduction to the Analysis of Environmental Sequences: Metagenomics with MEGAN.

作者信息

Bağcı Caner, Beier Sina, Górska Anna, Huson Daniel H

机构信息

Algorithms in Bioinformatics, Faculty of Computer Science, University of Tübingen, Tübingen, Germany.

出版信息

Methods Mol Biol. 2019;1910:591-604. doi: 10.1007/978-1-4939-9074-0_19.

Abstract

Metagenomics has become a part of the standard toolkit for scientists interested in studying microbes in the environment. Compared to 16S rDNA sequencing, which allows coarse taxonomic profiling of samples, shotgun metagenomic sequencing provides a more detailed analysis of the taxonomic and functional content of samples. Long read technologies, such as developed by Pacific Biosciences or Oxford Nanopore, produce much longer stretches of informative sequence, greatly simplifying the difficult and time-consuming process of metagenomic assembly. MEGAN6 provides a wide range of analysis and visualization methods for the analysis of short and long read metagenomic data. A simple and efficient analysis pipeline for metagenomic analysis consists of the DIAMOND alignment tool on short reads, or the LAST alignment tool on long reads, followed by MEGAN. This approach performs taxonomic and functional abundance analysis, supports comparative analysis of large-scale experiments, and allows one to involve experimental metadata in the analysis.

摘要

宏基因组学已成为对研究环境中微生物感兴趣的科学家标准工具包的一部分。与允许对样本进行粗略分类分析的16S rDNA测序相比,鸟枪法宏基因组测序能对样本的分类和功能内容进行更详细的分析。诸如太平洋生物科学公司或牛津纳米孔公司开发的长读长技术可产生长得多的信息序列片段,极大地简化了宏基因组组装这一困难且耗时的过程。MEGAN6为短读长和长读长宏基因组数据的分析提供了广泛的分析和可视化方法。一个简单而高效的宏基因组分析流程包括用于短读长的DIAMOND比对工具或用于长读长的LAST比对工具,随后是MEGAN。这种方法可进行分类和功能丰度分析,支持大规模实验的比较分析,并允许在分析中纳入实验元数据。

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