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一种基于系统发育树的自动化小亚基核糖体RNA分类与比对流程(STAP)。

An automated phylogenetic tree-based small subunit rRNA taxonomy and alignment pipeline (STAP).

作者信息

Wu Dongying, Hartman Amber, Ward Naomi, Eisen Jonathan A

机构信息

UC Davis Genome Center, University of California Davis, Davis, California, United States of America.

出版信息

PLoS One. 2008 Jul 2;3(7):e2566. doi: 10.1371/journal.pone.0002566.

Abstract

Comparative analysis of small-subunit ribosomal RNA (ss-rRNA) gene sequences forms the basis for much of what we know about the phylogenetic diversity of both cultured and uncultured microorganisms. As sequencing costs continue to decline and throughput increases, sequences of ss-rRNA genes are being obtained at an ever-increasing rate. This increasing flow of data has opened many new windows into microbial diversity and evolution, and at the same time has created significant methodological challenges. Those processes which commonly require time-consuming human intervention, such as the preparation of multiple sequence alignments, simply cannot keep up with the flood of incoming data. Fully automated methods of analysis are needed. Notably, existing automated methods avoid one or more steps that, though computationally costly or difficult, we consider to be important. In particular, we regard both the building of multiple sequence alignments and the performance of high quality phylogenetic analysis to be necessary. We describe here our fully-automated ss-rRNA taxonomy and alignment pipeline (STAP). It generates both high-quality multiple sequence alignments and phylogenetic trees, and thus can be used for multiple purposes including phylogenetically-based taxonomic assignments and analysis of species diversity in environmental samples. The pipeline combines publicly-available packages (PHYML, BLASTN and CLUSTALW) with our automatic alignment, masking, and tree-parsing programs. Most importantly, this automated process yields results comparable to those achievable by manual analysis, yet offers speed and capacity that are unattainable by manual efforts.

摘要

小亚基核糖体RNA(ss-rRNA)基因序列的比较分析构成了我们目前对培养和未培养微生物系统发育多样性认知的基础。随着测序成本持续下降以及通量增加,ss-rRNA基因序列的获取速度不断加快。这一不断增长的数据流为微生物多样性和进化打开了许多新窗口,同时也带来了重大的方法学挑战。那些通常需要耗时的人工干预的过程,比如多重序列比对的准备工作,根本无法跟上源源不断涌入的数据。因此需要全自动分析方法。值得注意的是,现有的自动化方法避开了一个或多个步骤,尽管这些步骤计算成本高或难度大,但我们认为它们很重要。特别是,我们认为构建多重序列比对和进行高质量的系统发育分析都是必要的。我们在此描述我们的全自动ss-rRNA分类和比对流程(STAP)。它能生成高质量的多重序列比对和系统发育树,因此可用于多种目的,包括基于系统发育的分类归属以及环境样本中物种多样性的分析。该流程将公开可用的软件包(PHYML、BLASTN和CLUSTALW)与我们的自动比对、屏蔽和树解析程序相结合。最重要的是,这个自动化过程产生的结果与人工分析所能达到的结果相当,但同时提供了人工操作无法企及的速度和能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85c/2432038/d6aa14879ac7/pone.0002566.g001.jpg

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