SpeciateIT 和 vSpeciateDB:新颖、快速、准确的阴道微生物群 16S rRNA 基因分类。

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Johanna B Holm, Pawel Gajer, Jacques Ravel
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引用次数: 0

摘要

背景:将序列聚类为可操作的分类单元(OTU)和去噪方法是对大量 16S rRNA 基因序列进行分类的主流方法。特定环境的参考数据库通常能产生最佳的分类分配:我们开发了一种新型分类工具 SpeciateIT,它能快速、准确地对单个扩增子序列进行分类 ( https://github.com/Ravel-Laboratory/speciateIT )。我们还介绍了 vSpeciateDB,这是一个定制的参考数据库,用于对来自阴道微生物群的 16S rRNA 基因扩增子序列进行分类。我们的研究表明,与其他算法相比,SpeciateIT 所需的计算资源最少,而且与 vSpeciateDB 结合使用时,能以特定环境的方式提供准确的物种级分类:结论:本文介绍了两种具有重要实际意义的新资源。新颖的分类算法 SpeciateIT 基于七阶马尔科夫链模型,可快速准确地按序列进行分类分配(107 个序列只需 10 分钟)。vSpeciateDB 是一个精心定制的参考数据库,是一项重要而实用的贡献。它的意义在于,与通用数据库相比,这个特定环境数据库能够提供更高的物种分辨率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SpeciateIT and vSpeciateDB: novel, fast, and accurate per sequence 16S rRNA gene taxonomic classification of vaginal microbiota.

Background: Clustering of sequences into operational taxonomic units (OTUs) and denoising methods are a mainstream stopgap to taxonomically classifying large numbers of 16S rRNA gene sequences. Environment-specific reference databases generally yield optimal taxonomic assignment.

Results: We developed SpeciateIT, a novel taxonomic classification tool which rapidly and accurately classifies individual amplicon sequences ( https://github.com/Ravel-Laboratory/speciateIT ). We also present vSpeciateDB, a custom reference database for the taxonomic classification of 16S rRNA gene amplicon sequences from vaginal microbiota. We show that SpeciateIT requires minimal computational resources relative to other algorithms and, when combined with vSpeciateDB, affords accurate species level classification in an environment-specific manner.

Conclusions: Herein, two resources with new and practical importance are described. The novel classification algorithm, SpeciateIT, is based on 7th order Markov chain models and allows for fast and accurate per-sequence taxonomic assignments (as little as 10 min for 107 sequences). vSpeciateDB, a meticulously tailored reference database, stands as a vital and pragmatic contribution. Its significance lies in the superiority of this environment-specific database to provide more species-resolution over its universal counterparts.

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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