Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns.
Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns.
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DOI：
10.1128/msystems.00191-16
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发表时间：
2017-03
期刊：
影响因子：
6.4
通讯作者：
Knight R
中科院分区：
文献类型：
--
作者：
Amir A;McDonald D;Navas-Molina JA;Kopylova E;Morton JT;Zech Xu Z;Kightley EP;Thompson LR;Hyde ER;Gonzalez A;Knight R
去模糊提供了一种快速和敏感的手段来评估由密切相关的类群的分化驱动的生态模式。该算法提供了一个解决方案，以确定其扩增子不同的单碱基对的分类群之间的真实的生态差异的问题，是适用于大规模测序数据集的自动化方式，并可以整合随时间收集的测序运行。16S核糖体RNA基因扩增子的高通量测序促进了对复杂微生物群落的理解，但PCR和DNA测序中固有的噪音限制了密切相关细菌的分化。虽然许多科学问题可以通过广泛的分类学特征来解决，但临床、食品安全和一些生态应用需要更高的特异性。在这里，我们介绍了一种新的子操作分类单元（sOTU）方法，Deflur，它使用错误配置文件从Illumina MiSeq和HiSeq测序平台获得推定的无错误序列。相对于类似的sOTU方法，去模糊大大减少了计算需求，并且以类似或更好的灵敏度和特异性这样做。使用模拟、模拟混合物和真实的数据集，我们检测到具有单核苷酸差异的密切相关的细菌序列，同时去除假阳性并保持检测的稳定性，这表明去模糊仅受扩增子序列内的读取长度和多样性的限制。由于Deflur是在每个样本的水平上操作的，因此它可以扩展到现代数据集和荟萃分析。为了突出Deflur整合数据集的能力，我们将其应用于美国肠道项目的多个不同测序轮的交互式探索。Deflur是基于Berkeley Software Distribution（BSD）许可证的开源软件，易于安装，并可从https://github.com/biocore/deblur下载。重要性去模糊提供了一种快速和敏感的手段来评估由密切相关的类群的分化驱动的生态模式。该算法提供了一个解决方案，以确定其扩增子不同的单碱基对的分类群之间的真实的生态差异的问题，是适用于大规模测序数据集的自动化方式，并可以整合随着时间的推移收集的测序运行。
Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time. High-throughput sequencing of 16S ribosomal RNA gene amplicons has facilitated understanding of complex microbial communities, but the inherent noise in PCR and DNA sequencing limits differentiation of closely related bacteria. Although many scientific questions can be addressed with broad taxonomic profiles, clinical, food safety, and some ecological applications require higher specificity. Here we introduce a novel sub-operational-taxonomic-unit (sOTU) approach, Deblur, that uses error profiles to obtain putative error-free sequences from Illumina MiSeq and HiSeq sequencing platforms. Deblur substantially reduces computational demands relative to similar sOTU methods and does so with similar or better sensitivity and specificity. Using simulations, mock mixtures, and real data sets, we detected closely related bacterial sequences with single nucleotide differences while removing false positives and maintaining stability in detection, suggesting that Deblur is limited only by read length and diversity within the amplicon sequences. Because Deblur operates on a per-sample level, it scales to modern data sets and meta-analyses. To highlight Deblur’s ability to integrate data sets, we include an interactive exploration of its application to multiple distinct sequencing rounds of the American Gut Project. Deblur is open source under the Berkeley Software Distribution (BSD) license, easily installable, and downloadable from https://github.com/biocore/deblur. IMPORTANCE Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time.