VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data.
VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data.
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VirFinder：一种基于K-MER的新型工具，用于识别组装的元基因组数据中的病毒序列。
DOI：
10.1186/s40168-017-0283-5
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发表时间：
2017-07-06
期刊：
影响因子：
15.5
通讯作者：
Sun F
中科院分区：
文献类型：
--
作者：
Ren J;Ahlgren NA;Lu YY;Fuhrman JA;Sun F
鉴定含有病毒和宿主重叠群的混合宏基因组中的病毒序列是分析样品的病毒组分的关键的第一步。目前用于区分原核病毒和宿主重叠群的工具主要使用基于基因的相似性方法。这些方法可以显著限制结果，特别是对于具有很少预测蛋白质或缺乏与先前已知病毒相似的蛋白质的短重叠群。我们开发了Virginia，这是第一个基于k聚体频率的机器学习方法，用于病毒重叠群识别，完全避免了基于基因的相似性搜索。相反，病毒学基于我们的经验观察来识别病毒序列，即病毒和宿主具有明显不同的k-mer特征。通过对2014年1月1日之前测序的宿主和病毒基因组序列训练其机器学习模型，并对2014年1月1日之后获得的序列进行评估，测试了Virginia在正确识别病毒序列方面的性能。当使用从完整基因组二次采样的重叠群或从模拟的人类肠道宏基因组组装的重叠群进行评估时，VirSorter比VirSorter（当前最先进的基于基因的病毒分类工具）具有显著更好的识别真正病毒重叠群的比率（真阳性率（TPR））。例如，对于从完整基因组二次采样的重叠群，Virginia在与VirSorter相同的假阳性率（分别为0、0.003和0.006）下，对于1、3和5 kb的重叠群分别具有比VirSorter高78倍、2.4倍和1.8倍的TPR，因此Virginia对于小重叠群的工作比VirSorter好得多。VirSorter还鉴定了几个最近测序的病毒基因组（2014年1月1日之后），VirSorter没有，并且与先前测序的病毒没有核苷酸相似性，证明VirSorter在鉴定新病毒序列方面的潜在优势。将Virginia应用于来自健康和肝硬化患者的一组人类肠道宏基因组，揭示了健康个体中比肝硬化患者更高的病毒多样性。我们还鉴定了含有在健康患者中具有较高丰度的crAspherage样重叠群的重叠群箱和与肝硬化患者相关的推定韦荣氏球菌属前噬菌体。这种创新的基于k-mer的工具补充了基于基因的方法，并将显着提高原核病毒序列鉴定，特别是基于宏基因组学的病毒生态学研究。本文的在线版本（doi：10.1186/s40168-017-0283-5）包含补充材料，可供授权用户使用。
Identifying viral sequences in mixed metagenomes containing both viral and host contigs is a critical first step in analyzing the viral component of samples. Current tools for distinguishing prokaryotic virus and host contigs primarily use gene-based similarity approaches. Such approaches can significantly limit results especially for short contigs that have few predicted proteins or lack proteins with similarity to previously known viruses. We have developed VirFinder, the first k-mer frequency based, machine learning method for virus contig identification that entirely avoids gene-based similarity searches. VirFinder instead identifies viral sequences based on our empirical observation that viruses and hosts have discernibly different k-mer signatures. VirFinder’s performance in correctly identifying viral sequences was tested by training its machine learning model on sequences from host and viral genomes sequenced before 1 January 2014 and evaluating on sequences obtained after 1 January 2014. VirFinder had significantly better rates of identifying true viral contigs (true positive rates (TPRs)) than VirSorter, the current state-of-the-art gene-based virus classification tool, when evaluated with either contigs subsampled from complete genomes or assembled from a simulated human gut metagenome. For example, for contigs subsampled from complete genomes, VirFinder had 78-, 2.4-, and 1.8-fold higher TPRs than VirSorter for 1, 3, and 5 kb contigs, respectively, at the same false positive rates as VirSorter (0, 0.003, and 0.006, respectively), thus VirFinder works considerably better for small contigs than VirSorter. VirFinder furthermore identified several recently sequenced virus genomes (after 1 January 2014) that VirSorter did not and that have no nucleotide similarity to previously sequenced viruses, demonstrating VirFinder’s potential advantage in identifying novel viral sequences. Application of VirFinder to a set of human gut metagenomes from healthy and liver cirrhosis patients reveals higher viral diversity in healthy individuals than cirrhosis patients. We also identified contig bins containing crAssphage-like contigs with higher abundance in healthy patients and a putative Veillonella genus prophage associated with cirrhosis patients. This innovative k-mer based tool complements gene-based approaches and will significantly improve prokaryotic viral sequence identification, especially for metagenomic-based studies of viral ecology. The online version of this article (doi:10.1186/s40168-017-0283-5) contains supplementary material, which is available to authorized users.
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影响因子：
14.9
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通讯作者：
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