Simulation study and comparative evaluation of viral contiguous sequence identification tools.
Simulation study and comparative evaluation of viral contiguous sequence identification tools.
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DOI：
10.1186/s12859-021-04242-0
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发表时间：
2021-06-16
影响因子：
3
通讯作者：
Strong M
中科院分区：
文献类型：
--
作者：
Glickman C;Hendrix J;Strong M
病毒，包括噬菌体，是环境和人类相关微生物群落的重要组成部分。病毒可以作为细菌基因的细胞外储库，可以介导微生物组动力学，并且可以影响临床病原体的毒力。各种靶向宏基因组分析技术检测病毒序列，但这些方法通常排除大的和基因组整合的病毒。在这项研究中，我们评估和比较了九种最先进的生物信息学工具的能力，包括Vibrant，VirSorter，VirSorter 2，Virginia，DeepVirginia，MetaPhinder，Kraken 2，Phybrid和BLAST搜索，使用来自地球病毒管道的已识别蛋白质来识别具有不同读段分布，分类组成和复杂性的模拟宏基因组中的病毒连续序列（重叠群）。在这项研究中测试的工具中，VirSorter在预测整合前噬菌体方面获得了最好的F1分数，而Vibrant在预测整合前噬菌体方面具有最高的平均F1分数。虽然在准确率和召回率方面不太平衡，但Kraken 2的平均准确率最高。我们推出了机器学习工具Phybrid，它在F1平均分数方面比MetaPhinder等工具有所提高。该工具利用机器学习与基因内容和核苷酸特征。与单独的基因内容特征相比，核苷酸特征的添加提高了精确度和召回率。所有工具的病毒鉴定不受潜在读段分布的影响，但随着重叠群长度的增加而提高。工具性能呈负相关的分类复杂性和不同的噬菌体宿主。例如，根瘤菌和肠球菌的鉴定一致的工具，而奈瑟氏菌原噬菌体序列通常在这项研究中错过。这项研究对9种最先进的生物信息学工具的性能进行了基准测试，以在不同的模拟条件下识别病毒重叠群。本研究探讨了工具鉴定传统上排除在靶向测序方法之外的整合前噬菌体元件的能力。我们对病毒鉴定工具的全面分析，以评估它们在各种情况下的性能，为病毒研究人员从公开的宏基因组数据中挖掘病毒元素提供了有价值的见解。在线版本包含补充材料，可通过10.1186/s12859-021-04242-0获得。
Viruses, including bacteriophages, are important components of environmental and human associated microbial communities. Viruses can act as extracellular reservoirs of bacterial genes, can mediate microbiome dynamics, and can influence the virulence of clinical pathogens. Various targeted metagenomic analysis techniques detect viral sequences, but these methods often exclude large and genome integrated viruses. In this study, we evaluate and compare the ability of nine state-of-the-art bioinformatic tools, including Vibrant, VirSorter, VirSorter2, VirFinder, DeepVirFinder, MetaPhinder, Kraken 2, Phybrid, and a BLAST search using identified proteins from the Earth Virome Pipeline to identify viral contiguous sequences (contigs) across simulated metagenomes with different read distributions, taxonomic compositions, and complexities. Of the tools tested in this study, VirSorter achieved the best F1 score while Vibrant had the highest average F1 score at predicting integrated prophages. Though less balanced in its precision and recall, Kraken2 had the highest average precision by a substantial margin. We introduced the machine learning tool, Phybrid, which demonstrated an improvement in average F1 score over tools such as MetaPhinder. The tool utilizes machine learning with both gene content and nucleotide features. The addition of nucleotide features improves the precision and recall compared to the gene content features alone.Viral identification by all tools was not impacted by underlying read distribution but did improve with contig length. Tool performance was inversely related to taxonomic complexity and varied by the phage host. For instance, Rhizobium and Enterococcus phages were identified consistently by the tools; whereas, Neisseria prophage sequences were commonly missed in this study. This study benchmarked the performance of nine state-of-the-art bioinformatic tools to identify viral contigs across different simulation conditions. This study explored the ability of the tools to identify integrated prophage elements traditionally excluded from targeted sequencing approaches. Our comprehensive analysis of viral identification tools to assess their performance in a variety of situations provides valuable insights to viral researchers looking to mine viral elements from publicly available metagenomic data. The online version contains supplementary material available at 10.1186/s12859-021-04242-0.
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影响因子：
3.7
作者：
Jurtz VI;Villarroel J;Lund O;Voldby Larsen M;Nielsen M
通讯作者：
Nielsen M
影响因子：
14.9
作者：
Federhen S
通讯作者：
Federhen S
影响因子：
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作者：
Amgarten, Deyvid;Braga, Lucas P. P.;Setubal, Joao C.
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影响因子：
2.7
作者：
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Putonti, Catherine
DOI：
10.1073/pnas.1305923110
发表时间：
2013-06-25
影响因子：
11.1
作者：
Barr, Jeremy J.;Auro, Rita;Rohwer, Forest
通讯作者：
Rohwer, Forest