Institute of Microbiology and Infection, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
Department of Genetics and Genome Biology, University of Leicester, Leicester, UK.
Microbiome. 2023 Apr 21;11(1):84. doi: 10.1186/s40168-023-01533-x.
BACKGROUND: The prediction of bacteriophage sequences in metagenomic datasets has become a topic of considerable interest, leading to the development of many novel bioinformatic tools. A comparative analysis of ten state-of-the-art phage identification tools was performed to inform their usage in microbiome research. METHODS: Artificial contigs generated from complete RefSeq genomes representing phages, plasmids, and chromosomes, and a previously sequenced mock community containing four phage species, were used to evaluate the precision, recall, and F1 scores of the tools. We also generated a dataset of randomly shuffled sequences to quantify false-positive calls. In addition, a set of previously simulated viromes was used to assess diversity bias in each tool's output. RESULTS: VIBRANT and VirSorter2 achieved the highest F1 scores (0.93) in the RefSeq artificial contigs dataset, with several other tools also performing well. Kraken2 had the highest F1 score (0.86) in the mock community benchmark by a large margin (0.3 higher than DeepVirFinder in second place), mainly due to its high precision (0.96). Generally, k-mer-based tools performed better than reference similarity tools and gene-based methods. Several tools, most notably PPR-Meta, called a high number of false positives in the randomly shuffled sequences. When analysing the diversity of the genomes that each tool predicted from a virome set, most tools produced a viral genome set that had similar alpha- and beta-diversity patterns to the original population, with Seeker being a notable exception. CONCLUSIONS: This study provides key metrics used to assess performance of phage detection tools, offers a framework for further comparison of additional viral discovery tools, and discusses optimal strategies for using these tools. We highlight that the choice of tool for identification of phages in metagenomic datasets, as well as their parameters, can bias the results and provide pointers for different use case scenarios. We have also made our benchmarking dataset available for download in order to facilitate future comparisons of phage identification tools. Video Abstract.
背景:在宏基因组数据集预测噬菌体序列已成为一个备受关注的话题,由此开发了许多新型生物信息学工具。我们对十种最先进的噬菌体识别工具进行了综合评估,以指导它们在微生物组研究中的应用。
方法:我们使用人工合成的 contigs 对 RefSeq 全基因组进行模拟,这些 contigs 分别代表噬菌体、质粒和染色体,同时还使用了之前测序的模拟群落,其中包含四种噬菌体物种。通过评估这些工具的精确率、召回率和 F1 分数,来评估它们的性能。我们还生成了一组随机打乱的序列数据集,以量化假阳性预测。此外,我们还使用了一组之前模拟的病毒组来评估每个工具输出的多样性偏差。
结果:在 RefSeq 人工 contigs 数据集上,VIBRANT 和 VirSorter2 取得了最高的 F1 分数(0.93),还有其他几个工具也表现出色。Kraken2 在模拟群落基准测试中以 0.3 的巨大优势取得了最高的 F1 分数(0.86,比第二名 DeepVirFinder 高出 0.3),主要是因为它的精确率高(0.96)。一般来说,基于 k-mer 的工具比基于参考相似性的工具和基于基因的方法表现要好。有几个工具,特别是 PPR-Meta,在随机打乱的序列中预测到了大量的假阳性。当分析每个工具从病毒组中预测到的基因组多样性时,大多数工具生成的病毒基因组集与原始种群具有相似的 alpha 和 beta 多样性模式,Seeker 是一个明显的例外。
结论:本研究提供了评估噬菌体检测工具性能的关键指标,为进一步比较其他病毒发现工具提供了框架,并讨论了使用这些工具的最佳策略。我们强调,在宏基因组数据集中识别噬菌体的工具选择及其参数可能会对结果产生偏差,并为不同的用例场景提供了指导。我们还提供了基准测试数据集的下载,以方便未来对噬菌体识别工具的比较。
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