de Campos Gabriel Montenegro, Clemente Luan Gaspar, Lima Alex Ranieri Jerônimo, Cella Eleonora, Fonseca Vagner, Ximenez João Paulo Bianchi, Nishiyama Milton Yutaka, de Carvalho Enéas, Sampaio Sandra Coccuzzo, Giovanetti Marta, Elias Maria Carolina, Slavov Svetoslav Nanev
Programa de Pós-graduação em Oncologia Clínica, Células-Tronco e Terapia Celular, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Prêto, Brazil.
Escola Superior de Agricultura Luiz de Queiroz, Departamento de Zootecnia, Universidade de São Paulo, Piracicaba, Brazil.
Virol J. 2025 Mar 28;22(1):88. doi: 10.1186/s12985-025-02708-8.
Viral metagenomics has expanded significantly in recent years due to advancements in next-generation sequencing, establishing it as the leading method for identifying emerging viruses. A crucial step in metagenomics is taxonomic classification, where sequence data is assigned to specific taxa, thereby enabling the characterization of species composition within a sample. Various taxonomic classifiers have been developed in recent years, each employing distinct classification approaches that produce varying results and abundance profiles, even when analyzing the same sample.
In this study, we propose using the identification of Torque Teno Viruses (TTVs), from the Anelloviridae family, as indicators to evaluate the performance of four short-read-based metagenomic classifiers: Kraken2, Kaiju, CLARK and DIAMOND, when evaluating human plasma samples.
Our results show that each classifier assigns TTV species at different abundance levels, potentially influencing the interpretation of diversity within samples. Specifically, nucleotide-based classifiers tend to detect a broader range of TTV species, indicating higher sensitivity, while amino acid-based classifiers like DIAMOND and CLARK display lower abundance indices. Interestingly, despite employing different algorithms and data types (protein-based vs. nucleotide-based), Kaiju and Kraken2 performed similarly.
Our study underscores the critical impact of classifier selection on diversity indices in metagenomic analyses. Kaiju effectively assigned a wide variety of TTV species, demonstrating it did not require a high volume of reads to capture diversity. Nucleotide-based classifiers like CLARK and Kraken2 showed superior sensitivity, which is valuable for detecting emerging or rare viruses. At the same time, protein-based approaches such as DIAMOND and Kaiju proved robust for identifying known species with low variability.
近年来，由于下一代测序技术的进步，病毒宏基因组学有了显著发展，使其成为识别新出现病毒的主要方法。宏基因组学中的一个关键步骤是分类学分类，即将序列数据分配到特定的分类单元，从而能够对样本中的物种组成进行表征。近年来已经开发了各种分类学分类器，即使在分析同一样本时，每个分类器采用的不同分类方法也会产生不同的结果和丰度分布。
在本研究中，我们建议使用来自细小病毒科的Torque Teno病毒（TTV）的鉴定作为指标，来评估四种基于短读长的宏基因组分类器（Kraken2、Kaiju、CLARK和DIAMOND）在评估人类血浆样本时的性能。
我们的结果表明，每个分类器在不同丰度水平上分配TTV物种，这可能会影响对样本内多样性的解释。具体而言，基于核苷酸的分类器倾向于检测更广泛的TTV物种，表明灵敏度更高，而像DIAMOND和CLARK这样基于氨基酸的分类器显示出较低的丰度指数。有趣的是，尽管采用了不同的算法和数据类型（基于蛋白质与基于核苷酸），Kaiju和Kraken2的表现相似。
我们的研究强调了分类器选择对宏基因组分析中多样性指数的关键影响。Kaiju有效地分配了多种TTV物种，表明它不需要大量读数就能捕捉到多样性。像CLARK和Kraken2这样基于核苷酸的分类器显示出更高的灵敏度，这对于检测新出现或罕见病毒很有价值。同时，像DIAMOND和Kaiju这样基于蛋白质的方法在识别低变异性的已知物种方面证明是可靠的。
