Department of Pharmacology, Faculty of Medicine, Dalhousie University, Halifax, Canada.
Integrated Microbiome Resource (IMR), Dalhousie University, Halifax, Canada.
Microb Genom. 2023 Mar;9(3). doi: 10.1099/mgen.0.000949.
In metagenomic analyses of microbiomes, one of the first steps is usually the taxonomic classification of reads by comparison to a database of previously taxonomically classified genomes. While different studies comparing metagenomic taxonomic classification methods have determined that different tools are 'best', there are two tools that have been used the most to-date: Kraken (-mer-based classification against a user-constructed database) and MetaPhlAn (classification by alignment to clade-specific marker genes), the latest versions of which are Kraken2 and MetaPhlAn 3, respectively. We found large discrepancies in both the proportion of reads that were classified as well as the number of species that were identified when we used both Kraken2 and MetaPhlAn 3 to classify reads within metagenomes from human-associated or environmental datasets. We then investigated which of these tools would give classifications closest to the real composition of metagenomic samples using a range of simulated and mock samples and examined the combined impact of tool-parameter-database choice on the taxonomic classifications given. This revealed that there may not be a one-size-fits-all 'best' choice. While Kraken2 can achieve better overall performance, with higher precision, recall and F1 scores, as well as alpha- and beta-diversity measures closer to the known composition than MetaPhlAn 3, the computational resources required for this may be prohibitive for many researchers, and the default database and parameters should not be used. We therefore conclude that the best tool-parameter-database choice for a particular application depends on the scientific question of interest, which performance metric is most important for this question and the limit of available computational resources.
在微生物组的宏基因组分析中，通常的第一步通常是通过与以前分类的基因组数据库进行比较，对读取内容进行分类。虽然比较宏基因组分类方法的不同研究已经确定了不同的工具是“最佳”的，但到目前为止，使用最多的两种工具是：Kraken（基于-mer 的分类，针对用户构建的数据库）和 MetaPhlAn（通过与特定进化枝的标记基因比对进行分类），它们的最新版本分别是 Kraken2 和 MetaPhlAn 3。我们发现，当我们使用 Kraken2 和 MetaPhlAn 3 对来自人类相关或环境数据集的宏基因组中的读取内容进行分类时，无论是分类的读取内容比例，还是鉴定的物种数量，都存在很大的差异。然后，我们使用一系列模拟和模拟样本研究了这些工具中的哪一个最能给出与宏基因组样本实际组成最接近的分类，检查了工具参数数据库选择对分类的综合影响。这表明，可能没有一种适合所有情况的“最佳”选择。虽然 Kraken2 可以实现更好的整体性能，具有更高的精度、召回率和 F1 分数，以及与 MetaPhlAn 3 相比，α和β多样性测量更接近已知组成，但这可能需要大量的计算资源，对于许多研究人员来说可能是不可行的，并且不应使用默认的数据库和参数。因此，我们的结论是，对于特定的应用程序，最佳的工具参数数据库选择取决于研究人员感兴趣的科学问题、对于这个问题最重要的性能指标以及可用的计算资源限制。
