数字农科院2.0

Impact of database choice and confidence score on the performance of taxonomic classification using Kraken2

文献类型： 外文期刊

作者： Liu, Yunlong;Ghaffari, Morteza H.;Ma, Tao;Tu, Yan

作者机构：

关键词： Metagenome;Taxonomic classification;Kraken2;Reference database;Confidence score

期刊名称： ABIOTECH

ISSN： 2096-6326

年卷期： 2024 年

页码：

收录情况： EI（2024版） ; ; CSCD（2023-2024年度）

摘要： Accurate taxonomic classification is essential to understanding microbial diversity and function through metagenomic sequencing. However, this task is complicated by the vast variety of microbial genomes and the computational limitations of bioinformatics tools. The aim of this study was to evaluate the impact of reference database selection and confidence score (CS) settings on the performance of Kraken2, a widely used k-mer-based metagenomic classifier. In this study, we generated simulated metagenomic datasets to systematically evaluate how the choice of reference databases, from the compact Minikraken v1 to the expansive nt- and GTDB r202, and different CS (from 0 to 1.0) affect the key performance metrics of Kraken2. These metrics include classification rate, precision, recall, F1 score, and accuracy of true versus calculated bacterial abundance estimation. Our results show that higher CS, which increases the rigor of taxonomic classification by requiring greater k-mer agreement, generally decreases the classification rate. This effect is particularly pronounced for smaller databases such as Minikraken and Standard-16, where no reads could be classified when the CS was above 0.4. In contrast, for larger databases such as Standard, nt and GTDB r202, precision and F1 scores improved significantly with increasing CS, highlighting their robustness to stringent conditions. Recovery rates were mostly stable, indicating consistent detection of species under different CS settings. Crucially, the results show that a comprehensive reference database combined with a moderate CS (0.2 or 0.4) significantly improves classification accuracy and sensitivity. This finding underscores the need for careful selection of database and CS parameters tailored to specific scientific questions and available computational resources to optimize the results of metagenomic analyses.

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