ArticleaBIOTECH2024
Impact of database choice and confidence score on the performance of taxonomic classification using Kraken2.
Article in aBIOTECH, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
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- Identification and complete genome sequence of a novel secovirus infecting Broussonetia kaempferi in China.Archives of virology · 2026Article
- Microbial single-cell transcriptomics links gut microbiota functional states to metabolic changes in male mice.Nature communications · 2026Article
- Protocol for the assessment of the impact of mycotoxins and glyphosate residues on the gut microbiome and resistome of European fallow deer.STAR protocols · 2026Article
- Whole-genome characterization of a colistin-resistant Klebsiella quasipneumoniae subsp. quasipneumoniae isolate from a urinary tract infection in Peshawar, Pakistan.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2026Article
- Multikingdom microbiome-based machine learning enables multiple sclerosis diagnosis.NPJ biofilms and microbiomes · 2026Article
- Long-Term Application of Fermented Fertilizer Attenuates the Accumulation of Antibiotic Resistance Genes in Aquaculture Sediment.Microorganisms · 2026Article
- TIPP-SD: A new method for species detection in microbiomes.PLoS computational biology · 2026Article
- African carnivore gut bacterial diversity and composition are associated with sample condition but not storage technique.Animal microbiome · 2026Article
- Kun-peng enables scalable and accurate pan-domain metagenomic classification.Briefings in bioinformatics · 2026Article
- Filtering for truth: high-precision taxonomic classification in nanopore shotgun metagenomics data through a KMA-based bioinformatic pipeline (KAPTAIN).BMC genomics · 2026Article
- Reproducible Emu-Based Workflow for High-Fidelity Soil and Plant Microbiome Profiling on HPC Clusters.Bio-protocol · 2026Article
- Bioinformatic tools for microbiome analysis: from raw sequences to biological insights.Frontiers in microbiology · 2026Review
- Absolute Quantification of Bacteria in the Microbiome and Its Application.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Interpreting fungal ecological contributions through taxonomic and functional profiling of metatranscriptomics.IMA fungus · 2026Review
- Detection ofFood chemistry. Molecular sciences · 2025Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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. Supplementary Information: The online version contains supplementary material available at 10.1007/s42994-024-00178-0.
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