Troubleshooting and FAQs

Which classifier(s) should I use?

Choosing a classifier is not easy! There are many pros and cons to each, and the best choice will depend on your research question, the information you need, and the trade-offs needed regarding resources (amongst others). nf-core/taxprofiler is designed to offer multiple classifiers; facilitating easier execution of the one(s) that best suit your needs.

In most cases, you should read the publication of each classifier to understand their trade-offs, in addition to reviewing what other researchers are using in your specific domain. For example, for more detailed and regularly-updated quantitative comparisons, we highly recommend seeing the latest results of automated benchmarking platforms such as:

• CAMI challenge
• LEMMI

Below we provide a brief summary of some of the characteristics of the classifiers available in nf-core/taxprofiler. With this information we hope to help you make an informed decision on which classifier(s) to use for your particular use case analysis.

Decision tree

The decision tree below can help you initially narrow down the classifiers that match your data. Kraken2 can use both DNA-to-DNA and DNA-to-AA databases, and Bracken follows Kraken2.

Full details

The table below contains all criteria that we find relevant to compare.

Furthermore, the table below provides a summary of the original primary ‘unique characteristics’ of the particular classifier (as described in the original publication of the classifier). We order by original publication year as often new-classifier ‘improvements’ are referring to earlier comparable classifiers; but may not reflect the latest version of the earlier classifier.

Other readings

The follow list of publicly accessible or open-access readings may be useful for further understanding of the classifiers and their differences:

• Differences between the Kraken family of classifiers: from the Kraken* authors themselves.
• de Vries, J. J. C., et al. (2021). Recommendations for the introduction of metagenomic next-generation sequencing in clinical virology, part II: bioinformatic analysis and reporting. Journal of Clinical Virology: The Official Publication of the Pan American Society for Clinical Virology, 138(104812), 104812. https://doi.org/10.1016/j.jcv.2021.104812
• de Vries, J. J. C., et al. (2021). Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples. Journal of Clinical Virology: The Official Publication of the Pan American Society for Clinical Virology, 141(104908), 104908. https://doi.org/10.1016/j.jcv.2021.104908
• Maas, M. A. M., et al. (2026). Use of metagenomics for the detection of pathogens in the environment: A scoping review. Environmental Research, 305(Pt 2), 125050. https://doi.org/10.1016/j.envres.2026.125050
• Meyer, F., et al. (2022). Critical Assessment of Metagenome Interpretation: the second round of challenges. Nature Methods, 19(4), 429–440. https://doi.org/10.1038/s41592-022-01431-4
• Sczyrba, A., et al.(2017). Critical Assessment of Metagenome Interpretation-a benchmark of metagenomics software. Nature Methods, 14(11), 1063–1071. https://doi.org/10.1038/nmeth.4458
• Seppey, M., et al. (2026). LEMMIv2: benchmarking framework for metagenomic and 16S amplicon profilers with a catalogue of evaluated tools. Genome Biology, 1–25. https://doi.org/10.1186/s13059-026-04089-9
• Seppey, M., et al. (2020). LEMMI: a continuous benchmarking platform for metagenomics classifiers. Genome Research, 30(8), 1208–1216. https://doi.org/10.1101/gr.260398.119
• Ye, S. H., et al. (2019). Benchmarking Metagenomics Tools for Taxonomic Classification. Cell, 178(4), 779–794. https://doi.org/10.1016/j.cell.2019.07.010

I get a warning during CENTRIFUGE_KREPORT or KRAKENTOOLS_COMBINEKREPORTS_CENTRIFUGE process with exit status 255

When a sample has insufficient hits for abundance estimation, the resulting report.txt file will be empty.

When trying to convert this to a kraken-style report or merging together, the tools will exit with a status code 255, and provide a WARN.

This is not an error nor a failure of the pipeline, just your sample has no hits to the provided database when using Centrifuge.

Why does any error or failed process from BRACKEN_BRACKEN get ignored?

If Kraken2 doesn’t classify any reads (100% unclassified) Bracken will fail on that input. Having no reads taxonomically classified can be a reasonable outcome for some samples, and we don’t want to treat this as an error.

Therefore to allow the remainder of a given run to complete if a single Bracken task fails, we set the Nextflow errorStrategy to ignore. You will still get a warning in the Nextflow console output and log that a Bracken task failed.

If you want to change this behaviour, you can override the errorStrategy in your own Nextflow configuration file.

For example:

process {
    withName: BRACKEN_BRACKEN {
        errorStrategy = 'retry'
    }
}

Other errorStrategy options can be found on the Nextflow documentation.