Configuration

MMseqs2 taxonomy* (Final model dataset, Mock et al. 2022)

MMseqs2 taxonomy: MMseqs2 with the parameters of MMseqs2 taxonomy (--e-profile 0.001, -e 1), so each hit carries an E value used for the PR curve; same hits as MMseqs2 taxonomy for 99.94% of samples.

1 evaluation · 3 results

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Evaluations and results

1 evaluation · 3 results. Different protocols are not a single leaderboard.

Protocol: BERTax Final model dataset: taxonomic classification at superkingdom and phylum, and genus (Mock et al. 2022) · Dataset: BERTax Final model dataset (Mock et al. 2022)

Sorted by Macro average precision (genus rank; macro average over classes; unclassified samples treated as random guesses) (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	Macro average precision (genus rank; macro average over classes; unclassified samples treated as random guesses)	Macro average precision (phylum rank; macro average over classes; unclassified samples treated as random guesses)	Macro average precision (superkingdom rank; macro average over classes; unclassified samples treated as random guesses)
MMseqs2 taxonomy* (Final model dataset, Mock et al. 2022)Evaluation	75.1%	93.5%	98.1%

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Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: MMseqs2 taxonomy* (Final model dataset, Mock et al. 2022)	Protocol: BERTax Final model dataset: taxonomic classification at superkingdom and phylum, and genus (Mock et al. 2022) Dataset: BERTax Final model dataset (Mock et al. 2022)	75.1% Macro average precision (genus rank; macro average over classes; unclassified samples treated as random guesses) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source MMseqs2 taxonomy* on the BERTax final model dataset dna-pathogen-20261012-bertax-protocol-final Aggregation: Not reported Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks (PNAS PDF) · Table 1, row "MMseqs2 tax. ∗", column group "Final", column "Genus" (PNAS PDF page 4)
Configuration: MMseqs2 taxonomy* (Final model dataset, Mock et al. 2022)	Protocol: BERTax Final model dataset: taxonomic classification at superkingdom and phylum, and genus (Mock et al. 2022) Dataset: BERTax Final model dataset (Mock et al. 2022)	93.5% Macro average precision (phylum rank; macro average over classes; unclassified samples treated as random guesses) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source MMseqs2 taxonomy* on the BERTax final model dataset dna-pathogen-20261012-bertax-protocol-final Aggregation: Not reported Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks (PNAS PDF) · Table 1, row "MMseqs2 tax. ∗", column group "Final", column "Phyl." (PNAS PDF page 4)
Configuration: MMseqs2 taxonomy* (Final model dataset, Mock et al. 2022)	Protocol: BERTax Final model dataset: taxonomic classification at superkingdom and phylum, and genus (Mock et al. 2022) Dataset: BERTax Final model dataset (Mock et al. 2022)	98.1% Macro average precision (superkingdom rank; macro average over classes; unclassified samples treated as random guesses) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source MMseqs2 taxonomy* on the BERTax final model dataset dna-pathogen-20261012-bertax-protocol-final Aggregation: Not reported Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks (PNAS PDF) · Table 1, row "MMseqs2 tax. ∗", column group "Final", column "Supk." (PNAS PDF page 4)

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Release 2026-10-22-1b7cf027e4f6 · Record review: source checked

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Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks · Original source · PNAS 119(35):e2122636119, published 2022-08-26; PMC9436379 full-text XML

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Technical metadata and extraction receipts

Stable ID: dna-pathogen-20261012-bertax-config-mmseqs2-taxonomy-final

areas: microbes-communities
contexts: research
method types: conventional_pipeline
reported name: MMseqs2 taxonomy*
foundation model eligible: false
parameters: MMseqs2 with the parameters of MMseqs2 taxonomy (--e-profile 0.001, -e 1), so each hit carries an E value used for the PR curve; same hits as MMseqs2 taxonomy for 99.94% of samples
source locator: Materials and Methods 'Comparison to Other Tools' paragraphs 1-2; Results 'Comparison to Other Classification Approaches' paragraphs 1 and 3
access reuse: Tool run by the article's authors; trained models and reference builds are not released except the downloadable final BERTax model
missing metadata: version: reason: unreported; note: Tool versions are not stated in the article or SI, except MMseqs2-linclust 11.e1a1c used for dataset clustering
review: method: source-hash-verification; scope-review; reviewer: claude; reviewer note: Separate Claude review agent (rev-mlvb), independent of the extracting agent; no human review claimed; date: 2026-10-12; artifact sha256: c0bb2f1bba8680b300076292389b39346ae58bea4bdf28647dd9462333b3bcd7; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9436379/fullTextXML; note: Checked against the re-downloaded article XML (SHA-256 matches) and SI.
provenance note: MMseqs2 taxonomy* as run by the BERTax authors with the final dataset's training partition as reference or training data.

Related records

configuration of: MMseqs2
system: MMseqs2 taxonomy* on the BERTax final model dataset

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