Dataset

BERTax Final model dataset (Mock et al. 2022)

1,500-nt genome fragments from NCBI RefSeq genomes of archaea, bacteria, eukaryotes and viruses, clustered at 80% identity with MMseqs2-linclust.

Evaluation results

9 evaluations · 27 results. Different protocols are not a single leaderboard.

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: BERTax (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)	66.9% 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 BERTax 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 "BERTax", column group "Final", column "Genus" (PNAS PDF page 4)
Configuration: BERTax (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)	95.1% 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 BERTax 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 "BERTax", column group "Final", column "Phyl." (PNAS PDF page 4)
Configuration: BERTax (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)	98.6% 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 BERTax 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 "BERTax", column group "Final", column "Supk." (PNAS PDF page 4)
Configuration: DeepMicrobes (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)	66.4% 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 DeepMicrobes 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 "DeepMicrobes", column group "Final", column "Genus" (PNAS PDF page 4)
Configuration: DeepMicrobes (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)	92.1% 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 DeepMicrobes 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 "DeepMicrobes", column group "Final", column "Phyl." (PNAS PDF page 4)
Configuration: DeepMicrobes (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 DeepMicrobes 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 "DeepMicrobes", column group "Final", column "Supk." (PNAS PDF page 4)
Configuration: Kraken2 (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)	70.6% 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 Kraken2 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 "Kraken2", column group "Final", column "Genus" (PNAS PDF page 4)
Configuration: Kraken2 (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)	87.1% 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 Kraken2 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 "Kraken2", column group "Final", column "Phyl." (PNAS PDF page 4)
Configuration: Kraken2 (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)	93.7% 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 Kraken2 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 "Kraken2", column group "Final", column "Supk." (PNAS PDF page 4)
Configuration: minimap2 (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)	66.7% 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 minimap2 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 "minimap2", column group "Final", column "Genus" (PNAS PDF page 4)
Configuration: minimap2 (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)	86.7% 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 minimap2 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 "minimap2", column group "Final", column "Phyl." (PNAS PDF page 4)
Configuration: minimap2 (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 (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 minimap2 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 "minimap2", column group "Final", column "Supk." (PNAS PDF page 4)
Configuration: MMseqs2 + BERTax (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)	79.3% 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 + BERTax 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+ BERTax", column group "Final", column "Genus" (PNAS PDF page 4)
Configuration: MMseqs2 + BERTax (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)	97.8% 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 + BERTax 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+ BERTax", column group "Final", column "Phyl." (PNAS PDF page 4)
Configuration: MMseqs2 + BERTax (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)	99.8% 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 + BERTax 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+ BERTax", column group "Final", column "Supk." (PNAS PDF page 4)
Configuration: MMseqs2 + DeepMicrobes (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)	77.8% 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 + DeepMicrobes 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+ DeepMicrobes", column group "Final", column "Genus" (PNAS PDF page 4)
Configuration: MMseqs2 + DeepMicrobes (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)	93.2% 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 + DeepMicrobes 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+ DeepMicrobes", column group "Final", column "Phyl." (PNAS PDF page 4)
Configuration: MMseqs2 + DeepMicrobes (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)	97.2% 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 + DeepMicrobes 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+ DeepMicrobes", column group "Final", column "Supk." (PNAS PDF page 4)
Configuration: MMseqs2 (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)	74.8% 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 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", column group "Final", column "Genus" (PNAS PDF page 4)
Configuration: MMseqs2 (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)	92.9% 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 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", column group "Final", column "Phyl." (PNAS PDF page 4)
Configuration: MMseqs2 (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)	96.9% 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 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", column group "Final", column "Supk." (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)	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)
Configuration: sourmash (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)	3.07% 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 sourmash 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 "sourmash", column group "Final", column "Genus" (PNAS PDF page 4)

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7 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-22-1b7cf027e4f6
Property and statement	Original source and location	Review and provenance
Access Genome list in Dataset S1 (pnas.2122636119.sd01.xlsx); the fragment sets themselves are not linked in the article Context-only references	Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks Original source ↗ Materials and Methods 'Data', 'Pretraining Dataset', 'Preparing Data for Fine-Tuning', 'Three Evaluation Datasets'; Table 2; Fig. 4 Version: PNAS 119(35):e2122636119, published 2022-08-26; PMC9436379 full-text XML Retrieved: 2026-10-12T20:51:54Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.access` Source artifact SHA-256: `c0bb2f1bba8680b300076292389b39346ae58bea4bdf28647dd9462333b3bcd7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Population 5 superkingdom, 44 phylum and 156 genus classes, including 'unknown' Context-only references	Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks Original source ↗ Materials and Methods 'Data', 'Pretraining Dataset', 'Preparing Data for Fine-Tuning', 'Three Evaluation Datasets'; Table 2; Fig. 4 Version: PNAS 119(35):e2122636119, published 2022-08-26; PMC9436379 full-text XML Retrieved: 2026-10-12T20:51:54Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.population` Source artifact SHA-256: `c0bb2f1bba8680b300076292389b39346ae58bea4bdf28647dd9462333b3bcd7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Source location Materials and Methods 'Data', 'Pretraining Dataset', 'Preparing Data for Fine-Tuning', 'Three Evaluation Datasets'; Table 2; Fig. 4 Context-only references	Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks Original source ↗ Materials and Methods 'Data', 'Pretraining Dataset', 'Preparing Data for Fine-Tuning', 'Three Evaluation Datasets'; Table 2; Fig. 4 Version: PNAS 119(35):e2122636119, published 2022-08-26; PMC9436379 full-text XML Retrieved: 2026-10-12T20:51:54Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.source_locator` Source artifact SHA-256: `c0bb2f1bba8680b300076292389b39346ae58bea4bdf28647dd9462333b3bcd7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Split Closely related selection on an expanded fragment set (additional 2 million eukaryotic and bacterial fragments); classes with fewer than 10,000 fragments are pooled into an 'unknown' class at each rank. Context-only references	Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks Original source ↗ Materials and Methods 'Data', 'Pretraining Dataset', 'Preparing Data for Fine-Tuning', 'Three Evaluation Datasets'; Table 2; Fig. 4 Version: PNAS 119(35):e2122636119, published 2022-08-26; PMC9436379 full-text XML Retrieved: 2026-10-12T20:51:54Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.split` Source artifact SHA-256: `c0bb2f1bba8680b300076292389b39346ae58bea4bdf28647dd9462333b3bcd7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Version As described in Mock et al. 2022 (genome list and assembly versions in Dataset S1) Context-only references	Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks Original source ↗ Materials and Methods 'Data', 'Pretraining Dataset', 'Preparing Data for Fine-Tuning', 'Three Evaluation Datasets'; Table 2; Fig. 4 Version: PNAS 119(35):e2122636119, published 2022-08-26; PMC9436379 full-text XML Retrieved: 2026-10-12T20:51:54Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.version` Source artifact SHA-256: `c0bb2f1bba8680b300076292389b39346ae58bea4bdf28647dd9462333b3bcd7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Description 1,500-nt genome fragments from NCBI RefSeq genomes of archaea, bacteria, eukaryotes and viruses, clustered at 80% identity with MMseqs2-linclust. Context-only references	Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks Original source ↗ Materials and Methods 'Data', 'Pretraining Dataset', 'Preparing Data for Fine-Tuning', 'Three Evaluation Datasets'; Table 2; Fig. 4 Version: PNAS 119(35):e2122636119, published 2022-08-26; PMC9436379 full-text XML Retrieved: 2026-10-12T20:51:54Z	not individually reviewed No individual claim review recorded Audit details Field: `description` Source artifact SHA-256: `c0bb2f1bba8680b300076292389b39346ae58bea4bdf28647dd9462333b3bcd7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Name BERTax Final model dataset (Mock et al. 2022) Context-only references	Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks Original source ↗ Materials and Methods 'Data', 'Pretraining Dataset', 'Preparing Data for Fine-Tuning', 'Three Evaluation Datasets'; Table 2; Fig. 4 Version: PNAS 119(35):e2122636119, published 2022-08-26; PMC9436379 full-text XML Retrieved: 2026-10-12T20:51:54Z	not individually reviewed No individual claim review recorded Audit details Field: `name` Source artifact SHA-256: `c0bb2f1bba8680b300076292389b39346ae58bea4bdf28647dd9462333b3bcd7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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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-data-final

areas: microbes-communities
contexts: research
version: As described in Mock et al. 2022 (genome list and assembly versions in Dataset S1)
population: 5 superkingdom, 44 phylum and 156 genus classes, including 'unknown'
split: Closely related selection on an expanded fragment set (additional 2 million eukaryotic and bacterial fragments); classes with fewer than 10,000 fragments are pooled into an 'unknown' class at each rank.
access: Genome list in Dataset S1 (pnas.2122636119.sd01.xlsx); the fragment sets themselves are not linked in the article
source locator: Materials and Methods 'Data', 'Pretraining Dataset', 'Preparing Data for Fine-Tuning', 'Three Evaluation Datasets'; Table 2; Fig. 4
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.

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