| Configuration: GeNet (main database, Peres da Silva et al. 2024) | Protocol: Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 134 Inference throughput (reads classified per second with four CPUs) sample-per-second · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceGeNet on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-resources Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Table 1, row "GeNet", column "Speed (reads/sec)" |
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| Configuration: GeNet (main database, Peres da Silva et al. 2024) | Protocol: Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 0.758 Reference storage size (database or index (model weights for MetageNN and GeNet) built from the 516-genome main database) gigabyte · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceGeNet on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-resources Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Table 1, row "GeNet", column "Memory (GB)" |
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| Configuration: Kraken2 (main database, Peres da Silva et al. 2024) | Protocol: Genus-level read classification of ONT bacterial isolates, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 0.83 F1 (genus level; mean across all 81 isolates (species in and out of the database)) fraction · higher Uncertainty: Not reported by the source: Figure 3 shows standard-deviation error bars; no value is printed in the text Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceKraken2 on genus-level read classification of ONT bacterial isolates, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-isolates Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Results 'Results for all genomes' paragraph 1, sentence 4 (values shown in Fig. 3a) |
|---|
| Configuration: Kraken2 (main database, Peres da Silva et al. 2024) | Protocol: Genus-level read classification of ONT bacterial isolates, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 0.79 Recall (genus level; mean across all 81 isolates (species in and out of the database)) fraction · higher Uncertainty: Not reported by the source: Figure 3 shows standard-deviation error bars; no value is printed in the text Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceKraken2 on genus-level read classification of ONT bacterial isolates, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-isolates Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Results 'Results for all genomes' paragraph 1, sentence 4 (values shown in Fig. 3a) |
|---|
| Configuration: Kraken2 (main database, Peres da Silva et al. 2024) | Protocol: Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 13.5 Inference throughput (reads classified per second with four CPUs) sample-per-second · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceKraken2 on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-resources Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Table 1, row "Kraken2", column "Speed (reads/sec)" |
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| Configuration: Kraken2 (main database, Peres da Silva et al. 2024) | Protocol: Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 13,471 Inference throughput (reads classified per second with four CPUs, including database loading time; value printed in the text) sample-per-second · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceKraken2 on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-resources Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Results 'MetageNN is faster and requires less storage than alignment and k-mer-based tools, respectively' paragraph 3, sentence 5 |
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| Configuration: Kraken2 (main database, Peres da Silva et al. 2024) | Protocol: Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 3.7 Reference storage size (database or index (model weights for MetageNN and GeNet) built from the 516-genome main database) gigabyte · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceKraken2 on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-resources Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Table 1, row "Kraken2", column "Memory (GB)" |
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| Configuration: MEGAN-LR (main database, Peres da Silva et al. 2024) | Protocol: Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 606 Inference throughput (reads classified per second with four CPUs) sample-per-second · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMEGAN-LR on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-resources Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Table 1, row "MEGAN-LR", column "Speed (reads/sec)" |
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| Configuration: MEGAN-LR (main database, Peres da Silva et al. 2024) | Protocol: Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 5.1 Reference storage size (database or index (model weights for MetageNN and GeNet) built from the 516-genome main database) gigabyte · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMEGAN-LR on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-resources Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Table 1, row "MEGAN-LR", column "Memory (GB)" |
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| Configuration: MetageNN (main database, Peres da Silva et al. 2024) | Protocol: Genus-level read classification of ONT bacterial isolates, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 0.88 F1 (genus level; mean across the out-of-database isolates whose genus has more than three species) fraction · higher Uncertainty: Not reported by the source: Figure 3 shows standard-deviation error bars; no value is printed in the text Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMetageNN on genus-level read classification of ONT bacterial isolates, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-isolates Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Results 'Results for “Species out of the database” settings' paragraph 3, last sentences (values shown in Fig. 3f) |
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| Configuration: MetageNN (main database, Peres da Silva et al. 2024) | Protocol: Genus-level read classification of ONT bacterial isolates, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 0.87 Recall (genus level; mean across the out-of-database isolates whose genus has more than three species) fraction · higher Uncertainty: Not reported by the source: Figure 3 shows standard-deviation error bars; no value is printed in the text Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMetageNN on genus-level read classification of ONT bacterial isolates, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-isolates Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Results 'Results for “Species out of the database” settings' paragraph 3, last sentences (values shown in Fig. 3f) |
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| Configuration: MetageNN (main database, Peres da Silva et al. 2024) | Protocol: Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 1420 Inference throughput (reads classified per second with four CPUs) sample-per-second · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMetageNN on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-resources Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Table 1, row "MetageNN", column "Speed (reads/sec)" |
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| Configuration: MetageNN (main database, Peres da Silva et al. 2024) | Protocol: Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 0.839 Reference storage size (database or index (model weights for MetageNN and GeNet) built from the 516-genome main database) gigabyte · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMetageNN on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-resources Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Table 1, row "MetageNN", column "Memory (GB)" |
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| Configuration: MetaMaps (main database, Peres da Silva et al. 2024) | Protocol: Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 194 Inference throughput (reads classified per second with four CPUs) sample-per-second · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMetaMaps on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-resources Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Table 1, row "MetaMaps", column "Speed (reads/sec)" |
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| Configuration: MetaMaps (main database, Peres da Silva et al. 2024) | Protocol: Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 2.2 Reference storage size (database or index (model weights for MetageNN and GeNet) built from the 516-genome main database) gigabyte · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMetaMaps on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-resources Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Table 1, row "MetaMaps", column "Memory (GB)" |
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| Configuration: MMseqs2 (main database, Peres da Silva et al. 2024) | Protocol: Genus-level read classification of ONT bacterial isolates, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 0.85 F1 (genus level; mean across all 81 isolates (species in and out of the database)) fraction · higher Uncertainty: Not reported by the source: Figure 3 shows standard-deviation error bars; no value is printed in the text Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMMseqs2 on genus-level read classification of ONT bacterial isolates, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-isolates Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Results 'Results for all genomes' paragraph 1, sentence 4 (values shown in Fig. 3a) |
|---|
| Configuration: MMseqs2 (main database, Peres da Silva et al. 2024) | Protocol: Genus-level read classification of ONT bacterial isolates, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 0.81 Recall (genus level; mean across all 81 isolates (species in and out of the database)) fraction · higher Uncertainty: Not reported by the source: Figure 3 shows standard-deviation error bars; no value is printed in the text Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMMseqs2 on genus-level read classification of ONT bacterial isolates, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-isolates Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Results 'Results for all genomes' paragraph 1, sentence 4 (values shown in Fig. 3a) |
|---|
| Configuration: MMseqs2 (main database, Peres da Silva et al. 2024) | Protocol: Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 588 Inference throughput (reads classified per second with four CPUs) sample-per-second · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMMseqs2 on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-resources Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Table 1, row "MMseqs2", column "Speed (reads/sec)" |
|---|
| Configuration: MMseqs2 (main database, Peres da Silva et al. 2024) | Protocol: Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Dataset: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024) | 27.8 Reference storage size (database or index (model weights for MetageNN and GeNet) built from the 516-genome main database) gigabyte · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMMseqs2 on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-resources Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Table 1, row "MMseqs2", column "Memory (GB)" |
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