Published long-read classification comparison; transcribed, not reproduced.
1 evaluation · 2 results. Different protocols are not a single leaderboard.
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)
Sorted by Inference throughput (reads classified per second with four CPUs) (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 | Inference throughput (reads classified per second with four CPUs) | Reference storage size (database or index (model weights for MetageNN and GeNet) built from the 516-genome main database) |
|---|---|---|
| MEGAN-LR (main database, Peres da Silva et al. 2024)Evaluation | 606 | 5.1 |
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Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
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19 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Comparison: adaptation All tools use the 516-genome main database Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Table 1; Results 'MetageNN is faster and requires less storage than alignment and k-mer-based tools, respectively' Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: aggregation Not reported Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Table 1; Results 'MetageNN is faster and requires less storage than alignment and k-mer-based tools, respectively' Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | missing or unspecified No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: budget Four CPUs Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Table 1; Results 'MetageNN is faster and requires less storage than alignment and k-mer-based tools, respectively' Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: dataset version Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024) Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Table 1; Results 'MetageNN is faster and requires less storage than alignment and k-mer-based tools, respectively' Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: inputs ONT R9.4.1 reads Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Table 1; Results 'MetageNN is faster and requires less storage than alignment and k-mer-based tools, respectively' Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: metric implementation Speed includes database loading (Kraken2) and k-mer counting and dataset loading (MetageNN); four CPUs for all tools. Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Table 1; Results 'MetageNN is faster and requires less storage than alignment and k-mer-based tools, respectively' Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: population ONT reads (the timed read set is not stated) Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Table 1; Results 'MetageNN is faster and requires less storage than alignment and k-mer-based tools, respectively' Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: protocol id dna-pathogen-20261012-metagenn-protocol-resources Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Table 1; Results 'MetageNN is faster and requires less storage than alignment and k-mer-based tools, respectively' Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: split Not stated Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Table 1; Results 'MetageNN is faster and requires less storage than alignment and k-mer-based tools, respectively' Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Limitations1 values
| MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Table 1; Results 'MetageNN is faster and requires less storage than alignment and k-mer-based tools, respectively' Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
Stable ID: dna-pathogen-20261012-metagenn-eval-megan-lr-resources