Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Memory needed to store each tool's database or model weights, and reads classified per second with four CPUs.
Overview
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Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
6 recorded evaluations, 13 metric rows. A comparison chart has not yet been validated for these results. The table retains the individual findings and their sources.
Results
Results are available, but no reviewed comparison panel is linked in this release.
All evaluations
6 evaluations · 13 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) | Inference throughput (reads classified per second with four CPUs, including database loading time; value printed in the text) |
|---|---|---|---|
| MetageNN (main database, Peres da Silva et al. 2024)Evaluation | 1424 (best in column) | 0.839 | Not reported |
| MEGAN-LR (main database, Peres da Silva et al. 2024)Evaluation | 606 | 5.1 | Not reported |
| MMseqs2 (main database, Peres da Silva et al. 2024)Evaluation | 588 | 27.8 | Not reported |
| MetaMaps (main database, Peres da Silva et al. 2024)Evaluation | 194 | 2.2 | Not reported |
| GeNet (main database, Peres da Silva et al. 2024)Evaluation | 134 | 0.758 (best in column) | Not reported |
| Kraken2 (main database, Peres da Silva et al. 2024)Evaluation | 13.5 | 3.7 | 13,471 |
All 13 result rows with coverage, uncertainty and sources
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Source checking is not independent reproduction. Release 2026-10-22-1b7cf027e4f6.
Methods and evaluation design
Procedure, tasks and evaluated configurations
Recorded evaluations
Each evaluation records what was tested and under which conditions.
- GeNet on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
- Kraken2 on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
- MEGAN-LR on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
- MetageNN on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
- MetaMaps on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
- MMseqs2 on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
Baseline coverage
Reference methods help show what a model adds beyond simple controls. We track a null control and a conventional method for each protocol.
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- Author-reported evaluations
- 6
Literature evidence is not a Rewire measurement. Executed but unpublished runs and private review status are not included.
Null control
Proposed control: requires review
Training-set class prior where supervised fitting is permitted
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Conventional reference
Proposed control: requires review
Regularised classifier on simple permitted features, or protocol's conventional reference
Protocol-specific applicability, permitted inputs, access, split, evaluator and execution requirements need review before implementation or execution.
This is a suggested selection rule, not a validated method or a measured score.
Protocol coverage CSV (gzip) · Model evaluation matrix (gzip) · Source table (gzip) · Release and checksums (gzip)
Coverage is derived from release 2026-10-22-1b7cf027e4f6. Source citations describe the original records; they do not validate an unreviewed baseline proposal. No results have been generated by this audit.
Run instructions
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Strengths, limitations and unresolved questions
Evidence
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Evidence table
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Sources and history
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
1 source record and release history
- MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Original source · BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML
Technical metadata and extraction receipts
Stable ID: dna-pathogen-20261012-metagenn-protocol-resources
- areas
- microbes-communities
- contexts
- research
- protocol
- Memory needed to store each tool's database or model weights, and reads classified per second with four CPUs.
- version
- BMC Bioinformatics 2024
- metric definition
- Speed includes database loading (Kraken2) and k-mer counting and dataset loading (MetageNN); four CPUs for all tools.
- source locator
- Table 1; Results 'MetageNN is faster and requires less storage than alignment and k-mer-based tools, respectively'
- limitations
- The article does not say which ONT reads were timed; the dataset link is the isolate set.
- 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: ac4b137508d295d5e04e9be263bef40bdcf2c494e096407e5896611194f14e12; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC11022314/fullTextXML; note: Checked against the re-downloaded article XML (SHA-256 matches).
Related records
- uses data: ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024)
- part of: MetageNN long-read genus classification benchmark (Peres da Silva et al. 2024)
- assessment: GeNet on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
- assessment: Kraken2 on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
- assessment: MEGAN-LR on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
- assessment: MetageNN on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
- assessment: MetaMaps on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)
- assessment: MMseqs2 on database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024)