MEGAN-LR (main database, Peres da Silva et al. 2024)

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2 evaluations · 3 results. Different protocols are not a single leaderboard.

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	Protocol and dataset	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)	Proportion (share of reads assigned at genus level (correct or not); mean across 30 pseudo-mock communities of out-of-database species)
MEGAN-LR (main database, Peres da Silva et al. 2024)Evaluation	Database storage and classification speed on ONT data, main database (Peres da Silva et al. 2024), ONT reads from 81 bacterial isolates (Peres da Silva et al. 2024)	606	5.1	Not reported
MEGAN-LR (main database, Peres da Silva et al. 2024)Evaluation	Read classification on 30 ONT pseudo-mock communities of out-of-database species (Peres da Silva et al. 2024), 30 ONT pseudo-mock communities of out-of-database species (Peres da Silva et al. 2024)	Not reported	Not reported	53.1%

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Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: MEGAN-LR (main database, Peres da Silva et al. 2024)	Protocol: Read classification on 30 ONT pseudo-mock communities of out-of-database species (Peres da Silva et al. 2024) Dataset: 30 ONT pseudo-mock communities of out-of-database species (Peres da Silva et al. 2024)	53.1% Proportion (share of reads assigned at genus level (correct or not); mean across 30 pseudo-mock communities of out-of-database species) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source MEGAN-LR on read classification on 30 ONT pseudo-mock communities of out-of-database species (Peres da Silva et al. 2024) dna-pathogen-20261012-metagenn-protocol-mocks Aggregation: Not reported MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Results 'Read classification results' paragraph 1 (values shown in Fig. 4a)
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 checked Methods, coverage and source MEGAN-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)"
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 checked Methods, coverage and source MEGAN-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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Release 2026-10-22-1b7cf027e4f6 · Record review: source checked

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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

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Technical metadata and extraction receipts

Stable ID: dna-pathogen-20261012-metagenn-config-megan-lr

areas: microbes-communities
contexts: research
method types: conventional_pipeline
reported name: MEGAN-LR
foundation model eligible: false
parameters: MEGAN-LR on minimap2 nucleotide alignments to the main-database genomes (MEGAN-LR-nuc in Portik et al. 2022); default settings
source locator: Results 'MetageNN presents a higher F1 score than GeNet...' Baselines and Experimental setup; Methods 'Databases'
access reuse: Tool run by the article's authors; the trained MetageNN model is not linked from the article
missing metadata: version: reason: unreported; note: No tool versions are given
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).
provenance note: MEGAN-LR as run by the MetageNN authors with the 516-genome main database.

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