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While alignment-based methods are generally slower, k-mer-based taxonomic classifiers can overcome this limitation, potentially at the expense of lower sensitivity for strains and species that are not in the database.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We present MetageNN, a memory-efficient long-read taxonomic classifier that is robust to sequencing errors and missing genomes. MetageNN is a neural network model that uses short k-mer profiles of sequences to reduce the impact of distribution shifts on error-prone long reads. Benchmarking MetageNN against other machine learning approaches for taxonomic classification (GeNet) showed substantial improvements with long-read data (20% improvement in F1 score). By utilizing nanopore sequencing data, MetageNN exhibits improved sensitivity in situations where the reference database is incomplete. It surpasses the alignment-based MetaMaps and MEGAN-LR, as well as the k-mer-based Kraken2 tools, with improvements of 100%, 36%, and 23% respectively at the read-level analysis. Notably, at the community level, MetageNN consistently demonstrated higher sensitivities than the previously mentioned tools. Furthermore, MetageNN requires\u2009&lt;\u20091\/4th of the database storage used by Kraken2, MEGAN-LR and MMseqs2 and is\u2009&gt;\u20097\u00d7\u2009faster than MetaMaps and GeNet and\u2009&gt;\u20092\u00d7\u2009faster than MEGAN-LR and MMseqs2.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>This proof of concept work demonstrates the utility of machine-learning-based methods for taxonomic classification using long reads. MetageNN can be used on sequences not classified by conventional methods and offers an alternative approach for memory-efficient classifiers that can be optimized further.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-024-05760-3","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T10:23:19Z","timestamp":1713262999000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes"],"prefix":"10.1186","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0854-1595","authenticated-orcid":false,"given":"Rafael","family":"Peres da Silva","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chayaporn","family":"Suphavilai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niranjan","family":"Nagarajan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"issue":"6","key":"5760_CR1","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1038\/s41576-019-0113-7","volume":"20","author":"CY Chiu","year":"2019","unstructured":"Chiu CY, Miller SA. 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