{"doi":"10.1038/ncomms11257","title":"Fast and sensitive taxonomic classification for metagenomics with Kaiju","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>\n                    Metagenomics emerged as an important field of research not only in microbial ecology but also for human health and disease, and metagenomic studies are performed on increasingly larger scales. While recent taxonomic classification programs achieve high speed by comparing genomic\n                    <jats:italic>k</jats:italic>\n                    -mers, they often lack sensitivity for overcoming evolutionary divergence, so that large fractions of the metagenomic reads remain unclassified. Here we present the novel metagenome classifier Kaiju, which finds maximum (in-)exact matches on the protein-level using the Burrows–Wheeler transform. We show in a genome exclusion benchmark that Kaiju classifies reads with higher sensitivity and similar precision compared with current\n                    <jats:italic>k</jats:italic>\n                    -mer-based classifiers, especially in genera that are underrepresented in reference databases. We also demonstrate that Kaiju classifies up to 10 times more reads in real metagenomes. Kaiju can process millions of reads per minute and can run on a standard PC. Source code and web server are available at\n                    <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"http://kaiju.binf.ku.dk\">http://kaiju.binf.ku.dk</jats:ext-link>\n                    .\n                  </jats:p>","journal":"Nature Communications","year":2016,"id":603726,"datarank":1.1603805479928853,"base_score":7.735870319952567,"endowment":7.735870319952567,"self_citation_contribution":1.1603805479928853,"citation_network_contribution":0.0,"self_endowment_contribution":1.1603805479928853,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2288,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1294688,"name":"Kim Lee Ng","orcid":"0000-0002-2679-8845","position":1,"is_corresponding":false},{"id":54423,"name":"Anders Krogh","orcid":"0000-0002-5147-6282","position":2,"is_corresponding":false},{"id":55390,"name":"Peter Menzel","orcid":"0000-0003-4964-0685","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Fast and sensitive taxonomic classification for metagenomics with Kaiju","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>\n                    Metagenomics emerged as an important field of research not only in microbial ecology but also for human health and disease, and metagenomic studies are performed on increasingly larger scales. While recent taxonomic classification programs achieve high speed by comparing genomic\n                    <jats:italic>k</jats:italic>\n                    -mers, they often lack sensitivity for overcoming evolutionary divergence, so that large fractions of the metagenomic reads remain unclassified. Here we present the novel metagenome classifier Kaiju, which finds maximum (in-)exact matches on the protein-level using the Burrows–Wheeler transform. We show in a genome exclusion benchmark that Kaiju classifies reads with higher sensitivity and similar precision compared with current\n                    <jats:italic>k</jats:italic>\n                    -mer-based classifiers, especially in genera that are underrepresented in reference databases. We also demonstrate that Kaiju classifies up to 10 times more reads in real metagenomes. Kaiju can process millions of reads per minute and can run on a standard PC. Source code and web server are available at\n                    <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"http://kaiju.binf.ku.dk\">http://kaiju.binf.ku.dk</jats:ext-link>\n                    .\n                  </jats:p>","is_dataset_classified":null,"base_score":7.735870319952567,"endowment":7.735870319952567,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"27071849","pmcid":"PMC4833860","openalex_id":"https://openalex.org/W2337747100","authors":[],"funders":[{"funder_name":"Novo Nordisk Fonden","grant_id":"NNF10SA1016550","title":null},{"funder_name":"European Commission","grant_id":"265933","title":"Systematic screening for novel hydrolases from hot environments"}],"total_grants":2,"fwci":46.3384,"citation_percentile":0.99913745,"influential_citations":0,"citation_trend":[{"year":2015,"count":1},{"year":2016,"count":6},{"year":2017,"count":55},{"year":2018,"count":93},{"year":2019,"count":135},{"year":2020,"count":236},{"year":2021,"count":319},{"year":2022,"count":313},{"year":2023,"count":300},{"year":2024,"count":394},{"year":2025,"count":278},{"year":2026,"count":156}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.nature.com/articles/ncomms11257.pdf","host_type":"journal"},{"url":"https://www.nature.com/articles/ncomms11257.pdf","host_type":"publisher"},{"url":"https://www.nature.com/articles/ncomms11257","host_type":"publisher"},{"url":"https://doi.org/10.1038/ncomms11257","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/27071849","host_type":"repository"},{"url":"https://curis.ku.dk/portal/da/publications/fast-and-sensitive-taxonomic-classification-for-metagenomics-with-kaiju(ae6ac71d-284b-433f-a599-8ca124d0d9b9).html","host_type":"repository"},{"url":"https://doaj.org/article/4f73f958111e43eeb0aa68e7af207f39","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4833860","host_type":"repository"},{"url":"https://researchprofiles.ku.dk/da/publications/ae6ac71d-284b-433f-a599-8ca124d0d9b9","host_type":"repository"},{"url":"https://curis.ku.dk/ws/files/161630095/Fast_and_sensitive_taxonomic_classification.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC4833860","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC4833860?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.1038/ncomms11257","host_type":""},{"url":"https://dx.doi.org/10.1038/ncomms11257","host_type":""},{"url":"https://doi.org/https://doi.org/10.1038/ncomms11257","host_type":""}],"fields_of_study":["Genomics and Phylogenetic Studies","Microbial Community Ecology and Physiology","Gene expression and cancer classification","0301 basic medicine","0303 health sciences","03 medical and health sciences"],"mesh_terms":["Algorithms","Amino Acid Sequence","Animals","Classification","Humans","Proteins","Metagenome","Metagenomics"],"keywords":["Metagenomics","Classifier (UML)","Benchmark (surveying)","Genome","Computer science","Biological classification","Biology","Computational biology","Taxonomic rank","Data mining","Machine learning","Artificial intelligence","Ecology","Evolutionary biology","Genetics","Cartography","Gene","Taxon","Geography","Science","Q","Proteins","Classification","Article","Animals","Humans","Metagenome","Amino Acid Sequence","Algorithms"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. 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