{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T19:05:37Z","timestamp":1780513537111,"version":"3.54.1"},"reference-count":28,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Merck Sharp & Dohme LLC"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Viruses"],"abstract":"<jats:p>Metagenomics analysis is a critical tool in identifying and typing viral samples to aid surveillance, clinical, epidemiological, and other workflows. Despite advances in sequencing technology and analysis pipelines, there are still limitations that lead to reduced taxonomic resolution or false positives from highly recombinant or challenging samples. Here we describe MGtree, a novel metagenomics pipeline that utilizes a combination of full-length read alignments and phylogenetic analysis to classify samples of interest. We demonstrate that MGtree accurately genotypes viral samples from challenging norovirus and HPV datasets. MGtree outperforms the popular metagenomics programs Kraken2 and Centrifuge, and it succeeds with low-input samples where de novo assembly fails. MGtree\u2019s correct assignments across highly mutant and coinfected samples highlights its ability to resolve viral genotypes and its potential to improve classification precision in complex samples.<\/jats:p>","DOI":"10.3390\/v18060643","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:08:07Z","timestamp":1780510087000},"page":"643","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MGtree: A Fast and Flexible Alignment-Based Metagenomics Pipeline"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1158-4633","authenticated-orcid":false,"given":"Samantha L.","family":"Sholes","sequence":"first","affiliation":[{"name":"Department of Data Science & Scientific Informatics, MRL\u2014Information Technology, Merck & Co., Inc., West Point, PA 19486, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1366-0628","authenticated-orcid":false,"given":"Scott","family":"Norton","sequence":"additional","affiliation":[{"name":"Department of Data Science & Scientific Informatics, MRL\u2014Information Technology, Merck & Co., Inc., Cambridge, MA 02141, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfredo","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"Department of Data Science & Scientific Informatics, MRL\u2014Information Technology, Merck & Co., Inc., Cambridge, MA 02141, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0155-4123","authenticated-orcid":false,"given":"John M.","family":"Gaspar","sequence":"additional","affiliation":[{"name":"Department of Data Science & Scientific Informatics, MRL\u2014Information Technology, Merck & Co., Inc., Cambridge, MA 02141, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1038\/s12276-024-01262-7","article-title":"Genome-resolved metagenomics: A game changer for microbiome medicine","volume":"56","author":"Kim","year":"2024","journal-title":"Exp. 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