{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T04:07:17Z","timestamp":1776312437467,"version":"3.50.1"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2019,3,13]],"date-time":"2019-03-13T00:00:00Z","timestamp":1552435200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"NASA","doi-asserted-by":"publisher","award":["NNH16ZTT001N-MOBE"],"award-info":[{"award-number":["NNH16ZTT001N-MOBE"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020017","name":"NASA\u2019s Planetary Science Division","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100020017","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Extreme Science and Engineering Discovery Environment"},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["ACI-1548562"],"award-info":[{"award-number":["ACI-1548562"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["TG-BCS180020"],"award-info":[{"award-number":["TG-BCS180020"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Genome-level evolutionary inference (i.e. phylogenomics) is becoming an increasingly essential step in many biologists\u2019 work. Accordingly, there are several tools available for the major steps in a phylogenomics workflow. But for the biologist whose main focus is not bioinformatics, much of the computational work required\u2014such as accessing genomic data on large scales, integrating genomes from different file formats, performing required filtering, stitching different tools together etc.\u2014can be prohibitive. Here I introduce GToTree, a command-line tool that can take any combination of fasta files, GenBank files and\/or NCBI assembly accessions as input and outputs an alignment file, estimates of genome completeness and redundancy, and a phylogenomic tree based on a specified single-copy gene (SCG) set. Although GToTree can work with any custom hidden Markov Models (HMMs), also included are 13 newly generated SCG-set HMMs for different lineages and levels of resolution, built based on searches of \u223c12\u00a0000 bacterial and archaeal high-quality genomes. GToTree aims to give more researchers the capability to make phylogenomic trees.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>GToTree is open-source and freely available for download from: github.com\/AstrobioMike\/GToTree. It is implemented primarily in bash with helper scripts written in python.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz188","type":"journal-article","created":{"date-parts":[[2019,3,12]],"date-time":"2019-03-12T08:38:55Z","timestamp":1552379935000},"page":"4162-4164","source":"Crossref","is-referenced-by-count":448,"title":["GToTree: a user-friendly workflow for phylogenomics"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7750-9145","authenticated-orcid":false,"given":"Michael D","family":"Lee","sequence":"first","affiliation":[{"name":"Exobiology Branch, NASA Ames Research Center , Moffett Field, CA, USA"}]}],"member":"286","published-online":{"date-parts":[[2019,3,13]]},"reference":[{"key":"2023013108284605500_btz188-B1","doi-asserted-by":"crossref","first-page":"180154","DOI":"10.1038\/sdata.2018.154","article-title":"Single cell genomes of Prochlorococcus, Synechococcus, and sympatric microbes from diverse marine environments","volume":"5","author":"Berube","year":"2018","journal-title":"Sci. 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Biol"},{"key":"2023013108284605500_btz188-B4","doi-asserted-by":"crossref","first-page":"113.","DOI":"10.1186\/1471-2105-5-113","article-title":"MUSCLE: a multiple sequence alignment method with reduced time and space complexity","volume":"5","author":"Edgar","year":"2004","journal-title":"BMC Bioinformatics"},{"key":"2023013108284605500_btz188-B5","doi-asserted-by":"crossref","first-page":"D427","DOI":"10.1093\/nar\/gky995","article-title":"The Pfam protein families database in 2019","volume":"47","author":"El-Gebali","year":"2019","journal-title":"Nucleic Acid Res"},{"key":"2023013108284605500_btz188-B6","doi-asserted-by":"crossref","first-page":"e1319","DOI":"10.7717\/peerj.1319","article-title":"Anvi\u2019o: an advanced analysis and visualization platform for \u2018omics data","volume":"3","author":"Eren","year":"2015","journal-title":"PeerJ"},{"key":"2023013108284605500_btz188-B7","doi-asserted-by":"crossref","first-page":"1972","DOI":"10.1093\/bioinformatics\/btp348","article-title":"TrimAl: a Tool for automatic alignment trimming","volume":"25","author":"Gut\u00ederrez","year":"2009","journal-title":"Bioinformatics"},{"key":"2023013108284605500_btz188-B8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/nmicrobiol.2016.48","article-title":"A new view of the tree of life","volume":"1","author":"Hug","year":"2016","journal-title":"Nat. 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