{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:55Z","timestamp":1740185155470,"version":"3.37.3"},"reference-count":11,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T00:00:00Z","timestamp":1620864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"EFRE.NRW program OsteoSys","award":["EFRE-0800427","LS-1-1-019c"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,25]]},"abstract":"Abstract<\/jats:title>\n \n Motivation<\/jats:title>\n Clustering T-cell receptor repertoire (TCRR) sequences according to antigen specificity is challenging. The previously published tool GLIPH needs several days to weeks for clustering large repertoires, making its use impractical in larger studies. In addition, the methodology used in GLIPH suffers from shortcomings, including non-determinism, potential loss of significant antigen-specific sequences or inclusion of too many unspecific sequences.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n We present an algorithm for clustering TCRR sequences that scales efficiently to large repertoires. We clustered 36 real datasets with up to 62 000 unique CDR3\u03b2 sequences using both an implementation of our method called ting, GLIPH and its successor GLIPH2. While GLIPH required multiple weeks, ting only needed about one minute for the same task. GLIPH2 is comparably fast, but uses a different grouping paradigm. In addition, we found that in na\u00efve repertoires, where no or very few antigen-specific CDR3 sequences or clusters should exist, our method indeed selects much fewer motifs and produces smaller clusters.<\/jats:p>\n <\/jats:sec>\n \n Availability and implementation<\/jats:title>\n Our method has been implemented in Python as a tool called ting. It is available from GitHub (https:\/\/github.com\/FelixMoelder\/ting) or PyPI under the MIT license.<\/jats:p>\n <\/jats:sec>\n \n Supplementary information<\/jats:title>\n Supplementary data are available at Bioinformatics online.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab361","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T19:15:20Z","timestamp":1620760520000},"page":"3444-3448","source":"Crossref","is-referenced-by-count":1,"title":["Rapid T-cell receptor interaction grouping with ting"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3976-9701","authenticated-orcid":false,"given":"Felix","family":"M\u00f6lder","sequence":"first","affiliation":[{"name":"Genome Informatics, Institute of Human Genetics, University of Duisburg-Essen , 45147 Essen, Germany"},{"name":"Institute of Pathology, University of Duisburg-Essen , 45147 Essen, Germany"}]},{"given":"Ulrik","family":"Stervbo","sequence":"additional","affiliation":[{"name":"Center for Translational Medicine, Medical Clinic I, Marien Hospital Herne, Ruhr-University Bochum , 44623 Herne, Germany"}]},{"given":"Lucie","family":"Loyal","sequence":"additional","affiliation":[{"name":"Si-M\/\u201cDer Simulierte Mensch\u201d a Science Framework of Technische Universit\u00e4t Berlin and Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin , 13353 Berlin, Germany"},{"name":"Berlin Institute of Health, Berlin-Brandenburg Center for Regenerative Therapies , 13353 Berlin, Germany"},{"name":"Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Corporate Member of Freie Universit\u00e4t Berlin, Humboldt-Universit\u00e4t zu Berlin , 13353 Berlin, Germany"}]},{"given":"Petra","family":"Bacher","sequence":"additional","affiliation":[{"name":"Institute of Immunology, Christian-Albrechts Universit\u00e4t zu Kiel and Universit\u00e4tsklinik Schleswig-Holstein , 24105 Kiel, Germany"},{"name":"Institute of Clinical Molecular Biology, Christian-Albrechts Universit\u00e4t zu Kiel , 24118 Kiel, Germany"}]},{"given":"Nina","family":"Babel","sequence":"additional","affiliation":[{"name":"Center for Translational Medicine, Medical Clinic I, Marien Hospital Herne, Ruhr-University Bochum , 44623 Herne, Germany"},{"name":"Berlin Institute of Health, Berlin-Brandenburg Center for Regenerative Therapies , 13353 Berlin, Germany"},{"name":"Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Corporate Member of Freie Universit\u00e4t Berlin, Humboldt-Universit\u00e4t zu Berlin , 13353 Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8536-6065","authenticated-orcid":false,"given":"Sven","family":"Rahmann","sequence":"additional","affiliation":[{"name":"Genome Informatics, Institute of Human Genetics, University of Duisburg-Essen , 45147 Essen, Germany"},{"name":"Algorithmic Bioinformatics, Center for Bioinformatics and Department of Computer Science, Saarland University , 66123 Saarbr\u00fccken, Germany"}]}],"member":"286","published-online":{"date-parts":[[2021,5,13]]},"reference":[{"key":"2023051609020377100_btab361-B1","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1016\/j.cell.2019.01.041","article-title":"Human anti-fungal Th17 immunity and pathology rely on cross-reactivity against Candida albicans","volume":"176","author":"Bacher","year":"2019","journal-title":"Cell"},{"key":"2023051609020377100_btab361-B2","volume-title":"Introduction to Algorithms","author":"Cormen","year":"2009","edition":"3rd edn"},{"key":"2023051609020377100_btab361-B3","volume-title":"Statistical Methods for Research Workers","author":"Fisher","year":"1934","edition":"5th edn"},{"key":"2023051609020377100_btab361-B4","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1145\/364099.364331","article-title":"An improved equivalence algorithm","volume":"7","author":"Galler","year":"1964","journal-title":"Commun. 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