{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T05:09:33Z","timestamp":1774156173252,"version":"3.50.1"},"reference-count":11,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,11,25]],"date-time":"2016-11-25T00:00:00Z","timestamp":1480032000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2016,11,25]],"date-time":"2016-11-25T00:00:00Z","timestamp":1480032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["F31NS090883"],"award-info":[{"award-number":["F31NS090883"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R01NS086810"],"award-info":[{"award-number":["R01NS086810"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["T32CA140044"],"award-info":[{"award-number":["T32CA140044"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"publisher","award":["R01HG007068"],"award-info":[{"award-number":["R01HG007068"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Active protein translation can be assessed and measured using ribosome profiling sequencing strategies. Prevailing analytical approaches applied to this technology make use of sequence fragment length profiling or reading frame occupancy enrichment to differentiate between active translation and background noise, however they do not consider additional characteristics inherent to the technology which limits their overall accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we present an analytical tool that models the overall tri\u00adnucleotide periodicity of ribosomal occupancy using a classifier based on spectral coherence. Our software, SPECtre, examines the relationship of normalized ribosome profiling read coverage over a rolling series of windows along a transcript relative to an idealized reference signal without the matched requirement of mRNA-Seq.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      A comparison of SPECtre against previously published methods on existing data shows a marked improvement in accuracy for detecting active translation and exhibits overall high accuracy at a low false discovery rate. In addition, SPECtre performs comparably to a recently published method similarly based on spectral coherence, however with reduced runtime and memory requirements. SPECtre is available as an open source software package at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/mills-lab\/spectre\">https:\/\/github.com\/mills-lab\/spectre<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-016-1355-4","type":"journal-article","created":{"date-parts":[[2016,11,24]],"date-time":"2016-11-24T19:50:43Z","timestamp":1480017043000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["SPECtre: a spectral coherence-\u00adbased classifier of actively translated transcripts from ribosome profiling sequence data"],"prefix":"10.1186","volume":"17","author":[{"given":"Sang Y.","family":"Chun","sequence":"first","affiliation":[]},{"given":"Caitlin M.","family":"Rodriguez","sequence":"additional","affiliation":[]},{"given":"Peter K.","family":"Todd","sequence":"additional","affiliation":[]},{"given":"Ryan E.","family":"Mills","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,11,25]]},"reference":[{"issue":"2","key":"1355_CR1","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1093\/bioinformatics\/btu638","volume":"31","author":"S Anders","year":"2015","unstructured":"Anders S, Pyl PT, Huber W. HTSeq \u2013 a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166\u20139.","journal-title":"Bioinformatics"},{"key":"1355_CR2","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1002\/embj.201488411","volume":"33","author":"AA Bazzini","year":"2014","unstructured":"Bazzini AA, Johnstone TG, Christiano R, Mackowiak SD, Obermayer B, Fleming ES, Vejnar CE, Lee MT, Rajewsky N, Walther TC, Giraldez AJ. Identification of small ORFs in vertebrates using ribosome footprinting and evolutionary conservation. EMBO J. 2014;33:981\u201393.","journal-title":"EMBO J"},{"key":"1355_CR3","unstructured":"Bendat JS, Piersol AG. Random Data: Analysis and Management Procedures. Wiley Interscience. 1986."},{"key":"1355_CR4","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1038\/nmeth.3688","volume":"13","author":"L Calviello","year":"2015","unstructured":"Calviello L, Mukherjee N, Wyler E, Zauber H, Hirsekorn A, Selbach M, Landthaler M, Obermayer B, Ohler U. Detecting actively translated open reading frames in ribosome profiling data. 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