{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T22:51:41Z","timestamp":1762642301587},"reference-count":10,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T00:00:00Z","timestamp":1577664000000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>In many research disciplines, ordered lists are compared. One example is to compare a subset of all significant genes or proteins in a primary study to those in a replication study. Often, the top of the lists are compared using Venn diagrams, ore more precisely Euler diagrams (set diagrams showing logical relations between a finite collection of different sets). If different cohort sizes, different techniques or algorithms for evaluation were applied, a direct comparison of significant genes with a fixed threshold can however be misleading and approaches comparing lists would be more appropriate.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>We developed <jats:italic>DynaVenn<\/jats:italic>, a web-based tool that incrementally creates all possible subsets from two or three ordered lists and computes for each combination a p-value for the overlap. Respectively, dynamic Venn diagrams are generated as graphical representations. Additionally an animation is generated showing how the most significant overlap is reached by backtracking. We demonstrate the improved performance of <jats:italic>DynaVenn<\/jats:italic> over an arbitrary cut-off approach on an Alzheimer\u2019s Disease biomarker set.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p><jats:italic>DynaVenn<\/jats:italic> combines the calculation of the most significant overlap of different cohorts with an intuitive visualization of the results. It is freely available as a web service at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/www.ccb.uni-saarland.de\/dynavenn\">http:\/\/www.ccb.uni-saarland.de\/dynavenn<\/jats:ext-link>.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12859-019-3320-5","type":"journal-article","created":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T22:02:51Z","timestamp":1577743371000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["DynaVenn: web-based computation of the most significant overlap between ordered sets"],"prefix":"10.1186","volume":"20","author":[{"given":"J\u00e9r\u00e9my","family":"Amand","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tobias","family":"Fehlmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christina","family":"Backes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Keller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,30]]},"reference":[{"issue":"1","key":"3320_CR1","doi-asserted-by":"publisher","first-page":"21499","DOI":"10.1038\/srep21499","volume":"6","author":"C Dou","year":"2016","unstructured":"Dou C, Cao Z, Yang B, Ding N, Hou T, Luo F, Kang F, Li J, Yang X, Jiang H, Xiang J, Quan H, Xu J, Dong S. 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Alzheimer\u2019s Dementia. 2016; 12(5):565\u201376. https:\/\/doi.org\/10.1016\/J.JALZ.2015.12.012.","journal-title":"Alzheimer\u2019s Dementia"},{"key":"3320_CR10","doi-asserted-by":"publisher","unstructured":"Ludwig N, Fehlmann T, Kern F, Gogol M, Maetzler W, Deutscher S, Gurlit S, Schulte C, von Thaler AK, Deuschle C, Metzger F, Berg D, Suenkel U, Keller V, Backes C, Lenhof HP, Meese E, Keller A. Machine Learning to Detect Alzheimer\u2019s Disease from Circulating Non-coding RNAs. Genomics Proteom Bioinforma. 2019. Dec 4. pii: S1672-0229(19)30157-3. https:\/\/doi.org\/10.1016\/j.gpb.2019.09.004. 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