{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T03:00:49Z","timestamp":1785466849299,"version":"3.56.0"},"reference-count":9,"publisher":"Oxford University Press (OUP)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Summary: False discovery rate (FDR) methodologies are essential in the study of high-dimensional genomic and proteomic data. The R package \u2018fdrtool\u2019 facilitates such analyses by offering a comprehensive set of procedures for FDR estimation. Its distinctive features include: (i) many different types of test statistics are allowed as input data, such as P-values, z-scores, correlations and t-scores; (ii) simultaneously, both local FDR and tail area-based FDR values are estimated for all test statistics and (iii) empirical null models are fit where possible, thereby taking account of potential over- or underdispersion of the theoretical null. In addition, \u2018fdrtool\u2019 provides readily interpretable graphical output, and can be applied to very large scale (in the order of millions of hypotheses) multiple testing problems. Consequently, \u2018fdrtool\u2019 implements a flexible FDR estimation scheme that is unified across different test statistics and variants of FDR.<\/jats:p><jats:p>Availability: The program is freely available from the Comprehensive R Archive Network (http:\/\/cran.r-project.org\/) under the terms of the GNU General Public License (version 3 or later).<\/jats:p><jats:p>Contact: \u00a0strimmer@uni-leipzig.de<\/jats:p>","DOI":"10.1093\/bioinformatics\/btn209","type":"journal-article","created":{"date-parts":[[2008,4,26]],"date-time":"2008-04-26T00:34:59Z","timestamp":1209170099000},"page":"1461-1462","source":"Crossref","is-referenced-by-count":646,"title":["fdrtool: a versatile R package for estimating local and tail area-based false discovery rates"],"prefix":"10.1093","volume":"24","author":[{"given":"Korbinian","family":"Strimmer","sequence":"first","affiliation":[{"name":"Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, H\u00e4rtelstr. 16-18, 04107 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2008,4,25]]},"reference":[{"key":"2023020210102921700_B1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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