{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:34:13Z","timestamp":1786066453743,"version":"3.56.0"},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"7","funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P30ES017885-01 and R01CA158286-01"],"award-info":[{"award-number":["P30ES017885-01 and R01CA158286-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Summary: Tests for differential gene expression with RNA-seq data have a tendency to identify certain types of transcripts as significant, e.g. longer and highly-expressed transcripts. This tendency has been shown to bias gene set enrichment (GSE) testing, which is used to find over- or under-represented biological functions in the data. Yet, there remains a surprising lack of tools for GSE testing specific for RNA-seq. We present a new GSE method for RNA-seq data, RNA-Enrich, that accounts for the above tendency empirically by adjusting for average read count per gene. RNA-Enrich is a quick, flexible method and web-based tool, with 16 available gene annotation databases. It does not require a P-value cut-off to define differential expression, and works well even with small sample-sized experiments. We show that adjusting for read counts per gene improves both the type I error rate and detection power of the test.<\/jats:p>\n               <jats:p>Availability and implementation: RNA-Enrich is available at http:\/\/lrpath.ncibi.org or from supplemental material as R code.<\/jats:p>\n               <jats:p>Contact: \u00a0sartorma@umich.edu<\/jats:p>\n               <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv694","type":"journal-article","created":{"date-parts":[[2015,11,26]],"date-time":"2015-11-26T02:03:25Z","timestamp":1448503405000},"page":"1100-1102","source":"Crossref","is-referenced-by-count":55,"title":["RNA-Enrich: a cut-off free functional enrichment testing method for RNA-seq with improved detection power"],"prefix":"10.1093","volume":"32","author":[{"given":"Chee","family":"Lee","sequence":"first","affiliation":[{"name":"1 Department of Computational Medicine and Bioinformatics and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Snehal","family":"Patil","sequence":"additional","affiliation":[{"name":"1 Department of Computational Medicine and Bioinformatics and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maureen A.","family":"Sartor","sequence":"additional","affiliation":[{"name":"1 Department of Computational Medicine and Bioinformatics and"},{"name":"2 Biostatistics Department, University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2015,11,25]]},"reference":[{"key":"2023020111595184700_btv694-B1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/nprot.2008.211","article-title":"Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources","volume":"4","author":"Huang da","year":"2009","journal-title":"Nat. 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Rep."},{"key":"2023020111595184700_btv694-B8","doi-asserted-by":"crossref","first-page":"R14","DOI":"10.1186\/gb-2010-11-2-r14","article-title":"Gene ontology analysis for RNA-seq: accounting for selection bias","volume":"11","author":"Young","year":"2010","journal-title":"Genome Biol."}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/7\/1100\/49018393\/bioinformatics_32_7_1100.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/7\/1100\/49018393\/bioinformatics_32_7_1100.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T22:24:26Z","timestamp":1675290266000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/32\/7\/1100\/1743923"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,25]]},"references-count":8,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2016,4,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btv694","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2016,4,1]]},"published":{"date-parts":[[2015,11,25]]}}}