{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T21:30:31Z","timestamp":1776979831798,"version":"3.51.4"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T00:00:00Z","timestamp":1565049600000},"content-version":"vor","delay-in-days":365,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100005714","name":"National Science Foundation\/EPSCoR Cooperative Agreement","doi-asserted-by":"publisher","award":["IIA-1355423"],"award-info":[{"award-number":["IIA-1355423"]}],"id":[{"id":"10.13039\/100005714","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State of South Dakota"},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences of the National Institutes of Health","doi-asserted-by":"publisher","award":["5P20GM121341"],"award-info":[{"award-number":["5P20GM121341"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sanford Health\u2013SDSU Collaborative Research Seed Grant Program"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["ACI-1548562"],"award-info":[{"award-number":["ACI-1548562"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"USDA National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["SD00H558-15"],"award-info":[{"award-number":["SD00H558-15"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["61772313"],"award-info":[{"award-number":["61772313"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["61432010"],"award-info":[{"award-number":["61432010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Young Scholars Program of Shandong University","award":["2015WLJH19"],"award-info":[{"award-number":["2015WLJH19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,11,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Differential gene expression (DGE) analysis is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes across two or more conditions and is widely used in many applications of RNA-seq data analysis. Interpretation of the DGE results can be nonintuitive and time consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. Here we reviewed DGE results analysis from a functional point of view for various visualizations. We also provide an R\/Bioconductor package, Visualization of Differential Gene Expression Results using R, which generates information-rich visualizations for the interpretation of DGE results from three widely used tools, Cuffdiff, DESeq2 and edgeR. The implemented functions are also tested on five real-world data sets, consisting of one human, one Malus domestica and three Vitis riparia data sets.<\/jats:p>","DOI":"10.1093\/bib\/bby067","type":"journal-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T23:44:30Z","timestamp":1530834270000},"page":"2044-2054","source":"Crossref","is-referenced-by-count":264,"title":["Interpretation of differential gene expression results of RNA-seq data: review and integration"],"prefix":"10.1093","volume":"20","author":[{"given":"Adam","family":"McDermaid","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA"}]},{"given":"Brandon","family":"Monier","sequence":"first","affiliation":[{"name":"Department of Biology and Microbiology, South Dakota State University, SD, USA"}]},{"given":"Jing","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Internal Medicine, Sanford Research, University of South Dakota Sanford School of Medicine"}]},{"given":"Bingqiang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mathematics, Shandong University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3264-8392","authenticated-orcid":false,"given":"Qin","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Agronomy, Horticulture, and Plant Science, Bioinformatics and Mathematical Biosciences Lab, South Dakota State University"},{"name":"Department of Mathematics and Statistics of SDSU, BioSNTR and Sanford Research, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,8,6]]},"reference":[{"key":"2020030521281592100_ref1","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/nrg.2016.49","article-title":"Coming of age: ten years of next-generation sequencing technologies","volume":"17","author":"Goodwin","year":"2016","journal-title":"Nat Rev 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