{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:14:34Z","timestamp":1787008474590,"version":"build-2736575974"},"reference-count":74,"publisher":"Oxford University Press (OUP)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Motivation: Coexpression networks are data-derived representations of genes behaving in a similar way across tissues and experimental conditions. They have been used for hypothesis generation and guilt-by-association approaches for inferring functions of previously unknown genes. So far, the main platform for expression data has been DNA microarrays; however, the recent development of RNA-seq allows for higher accuracy and coverage of transcript populations. It is therefore important to assess the potential for biological investigation of coexpression networks derived from this novel technique in a condition-independent dataset.<\/jats:p>\n                  <jats:p>Results: We collected 65 publicly available Illumina RNA-seq high quality Arabidopsis thaliana samples and generated Pearson correlation coexpression networks. These networks were then compared with those derived from analogous microarray data. We show how Variance-Stabilizing Transformed (VST) RNA-seq data samples are the most similar to microarray ones, with respect to inter-sample variation, correlation coefficient distribution and network topological architecture. Microarray networks show a slightly higher score in biology-derived quality assessments such as overlap with the known protein\u2013protein interaction network and edge ontological agreement. Different coexpression network centralities are investigated; in particular, we show how betweenness centrality is generally a positive marker for essential genes in A.thaliana, regardless of the platform originating the data. In the end, we focus on a specific gene network case, showing that although microarray data seem more suited for gene network reverse engineering, RNA-seq offers the great advantage of extending coexpression analyses to the entire transcriptome.<\/jats:p>\n                  <jats:p>Contact: \u00a0fgiorgi@appliedgenomics.org<\/jats:p>\n                  <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt053","type":"journal-article","created":{"date-parts":[[2013,2,1]],"date-time":"2013-02-01T21:00:36Z","timestamp":1359752436000},"page":"717-724","source":"Crossref","is-referenced-by-count":90,"title":["Comparative study of RNA-seq- and Microarray-derived coexpression networks in\n                    <i>Arabidopsis thaliana<\/i>"],"prefix":"10.1093","volume":"29","author":[{"given":"Federico M.","family":"Giorgi","sequence":"first","affiliation":[{"name":"1 Institute of Applied Genomics, Udine 33100, 2Scuola Superiore Sant\u2019Anna, Pisa 56124 and 3Department of Agriculture and Environmental Sciences, University of Udine, Udine 33100, Italy"},{"name":"1 Institute of Applied Genomics, Udine 33100, 2Scuola Superiore Sant\u2019Anna, Pisa 56124 and 3Department of Agriculture and Environmental Sciences, University of Udine, Udine 33100, Italy"},{"name":"1 Institute of Applied Genomics, Udine 33100, 2Scuola Superiore Sant\u2019Anna, Pisa 56124 and 3Department of Agriculture and Environmental Sciences, University of Udine, Udine 33100, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristian","family":"Del Fabbro","sequence":"additional","affiliation":[{"name":"1 Institute of Applied Genomics, Udine 33100, 2Scuola Superiore Sant\u2019Anna, Pisa 56124 and 3Department of Agriculture and Environmental Sciences, University of Udine, Udine 33100, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Licausi","sequence":"additional","affiliation":[{"name":"1 Institute of Applied Genomics, Udine 33100, 2Scuola Superiore Sant\u2019Anna, Pisa 56124 and 3Department of Agriculture and Environmental Sciences, University of Udine, Udine 33100, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2013,2,1]]},"reference":[{"key":"2023012810261686400_btt053-B1","doi-asserted-by":"crossref","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol."},{"key":"2023012810261686400_btt053-B2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.ygeno.2012.08.002","article-title":"RNA-seq transcriptome analysis of male and female zebra finch cell lines","volume":"100","author":"Balakrishnan","year":"2012","journal-title":"Genomics"},{"key":"2023012810261686400_btt053-B3","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1093\/bioinformatics\/16.5.412","article-title":"Assessing the accuracy of prediction algorithms for classification: an overview","volume":"16","author":"Baldi","year":"2000","journal-title":"Bioinformatics"},{"key":"2023012810261686400_btt053-B4","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1038\/nrg1272","article-title":"Network biology: understanding the cell\u2019s functional organization","volume":"5","author":"Barab\u00e1si","year":"2004","journal-title":"Nat. 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