{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T17:00:11Z","timestamp":1732035611382},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"17","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,9,1]]},"abstract":"Abstract<\/jats:title>Motivation: Statistical methods development for differential expression analysis of RNA sequencing (RNA-seq) requires software tools to assess accuracy and error rate control. Since true differential expression status is often unknown in experimental datasets, artificially constructed datasets must be utilized, either by generating costly spike-in experiments or by simulating RNA-seq data.<\/jats:p>Results: Polyester is an R package designed to simulate RNA-seq data, beginning with an experimental design and ending with collections of RNA-seq reads. Its main advantage is the ability to simulate reads indicating isoform-level differential expression across biological replicates for a variety of experimental designs. Data generated by Polyester is a reasonable approximation to real RNA-seq data and standard differential expression workflows can recover differential expression set in the simulation by the user.<\/jats:p>Availability and implementation: Polyester is freely available from Bioconductor (http:\/\/bioconductor.org\/).<\/jats:p>Contact: \u00a0jtleek@gmail.com<\/jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv272","type":"journal-article","created":{"date-parts":[[2015,4,30]],"date-time":"2015-04-30T00:32:23Z","timestamp":1430353943000},"page":"2778-2784","source":"Crossref","is-referenced-by-count":267,"title":["Polyester<\/i>: simulating RNA-seq datasets with differential transcript expression"],"prefix":"10.1093","volume":"31","author":[{"given":"Alyssa C.","family":"Frazee","sequence":"first","affiliation":[{"name":"1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health,"},{"name":"3 Center for Computational Biology and"}]},{"given":"Andrew E.","family":"Jaffe","sequence":"additional","affiliation":[{"name":"1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health,"},{"name":"2 Lieber Institute for Brain Development, Johns Hopkins Medical Campus,"},{"name":"3 Center for Computational Biology and"}]},{"given":"Ben","family":"Langmead","sequence":"additional","affiliation":[{"name":"1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health,"},{"name":"3 Center for Computational Biology and"},{"name":"4 Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA"}]},{"given":"Jeffrey T.","family":"Leek","sequence":"additional","affiliation":[{"name":"1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health,"},{"name":"3 Center for Computational Biology and"}]}],"member":"286","published-online":{"date-parts":[[2015,4,28]]},"reference":[{"key":"2023020202213251800_btv272-B1","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1038\/nbt.2702","article-title":"Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories","volume":"31","author":"AC\u2019t Hoen","year":"2013","journal-title":"Nat. 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