{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T22:30:15Z","timestamp":1781735415265,"version":"3.54.5"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"23","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":760,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Next-generation sequencing experiments, such as RNA-Seq, play an increasingly important role in biological research. One complication is that the power and accuracy of such experiments depend substantially on the number of reads sequenced, so it is important and challenging to determine the optimal read depth for an experiment or to verify whether one has adequate depth in an existing experiment.<\/jats:p>\n               <jats:p>Results: By randomly sampling lower depths from a sequencing experiment and determining where the saturation of power and accuracy occurs, one can determine what the most useful depth should be for future experiments, and furthermore, confirm whether an existing experiment had sufficient depth to justify its conclusions. We introduce the subSeq R package, which uses a novel efficient approach to perform this subsampling and to calculate informative metrics at each depth.<\/jats:p>\n               <jats:p>Availability and Implementation: The subSeq R package is available at http:\/\/github.com\/StoreyLab\/subSeq\/.<\/jats:p>\n               <jats:p>Contact: \u00a0dgrtwo@princeton.edu or jstorey@princeton.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btu552","type":"journal-article","created":{"date-parts":[[2014,9,5]],"date-time":"2014-09-05T05:34:33Z","timestamp":1409895273000},"page":"3424-3426","source":"Crossref","is-referenced-by-count":58,"title":["subSeq: Determining Appropriate Sequencing Depth Through Efficient Read Subsampling"],"prefix":"10.1093","volume":"30","author":[{"given":"David G.","family":"Robinson","sequence":"first","affiliation":[{"name":"1 Lewis-Sigler Institute for Integrative Genomics and 2Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John D.","family":"Storey","sequence":"additional","affiliation":[{"name":"1 Lewis-Sigler Institute for Integrative Genomics and 2Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA"},{"name":"1 Lewis-Sigler Institute for Integrative Genomics and 2Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2014,9,3]]},"reference":[{"key":"2023012712035865000_btu552-B2","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.1101\/gr.133744.111","article-title":"Detecting differential usage of exons from RNA-seq data","volume":"22","author":"Anders","year":"2012","journal-title":"Genome Res."},{"key":"2023012712035865000_btu552-B3","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1093\/toxsci\/kft249","article-title":"Comparison of microarrays and RNA-seq for gene expression analyses of dose-response experiments","volume":"137","author":"Black","year":"2014","journal-title":"Toxicol. 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