subSeq: determining appropriate sequencing depth through efficient read subsampling.

TitlesubSeq: determining appropriate sequencing depth through efficient read subsampling.
Publication TypeJournal Article
Year of Publication2014
AuthorsRobinson, DG, Storey, JD
JournalBioinformatics
Volume30
Issue23
Pagination3424-6
Date Published2014 Dec 01
ISSN1367-4811
KeywordsAnimals, High-Throughput Nucleotide Sequencing, Rats, Sequence Analysis, RNA, Software
Abstract

<p><b>MOTIVATION: </b>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.</p><p><b>RESULTS: </b>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.</p><p><b>AVAILABILITY AND IMPLEMENTATION: </b>The subSeq R package is available at http://github.com/StoreyLab/subSeq/.</p>

DOI10.1093/bioinformatics/btu552
Alternate JournalBioinformatics
PubMed ID25189781
PubMed Central IDPMC4296149
Grant ListR01 HG002913 / HG / NHGRI NIH HHS / United States