title: subSeq: determining appropriate sequencing depth through efficient read subsampling.
abstract: BACKGROUND
  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.
  
  
  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.
  
  
  METHODS
  The subSeq R package is available at http://github.com/StoreyLab/subSeq/.
authors.affiliation: Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.
authors.forename: David G
authors.initials: DG
authors.lastname: Robinson
authors.affiliation: Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.
authors.forename: John D
authors.initials: JD
authors.lastname: Storey
mesh_terms: D000818:Animals; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D051381:Rats; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software