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.
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.
AVAILABILITY AND IMPLEMENTATION: The subSeq R package is available at 
http://github.com/StoreyLab/subSeq/.