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|Abstract:||Next-generation sequencing experiments, such as RNASeq, 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. 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.|
|Electronic Publication Date:||3-Sep-2014|
|Citation:||Robinson, David G, Storey, John D. (2014). subSeq: Determining Appropriate Sequencing Depth Through Efficient Read Subsampling. Bioinformatics, 30 (23), 3424 - 3426. doi:10.1093/bioinformatics/btu552|
|Pages:||3424 - 3426|
|Type of Material:||Journal Article|
|Version:||Final published version. This is an open access article.|
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