Abstract
Motivation ChIP-seq is used extensively to identify sites of transcription factor binding or regions of epigenetic modifications to the genome. The fundamental bioinformatics problem is to take ChIP-seq read data and data representing some kind of control, and determine genomic regions that are enriched in the ChIP-seq versus the control, also called “peak calling.” While many programs have been designed to solve this task, nearly all fall into the statistical trap of using the data twice—once to determine candidate enriched regions, and a second time to assess enrichment by methods of classical statistical hypothesis testing. This double use of the data has the potential to invalidate the statistical significance assigned to enriched regions, or “peaks”, and as a consequence, to invalidate false discovery rate estimates. Thus, the true significance or reliability of peak calls remains unknown.
Results We show, through extensive simulation studies of null hypothesis data, that three well-known peak callers, MACS, SICER and diffReps, output optimistically biased p-values, and therefore optimistic false discovery rate estimates—in some cases, orders of magnitude optimistic. We also propose a new wrapper algorithm called RECAP, that uses resampling of ChIP-seq and control data to estimate and correct for biases built into peak calling algorithms. RECAP also enables for the first time local false discovery rate analysis, so that the likelihood of individual peaks being true positives or false positives can be estimated based on their re-calibrated p-values. RECAP is a powerful new tool for assessing the true statistical significance of ChIP-seq peak calls.
Availability The RECAP software is available at www.perkinslab.ca.
