Methods for constructing and evaluating consensus genomic interval sets

Abstract

MotivationThe amount of genomic region data continues to increase. Integrating across diverse genomic region sets requires consensus regions, which enable comparing regions across experiments, but also by necessity lose precision in region definitions. We require methods to assess this loss of precision and build optimal consensus region sets.ResultsWe introduce the concept offlexible intervalsand propose 3 novel methods for building consensus region sets, or universes: a coverage cutoff method, a likelihood method, and a Hidden Markov Model. We then propose 3 novel measures for evaluating how well a proposed universe fits a collection of region sets: a base-level overlap score, a region boundary score, and a likelihood score. We apply our methods and evaluation approaches to several collections of region sets and show how these methods can be used to evaluate fit of universes and build optimal universes. We describe scenarios where the common approach of merging regions to create consensus leads to undesirable outcomes and provide principled alternatives that provide interoperability of interval data while minimizing loss of resolution.Availabilityhttps://github.com/databio/geniml.