Methods for constructing and evaluating consensus genomic interval sets.

ABSTRACT: The 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. Here, we introduce the concept of flexible intervals and propose three novel methods for building consensus region sets, or universes: a coverage cutoff method, a likelihood method, and a Hidden Markov Model. We then propose three novel measures for evaluating how well a proposed universe fits a collection of region sets: a base-level overlap score, a region boundary distance score, and a likelihood score. We apply our methods and evaluation approaches to seve

SUBMITTER: Rymuza J 

PROVIDER: S-EPMC11417377 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature