Inference of 3D genome architecture by modeling overdispersion of Hi-C data.

ABSTRACT:

Motivation

We address the challenge of inferring a consensus 3D model of genome architecture from Hi-C data. Existing approaches most often rely on a two-step algorithm: first, convert the contact counts into distances, then optimize an objective function akin to multidimensional scaling (MDS) to infer a 3D model. Other approaches use a maximum likelihood approach, modeling the contact counts between two loci as a Poisson random variable whose intensity is a decreasing function of the distance between them. However, a Poisson model of contact counts implies that the variance of the data is equal to the mean, a relationship that is often too restrictive to properly model count data.

Results

We first confirm the presence of overdispersion in several real Hi-C datasets, and we

SUBMITTER: Varoquaux N 

PROVIDER: S-EPMC9857972 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature