Measuring significant changes in chromatin conformation with ACCOST.

Borislav H Hristov,William Stafford Noble,Kate B Cook,Jean Philippe Vert,Karine G Le Roch

doi:10.1093/nar/gkaa069

Abstract

Chromatin conformation assays such as Hi-C cannot directly measure differences in 3D architecture between cell types or cell states. For this purpose, two or more Hi-C experiments must be carried out, but direct comparison of the resulting Hi-C matrices is confounded by several features of Hi-C data. Most notably, the genomic distance effect, whereby contacts between pairs of genomic loci that are proximal along the chromosome exhibit many more Hi-C contacts that distal pairs of loci, dominates every Hi-C matrix. Furthermore, the form that this distance effect takes often varies between different Hi-C experiments, even between replicate experiments. Thus, a statistical confidence measure designed to identify differential Hi-C contacts must accurately account for the genomic distance effect or risk being misled by large-scale but artifactual differences. ACCOST (Altered Chromatin COnformation STatistics) accomplishes this goal by extending the statistical model employed by DEseq, re-purposing the ‘size factors,’ which were originally developed to account for differences in read depth between samples, to instead model the genomic distance effect. We show via analysis of simulated and real data that ACCOST provides unbiased statistical confidence estimates that compare favorably with competing methods such as diffHiC, FIND and HiCcompare. ACCOST is freely available with an Apache license at https://bitbucket.org/noblelab/accost.

Highlights

An increasing number of experimental techniques––includi ng ChIA-PET [1], Hi-C [2], Hi-ChIP [3], PLAC-seq [4], SPRITE [5] and GAM [6]––allow for the high-throughput characterization of pairwise chromatin contacts
We introduce ACCOST (Altered Chromatin COnformation STatistics), which assigns statistical confidence estimates to differences in Hi-C contacts, while taking into account differences in the genomic distance effect
Via simulation and analysis of real Hi-C data, that ACCOST delivers unbiased statistical confidence estimates while successfully controlling for systematic changes in the genomic distance effect

Summary

Introduction

An increasing number of experimental techniques––includi ng ChIA-PET [1], Hi-C [2], Hi-ChIP [3], PLAC-seq [4], SPRITE [5] and GAM [6]––allow for the high-throughput characterization of pairwise chromatin contacts. These techniques have helped to elucidate the roles that chromatin 3D architecture play in critical cellular processes such as gene regulation, DNA replication and splicing. A key statistical challenge for 3D chromatin analyses is to assign statistical confidence measures to observed differences in chromatin structure. We focus on calling differential interactions at the finest level, i.e. for a given contact matrix defined with respect to genomic loci (‘bins’) of size w bp, we ask whether the observed contact count associated with two w-bp loci x and y in experimental condition A is significantly different from the corresponding (x, y) contact count from condition B

Methods

Results

Conclusion