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A random effect model for reconstruction of spatial chromatin structure
Author(s) -
Park Jincheol,
Lin Shili
Publication year - 2017
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12544
Subject(s) - consistency (knowledge bases) , computer science , inference , chromatin , enhancer , genome , chromosome conformation capture , computational biology , poisson distribution , data mining , biology , gene , artificial intelligence , genetics , statistics , mathematics , gene expression
Summary A gene may be controlled by distal enhancers and repressors, not merely by regulatory elements in its promoter. Spatial organization of chromosomes is the mechanism that brings genes and their distal regulatory elements into close proximity. Recent molecular techniques, coupled with Next Generation Sequencing (NGS) technology, enable genome‐wide detection of physical contacts between distant genomic loci. In particular, Hi‐C is an NGS‐aided assay for the study of genome‐wide spatial interactions. The availability of such data makes it possible to reconstruct the underlying three‐dimensional (3D) spatial chromatin structure. In this article, we present the Poisson Random effect Architecture Model (PRAM) for such an inference. The main feature of PRAM that separates it from previous methods is that it addresses the issue of over‐dispersion and takes correlations among contact counts into consideration, thereby achieving greater consistency with observed data. PRAM was applied to Hi‐C data to illustrate its performance and to compare the predicted distances with those measured by a Fluorescence In Situ Hybridization (FISH) validation experiment. Further, PRAM was compared to other methods in the literature based on both real and simulated data.