Bayesian Estimation of Three-Dimensional Chromosomal Structure from Single-Cell Hi-C Data

Abstract The problem of three-dimensional (3D) chromosome structure inference from Hi-C data sets is important and challenging. While bulk Hi-C data sets contain contact information derived from millions of cells and can capture major structural features shared by the majority of cells in the sample, they do not provide information about local variability between cells. Single-cell Hi-C can overcome this problem, but contact matrices are generally very sparse, making structural inference more problematic. We have developed a Bayesian multiscale approach, named Structural Inference via Multiscale Bayesian Approach, to infer 3D structures of chromosomes from single-cell Hi-C while including the bulk Hi-C data and some regularization terms as a prior. We study the landscape of solutions for each single-cell Hi-C data set as a function of prior strength and demonstrate clustering of solutions using data from the same cell.

[1]  Kim-Chuan Toh,et al.  3D Chromosome Modeling with Semi-Definite Programming and Hi-C Data , 2013, J. Comput. Biol..

[2]  Jing Liang,et al.  Chromatin architecture reorganization during stem cell differentiation , 2015, Nature.

[3]  Marc A Marti-Renom,et al.  Genome structure determination via 3C-based data integration by the Integrative Modeling Platform. , 2012, Methods.

[4]  Badri Adhikari,et al.  Chromosome3D: reconstructing three-dimensional chromosomal structures from Hi-C interaction frequency data using distance geometry simulated annealing , 2016, BMC Genomics.

[5]  William Stafford Noble,et al.  Massively multiplex single-cell Hi-C , 2016, Nature Methods.

[6]  Shili Lin,et al.  Impact of data resolution on three-dimensional structure inference methods , 2016, BMC Bioinformatics.

[7]  William Stafford Noble,et al.  A statistical approach for inferring the 3D structure of the genome , 2014, Bioinform..

[8]  Chenchen Zou,et al.  HSA: integrating multi-track Hi-C data for genome-scale reconstruction of 3D chromatin structure , 2016, Genome Biology.

[9]  Philip E. Gill,et al.  Practical optimization , 1981 .

[10]  A. Tanay,et al.  Cell-cycle dynamics of chromosomal organisation at single-cell resolution , 2016, Nature.

[11]  Enrique Blanco,et al.  3 D structure of individual mammalian genomes studied by single cell HiC , 2017 .

[12]  Ilya M. Flyamer,et al.  Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition , 2017, Nature.

[13]  Philippe Collas,et al.  Manifold Based Optimization for Single-Cell 3D Genome Reconstruction , 2015, PLoS Comput. Biol..

[14]  Dariusz Plewczynski,et al.  An integrated 3-Dimensional Genome Modeling Engine for data-driven simulation of spatial genome organization , 2016, Genome research.

[15]  Job Dekker,et al.  Organization of the Mitotic Chromosome , 2013, Science.

[16]  Michael Nilges,et al.  Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data , 2016, PLoS Comput. Biol..

[17]  M. L. Le Gros,et al.  Population-based 3D genome structure analysis reveals driving forces in spatial genome organization , 2016, Proceedings of the National Academy of Sciences.

[18]  Daniel Jost,et al.  TADs are 3D structural units of higher-order chromosome organization in Drosophila , 2018, Science Advances.

[19]  Mathieu Blanchette,et al.  Three-dimensional modeling of chromatin structure from interaction frequency data using Markov chain Monte Carlo sampling , 2011, BMC Bioinformatics.

[20]  A. Tanay,et al.  Single cell Hi-C reveals cell-to-cell variability in chromosome structure , 2013, Nature.

[21]  Jianlin Cheng,et al.  3D Genome Structure Modeling by Lorentzian Objective Function , 2017, BCB.

[22]  A. Lesne,et al.  3D genome reconstruction from chromosomal contacts , 2014, Nature Methods.

[23]  I. Amit,et al.  Comprehensive mapping of long range interactions reveals folding principles of the human genome , 2011 .

[24]  Ming Hu,et al.  Bayesian Inference of Spatial Organizations of Chromosomes , 2013, PLoS Comput. Biol..

[25]  Shaun Mahony,et al.  miniMDS : 3 D structural inference from high-resolution HiC data , 2017 .