Chromosome3D: reconstructing three-dimensional chromosomal structures from Hi-C interaction frequency data using distance geometry simulated annealing

Abstract

BackgroundReconstructing three-dimensional structures of chromosomes is useful for visualizing their shapes in a cell and interpreting their function. In this work, we reconstruct chromosomal structures from Hi-C data by translating contact counts in Hi-C data into Euclidean distances between chromosomal regions and then satisfying these distances using a structure reconstruction method rigorously tested in the field of protein structure determination.ResultsWe first evaluate the robustness of the overall reconstruction algorithm on noisy simulated data at various levels of noise by comparing with some of the state-of-the-art reconstruction methods. Then, using simulated data, we validate that Spearman’s rank correlation coefficient between pairwise distances in the reconstructed chromosomal structures and the experimental chromosomal contact counts can be used to find optimum conversion rules for transforming interaction frequencies to wish distances. This strategy is then applied to real Hi-C data at chromosome level for optimal transformation of interaction frequencies to wish distances and for ranking and selecting structures. The chromosomal structures reconstructed from a real-world human Hi-C dataset by our method were validated by the known two-compartment feature of the human chromosome organization. We also show that our method is robust with respect to the change of the granularity of Hi-C data, and consistently produces similar structures at different chromosomal resolutions.ConclusionChromosome3D is a robust method of reconstructing chromosome three-dimensional models using distance restraints obtained from Hi-C interaction frequency data. It is available as a web application and as an open source tool at http://sysbio.rnet.missouri.edu/chromosome3d/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3210-4) contains supplementary material, which is available to authorized users.

CSynth: A Dynamic Modelling and Visualisation Tool for 3D Chromatin Structure

“…Comparison vs other software Chromosome3D 29 and LorDG 17 have been extensively benchmarked and hence we compare our modelling with the results of these papers. To aid our comparison we have implemented the Lorentzian function reported in LorDG and compared using the synthetic data sets described at https://missouri.box.com/v/LorDG .…”

Bayesian Estimation of 3D Chromosomal Structure from Single Cell Hi-C Data

“…These studies generate contact maps describing the probability of observing interactions between any two regions of the genome, which can be associated with distance matrices between pairs of genomic loci. Methods developed to infer the 3D structure of chromosomes from these contacts maps typically rely either on optimization-based strategies to minimize the difference between the inferred structure and the distance matrix [21,8,28,32,17], or on probabilistic modeling to find the most likely structure(s) given the observed contact probabilities [2,31,9,25,1,20,27].…”

Hierarchical Reconstruction of High-Resolution 3D Models of Human Chromosomes

“…Contact matrices can also be used to reconstruct 3D models of chromosomes and genomes to further facilitate the study of their organization. Various methods have been proposed to reconstruct 3D models of chromosomes or genomes 10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 . On one hand, some of these methods utilize a function that approximates the inverse relationship between interaction frequencies (IFs) and spatial distances between fragments and then uses the distances as restraints to build 3D models via spatial optimization.…”

“…On one hand, some of these methods utilize a function that approximates the inverse relationship between interaction frequencies (IFs) and spatial distances between fragments and then uses the distances as restraints to build 3D models via spatial optimization. These methods are called the optimization based method 10,14,17,24,25,26,29 . In the early work of Duan et al 10 , 3D models of a yeast genome were reconstructed to fit the Euclidian distances converted from IFs.…”