Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks

Abstract Background Hi-C data have been widely used to reconstruct chromosomal three-dimensional (3D) structures. One of the key limitations of Hi-C is the unclear relationship between spatial distance and the number of Hi-C contacts. Many methods used a fixed parameter when converting the number of...

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Main Authors: Tong Liu, Zheng Wang
Format: Article
Language:English
Published: BMC 2018-12-01
Series:BMC Bioinformatics
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12859-018-2464-z
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spelling doaj-0ad76cfd95ca4bc09d165d5b79bf1b592020-11-25T02:53:49ZengBMCBMC Bioinformatics1471-21052018-12-0119S17395010.1186/s12859-018-2464-zReconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networksTong Liu0Zheng Wang1Department of Computer Science, University of MiamiDepartment of Computer Science, University of MiamiAbstract Background Hi-C data have been widely used to reconstruct chromosomal three-dimensional (3D) structures. One of the key limitations of Hi-C is the unclear relationship between spatial distance and the number of Hi-C contacts. Many methods used a fixed parameter when converting the number of Hi-C contacts to wish distances. However, a single parameter cannot properly explain the relationship between wish distances and genomic distances or the  locations of topologically associating domains (TADs). Results We have addressed one of the key issues of using Hi-C data, that is, the unclear relationship between spatial distances and the number of Hi-C contacts, which is crucial to understand significant biological functions, such as the enhancer-promoter interactions. Specifically, we developed a new method to infer this converting parameter and pairwise Euclidean distances based on the topology of the Hi-C complex network (HiCNet). The inferred distances were modeled by clustering coefficient and multiple other types of constraints. We found that our inferred distances between bead-pairs within the same TAD were apparently smaller than those distances between bead-pairs from different TADs. Our inferred distances had a higher correlation with fluorescence in situ hybridization (FISH) data, fitted the localization patterns of Xist transcripts on DNA, and better matched 156 pairs of protein-enabled long-range chromatin interactions detected by ChIA-PET. Using the inferred distances and another round of optimization, we further reconstructed 40 kb high-resolution 3D chromosomal structures of mouse male ES cells. The high-resolution structures successfully illustrate TADs and DNA loops (peaks in Hi-C contact heatmaps) that usually indicate enhancer-promoter interactions. Conclusions We developed a novel method to infer the wish distances between DNA bead-pairs from Hi-C contacts. High-resolution 3D structures of chromosomes were built based on the newly-inferred wish distances. This whole process has been implemented as a tool named HiCNet, which is publicly available at http://dna.cs.miami.edu/HiCNet/.http://link.springer.com/article/10.1186/s12859-018-2464-zChromosomal three-dimensional structureHi-C complex networkWish distanceConverting parameterSmall-world networkTopologically associating domain
collection DOAJ
language English
format Article
sources DOAJ
author Tong Liu
Zheng Wang
spellingShingle Tong Liu
Zheng Wang
Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks
BMC Bioinformatics
Chromosomal three-dimensional structure
Hi-C complex network
Wish distance
Converting parameter
Small-world network
Topologically associating domain
author_facet Tong Liu
Zheng Wang
author_sort Tong Liu
title Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks
title_short Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks
title_full Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks
title_fullStr Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks
title_full_unstemmed Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks
title_sort reconstructing high-resolution chromosome three-dimensional structures by hi-c complex networks
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2018-12-01
description Abstract Background Hi-C data have been widely used to reconstruct chromosomal three-dimensional (3D) structures. One of the key limitations of Hi-C is the unclear relationship between spatial distance and the number of Hi-C contacts. Many methods used a fixed parameter when converting the number of Hi-C contacts to wish distances. However, a single parameter cannot properly explain the relationship between wish distances and genomic distances or the  locations of topologically associating domains (TADs). Results We have addressed one of the key issues of using Hi-C data, that is, the unclear relationship between spatial distances and the number of Hi-C contacts, which is crucial to understand significant biological functions, such as the enhancer-promoter interactions. Specifically, we developed a new method to infer this converting parameter and pairwise Euclidean distances based on the topology of the Hi-C complex network (HiCNet). The inferred distances were modeled by clustering coefficient and multiple other types of constraints. We found that our inferred distances between bead-pairs within the same TAD were apparently smaller than those distances between bead-pairs from different TADs. Our inferred distances had a higher correlation with fluorescence in situ hybridization (FISH) data, fitted the localization patterns of Xist transcripts on DNA, and better matched 156 pairs of protein-enabled long-range chromatin interactions detected by ChIA-PET. Using the inferred distances and another round of optimization, we further reconstructed 40 kb high-resolution 3D chromosomal structures of mouse male ES cells. The high-resolution structures successfully illustrate TADs and DNA loops (peaks in Hi-C contact heatmaps) that usually indicate enhancer-promoter interactions. Conclusions We developed a novel method to infer the wish distances between DNA bead-pairs from Hi-C contacts. High-resolution 3D structures of chromosomes were built based on the newly-inferred wish distances. This whole process has been implemented as a tool named HiCNet, which is publicly available at http://dna.cs.miami.edu/HiCNet/.
topic Chromosomal three-dimensional structure
Hi-C complex network
Wish distance
Converting parameter
Small-world network
Topologically associating domain
url http://link.springer.com/article/10.1186/s12859-018-2464-z
work_keys_str_mv AT tongliu reconstructinghighresolutionchromosomethreedimensionalstructuresbyhiccomplexnetworks
AT zhengwang reconstructinghighresolutionchromosomethreedimensionalstructuresbyhiccomplexnetworks
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