HSA: integrating multi-track Hi-C data for genome-scale reconstruction of 3D chromatin structure

Zou, Chenchen; Zhang, Yuping; Ouyang, Zhengqing

doi:10.1186/s13059-016-0896-1

Cited by 72 publications

(106 citation statements)

References 36 publications

(70 reference statements)

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“…In addition, we also reconstructed the regular helix structure (having 100 points) used as benchmark dataset by Zou et al [16] and compared our models with the models reconstructed by two other state-of-the-art methods, HSA [16] and ShRec3D [12]. Using Spearman’s rank correlation coefficient (SRCC) and Pearson’s correlation coefficient (PCC), we find that our method’s performance is similar to HSA and ShRec3D at 90, 70 and 25 % signal coverages [see Additional file 1].…”

Section: Resultsmentioning

confidence: 99%

“…We ignored the HSA [16] method because of its slow speed, considering the fact that our chromosome structures have up to 479 points. The average Spearman’s rank correlation coefficients between reconstructed models and input IF at 1 MB/500 KB reconstructed by Chromosome3D, PM2 and ShRec3D are −0.87/−0.85, −0.79/−0.78 and −0.65/−0.61 respectively.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, we use Spearman’s rank correlation [11] to compare reconstructed structures with original input interaction frequencies in order to investigate which conversion rule parameters for converting contacts into distances yields better reconstructed models. We validated our method with simulated datasets of a yeast chromosome [7] and a regular helix structure [16], and further tested it on a real Hi-C dataset [17]. …”

Section: Introductionmentioning

confidence: 99%

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

2016

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2016

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“…Consensus methods generate a single structure from ensemble Hi-C data 23,24 by relating contact frequencies with spatial distances, which are then used to generate a single 3D structure by optimizing a scoring function 3,23–26 , a likelihood function through Bayesian inference 27 , or solving a generalized linear model 28 . These methods are conceptually simple and computationally time efficient.…”

Section: Introductionmentioning

confidence: 99%

Producing genome structure populations with the dynamic and automated PGS software

et al. 2018

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Hi-C technologies are widely used to investigate the spatial organization of genomes. Because genome structures can vary considerably between individual cells of a population, interpreting ensemble-averaged Hi-C data can be challenging, in particular for long-range and inter-chromosomal interactions. We pioneered a probabilistic approach for generating a population of distinct diploid 3D genome structures consistent with all the chromatin-chromatin interaction probabilities from Hi-C experiments. Each structure in the population is a physical model of the genome in 3D. Analysis of these models yields new insights into the causes and the functional properties of the genome’s organization in space and time. We provide a user- friendly software package, called PGS, which runs on local machines (for toy runs) and high- performance computing platforms. PGS takes a genome-wide Hi-C contact frequency matrix along with information about genome segmentation and produces an ensemble of 3D genome structures entirely consistent with the input. The software automatically generates an analysis report, and provides tools to extract and analyze the 3D coordinates of specific domains. Basic Linux command line knowledge is sufficient for using this software. A typical running time of the pipeline is about 3 days with 300 cores on a computer cluster to generate a population of 1,000 diploid genome structures at TAD level resolution.

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“…Most of the approaches share the same strategy to convert the matrix of IFs into a matrix of preferred pairwise distances, and then use these distances in an optimization algorithm to build a chromosome structure. Variations of this approach are implemented in several programs, including TADbit (Baù et al 2011;Baù and Marti-Renom 2012), MOGEN Cheng 2014, 2016), MCMC5C (Rousseau et al 2011), AutoChrom3D (Peng et al 2013), BACH (Hu et al 2013), PASTIS (Varoquaux et al 2014), InfMod3DGen , and HSA (Zou et al 2016). Other approaches include ChromSDE , that used semidefinite programming, and ShRec3D (Lesne et al 2014), that used a graph theory to normalize the input heat map.…”

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confidence: 99%

An integrated 3-Dimensional Genome Modeling Engine for data-driven simulation of spatial genome organization

et al. 2016

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ChIA-PET is a high-throughput mapping technology that reveals long-range chromatin interactions and provides insights into the basic principles of spatial genome organization and gene regulation mediated by specific protein factors. Recently, we showed that a single ChIA-PET experiment provides information at all genomic scales of interest, from the high-resolution locations of binding sites and enriched chromatin interactions mediated by specific protein factors, to the low resolution of nonenriched interactions that reflect topological neighborhoods of higher-order chromosome folding. This multilevel nature of ChIA-PET data offers an opportunity to use multiscale 3D models to study structural-functional relationships at multiple length scales, but doing so requires a structural modeling platform. Here, we report the development of 3D-GNOME (3-Dimensional Genome Modeling Engine), a complete computational pipeline for 3D simulation using ChIA-PET data. 3D-GNOME consists of three integrated components: a graph-distance-based heat map normalization tool, a 3D modeling platform, and an interactive 3D visualization tool. Using ChIA-PET and Hi-C data derived from human B-lymphocytes, we demonstrate the effectiveness of 3D-GNOME in building 3D genome models at multiple levels, including the entire genome, individual chromosomes, and specific segments at megabase (Mb) and kilobase (kb) resolutions of single average and ensemble structures. Further incorporation of CTCF-motif orientation and high-resolution looping patterns in 3D simulation provided additional reliability of potential biologically plausible topological structures.

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HSA: integrating multi-track Hi-C data for genome-scale reconstruction of 3D chromatin structure

Cited by 72 publications

References 36 publications

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

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

Producing genome structure populations with the dynamic and automated PGS software

An integrated 3-Dimensional Genome Modeling Engine for data-driven simulation of spatial genome organization

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