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Three-dimensional chromatin structure reconstruction software tools | Hi-C data analysis

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3D-GNOME / 3D GeNOme Modeling Engine
Allows a user without any programing experience to generate 3D structures from 3C data with minimal effort, and simply requires any modern web browser to access and use. 3D-GNOME provides a web-based, interactive 3D viewer to visualize and analyze the resulting 3D structure, and includes options for the user to upload genomic annotation data to overlay on the structure. In addition to the 3D structure, 3D-GNOME provides a variety of other analysis tools, including 1D arc representations and 2D heatmap representations of the data.
A two-step algorithm and assess its accuracy using both in silico data and human genome-wide 3C (Hi-C) data. This algorithm avoids convergence issues, accommodates sparse and noisy contact maps, and is orders of magnitude faster than existing methods. ShRec3D involves no ad hoc constraints or tunable parameters and is free from convergence issues and misleading transient outcomes. Its speed makes it applicable to both 3C or carbon-copy 3C (5C) data sets, which typically involve tens of loci, and high-resolution Hi-C data sets, comprising sparse contacts between hundreds of thousands of points. Its accurate reconstruction of average distances between genomic loci and visualization of a consensus structure enable a meaningful use of cell-population Hi-C data, especially when extended into 3D genome browsers.
An approach for chromatin structure prediction capable of relaxing both kinds of sequencing biases by using this identified parameter. This method is validated by intra and inter cell-line comparisons among various chromatin regions for four human cell-lines (K562, GM12878, IMR90 and H1hESC), which shows that the openness of chromatin region is well correlated with chromatin function. This method has been executed by an automatic pipeline (AutoChrom3D) and thus can be conveniently used.
LorDG / Lorentzian 3D Genome
A restraint-based method that is capable of reconstructing 3D genome structures utilizing both intra-and inter-chromosomal contact data. LorDG method is robust to noise and performed well in comparison with a panel of existing methods on a controlled simulated data set. On a real Hi-C data set of the human genome, this method produced chromosome and genome structures that are consistent with 3D FISH data and known knowledge about the human chromosome and genome, such as, chromosome territories and the cluster of small chromosomes in the nucleus center with the exception of the chromosome 18.
GEM / Genomic organization reconstructor based on conformational Energy and Manifold learning
Reconstructs the three-dimensional spatial organizations of chromosomes from high chromosome contact map (Hi-C) interaction frequency data. GEM was developed as a manifold learning-based framework. It can provide an accurate modeling method to derive physically and physiologically reasonable 3D representations of chromosomes. It also faces several technical challenges, such as parameter selection and computational efficiency.
PASTIS / Poisson-based Algorithm for STable Inference of DNA Structure
An approach to infer a consensus 3D structure of a genome from Hi-C data. The method incorporates a statistical model of the contact counts, assuming that the counts between two loci follow a Poisson distribution whose intensity decreases with the physical distances between the loci. The method can automatically adjust the transfer function relating the spatial distance to the Poisson intensity and infer a genome structure that best explains the observed data.
A 3D chromosome reconstruction method to make it capable of reconstructing 3D models of genomes from both intra- and inter-chromosomal Hi-C contact data. We validated MOGEN on synthetic datasets of a polymer worm-like chain model and a yeast genome at first, and then applied it to generate an ensemble of 3D structural models of the genome of human B-cells from a Hi-C dataset. These genome models not only were validated by some known structural patterns of the human genome, such as chromosome compartmentalization, chromosome territories, co-localization of small chromosomes in the nucleus center with the exception of chromosome 18, enriched center-toward inter-chromosomal interactions between elongated or telomere regions of chromosomes, but also demonstrated the intrinsically dynamic orientations between chromosomes. Therefore, MOGEN is a useful tool for converting chromosomal contact data into 3D genome models to provide a better view into the spatial organization of genomes.
Reveals structural variations of chromatin in a cell population. In the BACH-MIX algorithm, we assume that the genomic region of interest is composed of two adjacent sub-regions, each with a rigid consensus 3D structure, but the spatial arrangement of the two sub-structures can vary in a cell population, which is represented by a rotation matrix with three Euler angles. In addition, we take into consideration the mirror symmetric structure which cannot be explained by the rotation matrix. BACH-MIX models the uncertainty of the relative position between the two sub-structures by a mixture component model. The weight of each component represents the proportion of that component in a cell population. The BACH-MIX algorithm is equivalent to a Poisson regression procedure with nonnegative constraints on all coefficients (proportions) of the mixture components.
ISDHiC / Inferential Structure Determination Hi-C data
A Bayesian probabilistic framework to view biomolecular structure from single-cell Hi-C data. ISDHiC computes diverse ensembles of coarse-grained chromosome conformations that reflect the sparsity of single-cell Hi-C contacts. ISDHiC allows to not only estimate bead positions and model parameters but also to compare alternative descriptions of the chromosome fiber. Along with the chromosome structure, ISDHiC also estimates model parameters such as the distance between two contacting loci.
Reconstructs chromosome three-dimensional models using distance restraints obtained from Hi-C interaction frequency (IF) data. Chromosome3D is robust with respect to the change of the granularity of Hi-C data, and consistently produces similar structures at different chromosomal resolutions. It converts the input IF matrix to wish distance matrix using an inverse relationship function. For each input IF matrix, the tool builds 20 structures using a Distance Geometry Simulated Annealing protocol.
A unique computational approach based on optimization procedures known as adaptation, simulated annealing, and genetic algorithm to construct 3D models of human chromosomes, using chromosomal contact data. Gen3D were evaluated using a percentage-based scoring function. Analysis of the scores of the final 3D models demonstrated their effective construction from our computational approach. Specifically, the models resulting from our approach yielded an average score of 80.41%, with a high of 91%, across models for all chromosomes of a normal human B-cell. The implementation of our approach proved effective in constructing 3D chromosome models and proved consistent with, and more effective than, some other methods thereby achieving our goal of creating a tool to help advance certain research efforts.
Infers detailed whole-genome 3D structures by progressively solving and integrating structures at three resolution levels. miniMDS is a method for inferring structures from Hi-C experiments that is suitable for high-resolution data. It uses genome partitioning and parallelization to achieve greater speed and lower memory requirements compared to alternative methods. As measured by the correlation between input distances and output distances, miniMDS is more accurate than other methods that are able to efficiently analyze large, high-resolution datasets.
SIMBA3D / Structure Inference from Multiscale BAyes in 3 Dimensions
Permits users to estimate 3D chromosome structures from single-cell Hi-C data. SIMBA3D is a Bayesian framework that uses penalties for regularization of the estimated structures and using additional information from the bulk Hi-C data. It generates computationally inferences and compares these across different initializations and different data (cells) using multiscale optimization tools and a Broyden-Fletcher-Goldfarb-Shanno (BFGS) routine.
3D Genome Browser
Allows users to visualize and explore chromatin interaction data, such as Hi-C, ChIA-PET, Capture Hi-C, PLAC-Seq, and more. 3D Genome Browser also allows users to browse other omics data such as ChIP-Seq or RNA-Seq for the same genomic region, and gain a complete view of both regulatory landscape and 3D genome structure for any given gene. Users can also check the expression of any queried gene across hundreds of tissue/cell types measured by the ENCODE consortium. Finally, the virtual 4C page provides multiple methods to link distal cis-regulatory elements with their potential target genes, including virtual 4C, ChIA-PET and cross-cell-type correlation of proximal and distal DHSs.
A probabilistic model linking 5C/Hi-C data to physical distances and describe a Markov chain Monte Carlo (MCMC) approach to generate a representative sample from the posterior distribution over structures from interaction frequency data. Structures produced from parallel MCMC runs on the same dataset demonstrate that MCMC5C mixes quickly and is able to sample from the posterior distribution of structures and find subclasses of structures. Structural properties (base looping, condensation, and local density) were defined and their distribution measured across the ensembles of structures generated. We believe that tools like MCMC5C are essential for the reliable analysis of data from the 3C-derived techniques such as 5C and Hi-C. By integrating complex, high-dimensional and noisy datasets into an easy to interpret ensemble of three-dimensional conformations, MCMC5C allows researchers to reliably interpret the result of their assay and contrast conformations under different conditions.
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