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Computational methods for analysing multiscale 3D genome organization

Nature Reviews Genetics volume 25, pages 123–141 (2024)Cite this article

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Abstract

Recent progress in whole-genome mapping and imaging technologies has enabled the characterization of the spatial organization and folding of the genome in the nucleus. In parallel, advanced computational methods have been developed to leverage these mapping data to reveal multiscale three-dimensional (3D) genome features and to provide a more complete view of genome structure and its connections to genome functions such as transcription. Here, we discuss how recently developed computational tools, including machine-learning-based methods and integrative structure-modelling frameworks, have led to a systematic, multiscale delineation of the connections among different scales of 3D genome organization, genomic and epigenomic features, functional nuclear components and genome function. However, approaches that more comprehensively integrate a wide variety of genomic and imaging datasets are still needed to uncover the functional role of 3D genome structure in defining cellular phenotypes in health and disease.

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Fig. 1: Overview of multiscale three-dimensional genome features and assays.
Fig. 2: Computational methods for identifying three-dimensional genome features from Hi-C data.
Fig. 3: Machine-learning-based approaches for predicting three-dimensional genome features.
Fig. 4: Incorporation of mechanisms of genome folding in modelling approaches.
Fig. 5: Data-driven genome-modelling methods.
Fig. 6: A typical workflow for processing and analysing single-cell Hi-C data.

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Acknowledgements

This work was supported in part by the National Institutes of Health Common Fund 4D Nucleome Program grant UM1HG011593 (to J.M., F.A. and T.M.), National Institutes of Health grants R01HG007352 (to J.M.) and R01HG012303 (to J.M.). J.M. was additionally supported by a Guggenheim Fellowship from the John Simon Guggenheim Memorial Foundation and T.M. was supported by the Intramural Program of the NIH, the NCI Center for Cancer Research (1 ZIA BC010309–23).

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  1. Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Yang Zhang, Muyu Yang & Jian Ma

  2. Department of Microbiology, Immunology and Molecular Genetics and Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA, USA

    Lorenzo Boninsegna & Frank Alber

  3. Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA

    Tom Misteli

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Glossary

Attention-based method

A machine learning technique used in neural network models to prioritize the most relevant parts of the input when making decisions.

Bystander interactions

An indirect effect of chromatin interactions that occurs through nearby long-range chromatin interactions.

Chromatin domains

Distinct units formed by the chromatin fibre inside the cell nucleus.

Cohesin-mediated loop extrusion

The process by which cohesin complexes extrude DNA into loops until they reach boundaries insulated by architectural chromatin proteins such as CTCF.

Contact frequency

The probability that a pair of genomic loci in the one-dimensional linear genome are spatially closer than a threshold value.

Contact frequency map

A symmetrical square matrix filled with the estimated contact probabilities between any pair of loci.

Convolutional kernel

A compact matrix or vector applied to an input DNA sequence to represent the importance of a specific pattern or feature at each position within the sequence.

Convolutional neural network

A type of artificial neural network that uses convolution layers to learn data representations by applying filters or kernels to the input signal to generate transformed output signals.

Dilated convolutional neural network

A variant of convolutional neural networks in which a ‘dilated’ or expanded convolution kernel is applied to broaden the receptive field of the network without increasing the number of parameters.

Gradient boosting

A machine learning method that iteratively trains a series of models, each of which is designed to correct errors arising from the previous model.

Graph-based posterior regularization

A method to improve the prediction accuracy by incorporating a penalty term that discourages solutions that violate the dependencies between variables, as represented within a graph structure.

Graph neural network

A type of neural network that is designed to handle data represented as graphs.

Homotypic interactions

Binding or association of objects with similar properties, in this context chromatin segments sharing the same type of activity.

Hyperedge

A connection between any number of vertices of a hypergraph.

Hypergraph

A generalization of a graph in which an edge can connect any number of vertices.

Latent Dirichlet allocation

A type of generative statistical model that allows sets of observations to be explained by unobserved groups or topics.

Molecular dynamics

A computational method used to simulate the evolution of a molecular system over time, by numerically integrating Newton’s equations of motion.

Monte Carlo

A computational method used to generate configurations of a physical system by drawing samples from a probability distribution.

Phase separation

Spatial separation of different phases of matter from one homogeneous mixture.

Principal component analysis

A statistical method used to reduce the dimensionality of data by transforming it into a set of linearly uncorrelated variables, known as principal components.

Random forest

A classic machine learning method for classification and regression tasks that works by combining the output of multiple decision trees.

Random walk with restart

A stochastic process on a graph that randomly selects a starting node and then probabilistically determines the next move.

Recurrent neural network

An artificial neural network designed to recognize patterns in sequences of data, with each output dependent on previous computations.

Tensor decomposition

A mathematical technique used to break down a complex tensor (multi-dimensional data) into a series of simpler, more interpretable components.

Variational inference

A probabilistic method in machine learning and statistics that uses optimization techniques to approximate complex, intractable posterior distributions.

Vector representation

A means of representing data, usually in the form of a real-valued vector, such that data points that are closer to each other in the vector space are expected to have similar attributes.

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Zhang, Y., Boninsegna, L., Yang, M. et al. Computational methods for analysing multiscale 3D genome organization. Nat Rev Genet 25, 123–141 (2024). https://doi.org/10.1038/s41576-023-00638-1

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