ArticleNucleic acids research2018
Reconstructing spatial organizations of chromosomes through manifold learning.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 61 citations in OpenAlex.
- Reconstructing the three-dimensional architecture of extrachromosomal DNA with ec3D.Nature communications · 2025Article
- Advancements and future directions in single-cell Hi-C based 3D chromatin modeling.Computational and structural biotechnology journal · 2024Review
- Hi-BDiSCO: folding 3D mesoscale genome structures from Hi-C data using brownian dynamics.Nucleic acids research · 2024Article
- Chromosome structure modeling tools and their evaluation in bacteria.Briefings in bioinformatics · 2024Review
- Techniques for and challenges in reconstructing 3D genome structures from 2D chromosome conformation capture data.Current opinion in cell biology · 2023Review
- ParticleChromo3D+: A Web Server for ParticleChromo3D Algorithm for 3D Chromosome Structure Reconstruction.Current issues in molecular biology · 2023Article
- Inference of 3D genome architecture by modeling overdispersion of Hi-C data.Bioinformatics (Oxford, England) · 2023Article
- Complementing Hi-C information for 3D chromatin reconstruction by ChromStruct.Frontiers in bioinformatics · 2023Article
- ParticleChromo3D: a Particle Swarm Optimization algorithm for chromosome 3D structure prediction from Hi-C data.BioData mining · 2022Article
- Integrative genome modeling platform reveals essentiality of rare contact events in 3D genome organizations.Nature methods · 2022Article
- Uncovering the Principles of Genome Folding by 3D Chromatin Modeling.Cold Spring Harbor perspectives in biology · 2022Review
- Minimalistic 3D chromatin models: Sparse interactions in single cells drive the chromatin fold and form many-body units.Current opinion in structural biology · 2021Review
- Understanding 3D genome organization by multidisciplinary methods.Nature reviews. Molecular cell biology · 2021Review
- Multiscale modeling of genome organization with maximum entropy optimization.The Journal of chemical physics · 2021Article
- Inferring Single-Cell 3D Chromosomal Structures Based on the Lennard-Jones Potential.International journal of molecular sciences · 2021Article
- A primer on machine learning techniques for genomic applications.Computational and structural biotechnology journal · 2021Review
- Application of Hi-C and other omics data analysis in human cancer and cell differentiation research.Computational and structural biotechnology journal · 2021Review
- Computational approaches for inferring 3D conformations of chromatin from chromosome conformation capture data.Methods (San Diego, Calif.) · 2020Review
- GSDB: a database of 3D chromosome and genome structures reconstructed from Hi-C data.BMC molecular and cell biology · 2020Article
- Computational methods for predicting 3D genomic organization from high-resolution chromosome conformation capture data.Briefings in functional genomics · 2020Article
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Decoding the spatial organizations of chromosomes has crucial implications for studying eukaryotic gene regulation. Recently, chromosomal conformation capture based technologies, such as Hi-C, have been widely used to uncover the interaction frequencies of genomic loci in a high-throughput and genome-wide manner and provide new insights into the folding of three-dimensional (3D) genome structure. In this paper, we develop a novel manifold learning based framework, called GEM (Genomic organization reconstructor based on conformational Energy and Manifold learning), to reconstruct the three-dimensional organizations of chromosomes by integrating Hi-C data with biophysical feasibility. Unlike previous methods, which explicitly assume specific relationships between Hi-C interaction frequencies and spatial distances, our model directly embeds the neighboring affinities from Hi-C space into 3D Euclidean space. Extensive validations demonstrated that GEM not only greatly outperformed other state-of-art modeling methods but also provided a physically and physiologically valid 3D representations of the organizations of chromosomes. Furthermore, we for the first time apply the modeled chromatin structures to recover long-range genomic interactions missing from original Hi-C data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.