ArticleBMC bioinformatics2015
Reconstruction of 3D genome architecture via a two-stage algorithm.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Assessing chromatin relocalization in 3D using the patient rule induction method.Biostatistics (Oxford, England) · 2023Article
- Does multi-way, long-range chromatin contact data advance 3D genome reconstruction?BMC bioinformatics · 2023Article
- Can 3D diploid genome reconstruction from unphased Hi-C data be salvaged?NAR genomics and bioinformatics · 2022Article
- Principal curve approaches for inferring 3D chromatin architecture.Biostatistics (Oxford, England) · 2022Article
- Article
- Multiscale modeling of genome organization with maximum entropy optimization.The Journal of chemical physics · 2021Article
- Computational approaches for inferring 3D conformations of chromatin from chromosome conformation capture data.Methods (San Diego, Calif.) · 2020Review
- Computational methods for predicting 3D genomic organization from high-resolution chromosome conformation capture data.Briefings in functional genomics · 2020Article
- Assessing stationary distributions derived from chromatin contact maps.BMC bioinformatics · 2020Article
- GenomeFlow: a comprehensive graphical tool for modeling and analyzing 3D genome structure.Bioinformatics (Oxford, England) · 2019Article
- Large-scale 3D chromatin reconstruction from chromosomal contacts.BMC genomics · 2019Article
- Hierarchical Reconstruction of High-Resolution 3D Models of Large Chromosomes.Scientific reports · 2019Article
- Subnuclear distribution of proteins: Links with genome architecture.Nucleus (Austin, Tex.) · 2018Review
- Network analysis identifies chromosome intermingling regions as regulatory hotspots for transcription.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
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2 authors.
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Abstract
backgroundThe three-dimensional (3D) configuration of chromosomes within the eukaryote nucleus is an important factor for several cellular functions, including gene expression regulation, and has also been linked with cancer-causing translocation events. While visualization of such architecture remains limited to low resolutions, the ability to infer structures at increasing resolutions has been enabled by recently-devised chromosome conformation capture techniques. In particular, when coupled with next generation sequencing, such methods yield an inventory of genome-wide chromatin contacts or interactions. Various algorithms have been advanced to operate on such contact data to produce reconstructed 3D configurations. Studies have shown that these reconstructions can provide added value over raw interaction data with respect to downstream biological insights. However, only limited, low-resolution reconstructions have been realized for mammals due to computational bottlenecks.
resultsHere we propose a two-stage algorithm to partially overcome these computational barriers. The central idea is to initially utilize existing reconstruction techniques on an individual chromosome basis, using intra-chromosomal contacts, and then to relatively position these chromosome-level reconstructions using inter-chromosomal contacts. This two-stage strategy represents a natural approach in view of the within- versus between- chromosome distribution of contacts. It can increase resolution ≈ 20 fold for mouse and human. After describing the algorithm we present 3D architectures for mouse embryonic stem cells and human lymphoblastoid cells. We evaluate the impact of several factors on reconstruction reproducibility and explore a variety of sampling schemes. We further analyze replicate data at differing resolutions obtained from recently devised in situ Hi-C assays. In all instances we demonstrate insensitivity of the whole-genome 3D reconstruction obtained by the two-stage algorithm to the sampling strategy used.
conclusionsOur two-stage algorithm has the potential to significantly increase the resolution of 3D genome reconstructions. The improvements are such that we can progress from 1 Mb resolution to 100 kb resolution, notable since this latter value has been identified as critical to inferring topological domains in analyses performed on the contact (rather than 3D) level.
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