ArticleQuantitative biology (Beijing, China)2013
Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data.
Article in Quantitative biology (Beijing, China), 2013. 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Cross-task interpretability through unified modeling reveals a universal shortcut bias in neoantigen prediction.Cell genomics · 2026Article
- Inferring Single-Cell 3D Chromosomal Structures Based on the Lennard-Jones Potential.International journal of molecular sciences · 2021Article
- Exploring the 2D and 3D structural properties of topologically associating domains.BMC bioinformatics · 2019Article
- Regulatory variants: from detection to predicting impact.Briefings in bioinformatics · 2019Review
- RT States: systematic annotation of the human genome using cell type-specific replication timing programs.Bioinformatics (Oxford, England) · 2019Article
- 3C and 3C-based techniques: the powerful tools for spatial genome organization deciphering.Molecular cytogenetics · 2018Review
- Statistical challenges in analyzing methylation and long-range chromosomal interaction data.Statistics in biosciences · 2016Article
- Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation.PLoS computational biology · 2016Article
- 4D nucleomes in single cells: what can computational modeling reveal about spatial chromatin conformation?Genome biology · 2016Review
- HSA: integrating multi-track Hi-C data for genome-scale reconstruction of 3D chromatin structure.Genome biology · 2016Article
- Inferring 3D chromatin structure using a multiscale approach based on quaternions.BMC bioinformatics · 2015Article
- Probing long-range interactions by extracting free energies from genome-wide chromosome conformation capture data.BMC bioinformatics · 2015Article
- Inferential modeling of 3D chromatin structure.Nucleic acids research · 2015Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Understanding how chromosomes fold provides insights into the transcription regulation, hence, the functional state of the cell. Using the next generation sequencing technology, the recently developed Hi-C approach enables a global view of spatial chromatin organization in the nucleus, which substantially expands our knowledge about genome organization and function. However, due to multiple layers of biases, noises and uncertainties buried in the protocol of Hi-C experiments, analyzing and interpreting Hi-C data poses great challenges, and requires novel statistical methods to be developed. This article provides an overview of recent Hi-C studies and their impacts on biomedical research, describes major challenges in statistical analysis of Hi-C data, and discusses some perspectives for future research.
Identifiers
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Registered trials
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.