ArticleBMC genomics2018
A maximum likelihood algorithm for reconstructing 3D structures of human chromosomes from chromosomal contact data.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed.
- Reconstructing the three-dimensional architecture of extrachromosomal DNA with ec3D.Nature communications · 2025Article
- Multi-dimensional epigenomic dynamics converge on H3K4-mediated regulation of low-COPlant communications · 2025Article
- DiCARN-DNase: enhancing cell-to-cell Hi-C resolution using dilated cascading ResNet with self-attention and DNase-seq chromatin accessibility data.Bioinformatics (Oxford, England) · 2025Article
- ChromInSight: Revealing DNA Double-Strand Breaks Through Chromatin Structural Insights With an Interpretable Graph Neural Network Framework.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Unicorn: enhancing single-cell Hi-C data with blind super-resolution for 3D genome structure reconstruction.Bioinformatics (Oxford, England) · 2025Article
- Reconstructing 3D chromosome structures from single-cell Hi-C data with SO(3)-equivariant graph neural networks.NAR genomics and bioinformatics · 2025Article
- HiCForecast: dynamic network optical flow estimation algorithm for spatiotemporal Hi-C data forecasting.Bioinformatics (Oxford, England) · 2025Article
- SEE: A Method for Predicting the Dynamics of Chromatin Conformation Based on Single-Cell Gene Expression.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A constitutive heterochromatic region shapes genome organization and impacts gene expression in Neurospora crassa.BMC genomics · 2024Article
- Advancements and future directions in single-cell Hi-C based 3D chromatin modeling.Computational and structural biotechnology journal · 2024Review
- 3D chromatin maps of a brown alga reveal U/V sex chromosome spatial organization.Nature communications · 2024Article
- A Constitutive Heterochromatic Region Shapes Genome Organization and Impacts Gene Expression inbioRxiv : the preprint server for biology · 2024Article
- Chromosome structure modeling tools and their evaluation in bacteria.Briefings in bioinformatics · 2024Review
- A Comprehensive Evaluation of Generalizability of Deep Learning-Based Hi-C Resolution Improvement Methods.Genes · 2023Article
- Techniques for and challenges in reconstructing 3D genome structures from 2D chromosome conformation capture data.Current opinion in cell biology · 2023Review
- Single-cell Hi-C data enhancement with deep residual and generative adversarial networks.Bioinformatics (Oxford, England) · 2023Article
- ParticleChromo3D+: A Web Server for ParticleChromo3D Algorithm for 3D Chromosome Structure Reconstruction.Current issues in molecular biology · 2023Article
- Article
- A maximum-entropy model to predict 3D structural ensembles of chromatin from pairwise distances with applications to interphase chromosomes and structural variants.Nature communications · 2023Article
- Complementing Hi-C information for 3D chromatin reconstruction by ChromStruct.Frontiers in bioinformatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundThe development of chromosomal conformation capture techniques, particularly, the Hi-C technique, has made the analysis and study of the spatial conformation of a genome an important topic in bioinformatics and computational biology. Aided by high-throughput next generation sequencing techniques, the Hi-C technique can generate genome-wide, large-scale intra- and inter-chromosomal interaction data capable of describing in details the spatial interactions within a genome. These data can be used to reconstruct 3D structures of chromosomes that can be used to study DNA replication, gene regulation, genome interaction, genome folding, and genome function.
resultsHere, we introduce a maximum likelihood algorithm called 3DMax to construct the 3D structure of a chromosome from Hi-C data. 3DMax employs a maximum likelihood approach to infer the 3D structures of a chromosome, while automatically re-estimating the conversion factor (α) for converting Interaction Frequency (IF) to distance. Our results show that the models generated by 3DMax from a simulated Hi-C dataset match the true models better than most of the existing methods. 3DMax is more robust to structural variability and noise. Compared on a real Hi-C dataset, 3DMax constructs chromosomal models that fit the data better than most methods, and it is faster than all other methods. The models reconstructed by 3DMax were consistent with fluorescent in situ hybridization (FISH) experiments and existing knowledge about the organization of human chromosomes, such as chromosome compartmentalization.
conclusions3DMax is an effective approach to reconstructing 3D chromosomal models. The results, and the models generated for the simulated and real Hi-C datasets are available here: http://sysbio.rnet.missouri.edu/bdm_download/3DMax/ . The source code is available here: https://github.com/BDM-Lab/3DMax . A short video demonstrating how to use 3DMax can be found here: https://youtu.be/ehQUFWoHwfo .
Indexed as
Identifiers
What OpenQuestion holds
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.