ArticleNucleic acids research2014
A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed.
- Centromere organization and epigenetic regulation in Aristolochia fimbriata.Genome biology · 2026Article
- Article
- CNNKSCEC: a deep learning-based framework for chromatin loop prediction with multi-source feature integration.Frontiers in genetics · 2026Article
- Extrachromosomal DNA associates with nuclear condensates and reorganizes chromatin structures to enhance oncogenic transcription.Cancer cell · 2025Article
- The 3D nuclear position and compartmentalization of genes prime their response to mechano-confinement.bioRxiv : the preprint server for biology · 2025Article
- Tri-omic single-cell mapping of the 3D epigenome and transcriptome in whole mouse brains throughout the lifespan.Nature methods · 2025Article
- Integration of chromosome conformation and gene expression networks reveals regulatory mechanisms in triple negative breast cancer.Frontiers in cell and developmental biology · 2025Article
- Modeling properties of chromosome territories using polymer filaments in diverse confinement geometries.Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology · 2024Article
- MMCT-Loop: a mix model-based pipeline for calling targeted 3D chromatin loops.Nucleic acids research · 2024Article
- Article
- Stage-specific dynamic reorganization of genome topology shapes transcriptional neighborhoods in developing human retinal organoids.Cell reports · 2023Article
- SilenceREIN: seeking silencers on anchors of chromatin loops by deep graph neural networks.Briefings in bioinformatics · 2023Article
- Computational methods for identifying enhancer-promoter interactions.Quantitative biology (Beijing, China) · 2023Review
- ChIATAC is an efficient strategy for multi-omics mapping of 3D epigenomes from low-cell inputs.Nature communications · 2023Article
- 3D chromatin architecture and transcription regulation in cancer.Journal of hematology & oncology · 2022Review
- Bacon: a comprehensive computational benchmarking framework for evaluating targeted chromatin conformation capture-specific methodologies.Genome biology · 2022Article
- TAD cliques predict key features of chromatin organization.BMC genomics · 2021Article
- Oncogenic extrachromosomal DNA functions as mobile enhancers to globally amplify chromosomal transcription.Cancer cell · 2021Article
- MACPET: model-based analysis for ChIA-PET.Biostatistics (Oxford, England) · 2020Article
- Chromatin interaction analyses elucidate the roles of PRC2-bound silencers in mouse development.Nature genetics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of three-dimensional (3D) interactions between regulatory elements across the genome is crucial to unravel the complex regulatory machinery that orchestrates proliferation and differentiation of cells. ChIA-PET is a novel method to identify such interactions, where physical contacts between regions bound by a specific protein are quantified using next-generation sequencing. However, determining the significance of the observed interaction frequencies in such datasets is challenging, and few methods have been proposed. Despite the fact that regions that are close in linear genomic distance have a much higher tendency to interact by chance, no methods to date are capable of taking such dependency into account. Here, we propose a statistical model taking into account the genomic distance relationship, as well as the general propensity of anchors to be involved in contacts overall. Using both real and simulated data, we show that the previously proposed statistical test, based on Fisher's exact test, leads to invalid results when data are dependent on genomic distance. We also evaluate our method on previously validated cell-line specific and constitutive 3D interactions, and show that relevant interactions are significant, while avoiding over-estimating the significance of short nearby interactions.
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.