ArticleBioinformatics (Oxford, England)2016
A hidden Markov random field-based Bayesian method for the detection of long-range chromosomal interactions in Hi-C data.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- HiCPotts: an R/bioconductor package to identify significant interactions in chromosome conformation capture data and model sources of bias.Bioinformatics (Oxford, England) · 2026Article
- Comparing chromatin contact maps at scale: methods and insights.Nature methods · 2025Article
- Comparative study on chromatin loop callers using Hi-C data reveals their effectiveness.BMC bioinformatics · 2024Article
- A Bioconductor/R Workflow for the Detection and Visualization of Differential Chromatin Loops.F1000Research · 2024Article
- The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data.Briefings in bioinformatics · 2023Review
- Comparing chromatin contact maps at scale: methods and insights.Research square · 2023Article
- Comparing chromatin contact maps at scale: methods and insights.bioRxiv : the preprint server for biology · 2023Article
- Posterior inference of Hi-C contact frequency through sampling.Frontiers in bioinformatics · 2023Article
- ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data.Bioinformatics (Oxford, England) · 2022Article
- THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data.PLoS genetics · 2022Article
- Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants.Frontiers in cell and developmental biology · 2022Review
- Probabilistic edge inference of gene networks with markov random field-based bayesian learning.Frontiers in genetics · 2022Article
- Seeing the forest through the trees: prioritising potentially functional interactions from Hi-C.Epigenetics & chromatin · 2021Review
- MUNIn: A statistical framework for identifying long-range chromatin interactions from multiple samples.HGG advances · 2021Article
- HiC-ACT: improved detection of chromatin interactions from Hi-C data via aggregated Cauchy test.American journal of human genetics · 2021Article
- FIREcaller: Detecting frequently interacting regions from Hi-C data.Computational and structural biotechnology journal · 2021Article
- CHROMATIX: computing the functional landscape of many-body chromatin interactions in transcriptionally active loci from deconvolved single cells.Genome biology · 2020Article
- HIFI: estimating DNA-DNA interaction frequency from Hi-C data at restriction-fragment resolution.Genome biology · 2020Article
- Multi-SNP mediation intersection-union test.Bioinformatics (Oxford, England) · 2019Article
- Identification of significant chromatin contacts from HiChIP data by FitHiChIP.Nature communications · 2019Article
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9 authors.
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Abstract
motivationAdvances in chromosome conformation capture and next-generation sequencing technologies are enabling genome-wide investigation of dynamic chromatin interactions. For example, Hi-C experiments generate genome-wide contact frequencies between pairs of loci by sequencing DNA segments ligated from loci in close spatial proximity. One essential task in such studies is peak calling, that is, detecting non-random interactions between loci from the two-dimensional contact frequency matrix. Successful fulfillment of this task has many important implications including identifying long-range interactions that assist interpreting a sizable fraction of the results from genome-wide association studies. The task - distinguishing biologically meaningful chromatin interactions from massive numbers of random interactions - poses great challenges both statistically and computationally. Model-based methods to address this challenge are still lacking. In particular, no statistical model exists that takes the underlying dependency structure into consideration.
resultsIn this paper, we propose a hidden Markov random field (HMRF) based Bayesian method to rigorously model interaction probabilities in the two-dimensional space based on the contact frequency matrix. By borrowing information from neighboring loci pairs, our method demonstrates superior reproducibility and statistical power in both simulation studies and real data analysis. AVAILABILITY AND IMPLEMENTATION: The Source codes can be downloaded at: http://www.unc.edu/∼yunmli/HMRFBayesHiC CONTACT: ming.hu@nyumc.org or yunli@med.unc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.