ArticleFrontiers in genetics2019
HiCeekR: A Novel Shiny App for Hi-C Data Analysis.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Mapping the 3D genome architecture.Computational and structural biotechnology journal · 2025Review
- ppHiC: Interactive exploration of Hi-C results on the ProteinPaint web portal.Computational and structural biotechnology journal · 2024Article
- Visualization and data exploration of chromosome conformation capture data using Voronoi diagrams with v3c-viz.Scientific reports · 2023Article
- CLARITY: a Shiny app for interactive visualisation of the bovine physical-genetic map.Frontiers in genetics · 2023Article
- Advantages of using graph databases to explore chromatin conformation capture experiments.BMC bioinformatics · 2021Article
- HCMB: A stable and efficient algorithm for processing the normalization of highly sparse Hi-C contact data.Computational and structural biotechnology journal · 2021Article
- Easyreporting simplifies the implementation of Reproducible Research layers in R software.PloS one · 2021Article
- FAN-C: a feature-rich framework for the analysis and visualisation of chromosome conformation capture data.Genome biology · 2020Article
- Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization.Nucleic acids research · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The High-throughput Chromosome Conformation Capture (Hi-C) technique combines the power of the Next Generation Sequencing technologies with chromosome conformation capture approach to study the 3D chromatin organization at the genome-wide scale. Although such a technique is quite recent, many tools are already available for pre-processing and analyzing Hi-C data, allowing to identify chromatin loops, topological associating domains and A/B compartments. However, only a few of them provide an exhaustive analysis pipeline or allow to easily integrate and visualize other omic layers. Moreover, most of the available tools are designed for expert users, who have great confidence with command-line applications. In this paper, we present HiCeekR (https://github.com/lucidif/HiCeekR), a novel R Graphical User Interface (GUI) that allows researchers to easily perform a complete Hi-C data analysis. With the aid of the Shiny libraries, it integrates several R/Bioconductor packages for Hi-C data analysis and visualization, guiding the user during the entire process. Here, we describe its architecture and functionalities, then illustrate its capabilities using a publicly available dataset.
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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.