paper-with-me

홈 › Papers

gghic: A Versatile R Package for Exploring and Visualizing 3D Genome Organization

2024-12-04 · Minghao Jiang, Duohui Jing, Jason W. H. Wong

Motivation: The three-dimensional (3D) organization of the genome plays a critical role in regulating gene expression and maintaining cellular homeostasis. Disruptions in this spatial organization can result in abnormal chromatin interactions, contributing to the development of various diseases including cancer. Advances in chromosome conformation capture technologies, such as Hi-C, have enabled researchers to study genome architecture at high resolution. However, the efficient visualization and interpretation of these complex datasets remain a major challenge, particularly when integrating genomic annotations and inter-chromosomal interactions. Results: We present gghic, an R package that extends the ggplot2 framework to enable intuitive and customizable visualization of genomic interaction data. gghic introduces novel layers for generating triangular heatmaps of chromatin interactions and annotating them with features such as chromatin loops, topologically associated domains (TADs), gene/transcript models, and data tracks (e.g., ChIP-seq signals). The package supports data from multiple chromosomes, facilitating the exploration of inter-chromosomal interactions. Built to integrate seamlessly with the R/Bioconductor ecosystem, gghic is compatible with widely used genomic data formats, including HiCExperiment and GInteractions objects. We demonstrate the utility of gghic by replicating a published figure showing a translocation event in T-cell acute lymphoblastic leukemia (T-ALL), highlighting its ability to integrate genomic annotations and generate publication-quality figures. Availability and implementation: The R package can be accessed at https://github.com/jasonwong-lab/gghic and is distributed under the GNU General Public License version 3.0.

📄 PDF Abstract BibTeX arXiv:2412.03005

Code (1)

jasonwong-lab/gghic 공식 구현

Similar Papers 제목 키워드 기반

gggenomes: effective and versatile visualizations for comparative genomics

2024-11-05 · Thomas Hackl, Markus Ankenbrand, Bart van Adrichem, David Wilkins 외

The effective visualization of genomic data is crucial for exploring and interpreting complex relationships within and across genes and genomes. Despite advances in developing dedicated bioinformatics software, common vi…

RobPy: a Python Package for Robust Statistical Methods

2024-11-04 · Sarah Leyder, Jakob Raymaekers, Peter J. Rousseeuw, Thomas Servotte 외

Robust estimation provides essential tools for analyzing data that contain outliers, ensuring that statistical models remain reliable even in the presence of some anomalous data. While robust methods have long been avail…

Diagnostic

KGP: An R Package with Metadata from the 1000 Genomes Project

2022-10-02 · Stephen D. Turner

The 1000 Genomes Project provides sequencing data on 3,202 samples from 26 populations spanning five continental regions with no access or use restrictions. The kgp R package provides consistent and comprehensive metadat…

PhyloProfile v2 -- Exploring multi-layered phylogenetic profiles at scale

2025-04-28 · Vinh Tran, Ingo Ebersberger

Phylogenetic profiles visualize the presence-absence pattern of genes across taxa and are essential for delineating the evolutionary fate of genes and gene families. Integrating phylogenetic profiles across many genes an…

Dimensionality Reduction

WinPCA: A package for windowed principal component analysis

2025-01-21 · L. Moritz Blumer, Jeffrey M. Good, Richard Durbin

Principal component analysis (PCA) is routinely used in population genetics to assess genetic structure. With chromosomal reference genomes and population-scale whole genome-sequencing becoming increasingly accessible, c…