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Updated: Mar 21, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

chromPlot: visualization of genomic data in chromosomal context.

Karen Y Oróstica¹, Ricardo A Verdugo¹

¹Programa de Genética Humana ICBM, Facultad de Medicina, Universidad de Chile, Independencia 1027, Santiago, Chile.

Bioinformatics (Oxford, England)

|May 7, 2016

Summary

This study introduces chromPlot, a new R software tool for visualizing genomic data along chromosomes. It aids in error detection and hypothesis generation for diverse genomic datasets.

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Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Visualizing genomic data in chromosomal context is crucial for identifying data processing errors and formulating new research hypotheses.
Existing tools may lack the flexibility to display large and diverse genomic datasets simultaneously.

Purpose of the Study:

To introduce chromPlot, a novel R package designed for comprehensive visualization of genomic data along chromosomes.
To provide a flexible tool for integrating visualization with genomic data processing workflows.

Main Methods:

The chromPlot software is implemented in R, supporting the simultaneous display of multiple data tracks.
It visualizes large genomic regions and various genomic elements using histograms, points, lines, or colored segments.
Data input is accepted in data.frame and GRanges formats, with built-in support for human and mouse chromosomes and compatibility with UCSC Genome Browser data for other organisms.

Main Results:

chromPlot enables the display of diverse genomic data, including gene counts, genetic variants, and continuous or categorical variables.
The tool facilitates the visualization of large genomic regions like quantitative trait loci (QTLs) and synteny tracts.
It offers multiple representation options (points, lines, segments, histograms) for user-defined data tracks.

Conclusions:

chromPlot offers a powerful and flexible solution for visualizing complex genomic data in chromosomal context.
The R implementation allows seamless integration with existing genomic analysis pipelines.
This tool can enhance the detection of errors and facilitate the generation of novel hypotheses in genomic research.