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Related Experiment Video

Updated: May 10, 2026

Defining Gene Functions in Tumorigenesis by Ex vivo Ablation of Floxed Alleles in Malignant Peripheral Nerve Sheath Tumor Cells

Defining Gene Functions in Tumorigenesis by Ex vivo Ablation of Floxed Alleles in Malignant Peripheral Nerve Sheath Tumor Cells

Published on: August 25, 2021

SVGenes: a library for rendering genomic features in scalable vector graphic format.

Graham J Etherington¹, Daniel MacLean

¹The Sainsbury Laboratory, Norwich Research Park, Norwich, NR4 7UH, UK.

Bioinformatics (Oxford, England)

|June 11, 2013

Summary

SVGenes is a Ruby library that generates genomic visualizations using Scalable Vector Graphics (SVG). It offers a flexible interface for creating publication-quality figures and interactive web graphics of genomic data.

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Area of Science:

Bioinformatics
Computational Biology
Data Visualization

Background:

Genomic data visualization is crucial for understanding biological information.
Scalable Vector Graphics (SVG) offers advanced features for interactive and high-quality graphics.
Existing methods may require data conversion for visualization.

Purpose of the Study:

To develop a flexible Ruby library for generating genomic visualizations.
To leverage SVG for creating publication-quality and interactive genomic figures.
To provide a tool that does not require pre-conversion of genomic data formats.

Main Methods:

Developed SVGenes, a Ruby-language library utilizing SVG primitives.
Implemented a Page object to manage horizontal Track objects.
Modeled genomic entities (genes, transcripts, histograms) as Glyph objects within tracks.

Main Results:

SVGenes renders genomic features using SVG primitives through a simple Ruby interface.
The library supports flexible styling and positioning of genomic elements within tracks.
It generates graphics for diverse genomic datasets without format conversion.

Conclusions:

SVGenes provides an effective solution for visualizing genomic data.
The library facilitates the creation of interactive and publication-ready genomic figures.
Its flexibility and ease of use make it valuable for bioinformatics and web applications.