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Rapid Analysis and Exploration of Fluorescence Microscopy Images

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Visualizing genomic information across chromosomes with PhenoGram.

Daniel Wolfe, Scott Dudek, Marylyn D Ritchie¹

¹Center for Systems Genomics, Department of Biochemistry and Molecular Biology, Eberly College of Science, The Huck Institutes of the Life Sciences, The Pennsylvania State University, 512 Wartik Laboratory, University Park, PA 16802, USA. marylyn.ritchie@psu.edu.

|October 18, 2013

Summary

This summary is machine-generated.

PhenoGram is a flexible software tool for visualizing genetic data using chromosomal ideograms. It aids researchers in exploring and sharing complex genomic information for better data understanding.

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Area of Science:

Genomics
Bioinformatics
Data Visualization

Background:

Genetic locus information is abundant, necessitating user-friendly data visualization tools.
Chromosomal ideograms offer a graphic representation of chromosomes, adaptable for various study data.
Existing methods require flexible visualization for diverse genomic data analysis.

Purpose of the Study:

To develop a flexible software tool, PhenoGram, for visualizing genomic data.
To enable user-friendly exploration and dissemination of genetic locus information.
To facilitate multiple data visualization approaches using chromosomal ideograms.

Main Methods:

Developed PhenoGram as a web-based tool and command-line program.
Utilized chromosomal ideograms for data representation.
Integrated annotation capabilities including lines, shapes, gene identifiers, and text.

Main Results:

PhenoGram generates annotated chromosomal ideograms with colored lines/regions and various annotations.
The tool supports detailed visualization of specific chromosomal regions.
Examples include visualizing SNP coverage, imputed SNP coverage, and copy-number variation regions.

Conclusions:

PhenoGram is a versatile and user-friendly software for exploring and sharing genomic information.
Data visualization through PhenoGram enhances the understanding of complex genomic results.
The tool supports diverse applications, including genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS).