GeneticsMakie.jl: a versatile and scalable toolkit for visualizing locus-level genetic and genomic data

Abstract Summary With the continued deluge of results from genome-wide association and functional genomic studies, it has become increasingly imperative to quickly combine and visualize different layers of genetic and genomic data within a given locus to facilitate exploratory and integrative data a...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2023-01, Vol.39 (1)
Main Authors: Kim, Minsoo, Vo, Daniel D, Kumagai, Michi E, Jops, Connor T, Gandal, Michael J
Format: Article
Language:English
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Bioinformatics
Data Analysis
Genetic analysis
Genetics
Genome
Genome-Wide Association Study - methods
Genomics
Genomics - methods
Plotting
Scientific visualization
Software
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Summary:Abstract Summary With the continued deluge of results from genome-wide association and functional genomic studies, it has become increasingly imperative to quickly combine and visualize different layers of genetic and genomic data within a given locus to facilitate exploratory and integrative data analyses. While several tools have been developed to visualize locus-level genetic results, the limited speed, scalability and flexibility of current approaches remain a significant bottleneck. Here, we present a Julia package for high-performance genetics and genomics-related data visualization that enables fast, simultaneous plotting of hundreds of association results along with multiple relevant genomic annotations. Leveraging the powerful plotting and layout utilities from Makie.jl facilitates the customization and extensibility of every component of a plot, enabling generation of publication-ready figures. Availability and implementation The GeneticsMakie.jl package is open source and distributed under the MIT license via GitHub (https://github.com/mmkim1210/GeneticsMakie.jl). The GitHub repository contains installation instructions as well as examples and documentation for built-in functions. Supplementary information Supplementary data are available at Bioinformatics online.
ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btac786