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

Author:

Kim Minsoo123ORCID,Vo Daniel D1,Kumagai Michi E1,Jops Connor T1,Gandal Michael J12345

Affiliation:

1. Department of Psychiatry, Semel Institute

2. Department of Human Genetics

3. Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, UCLA , Los Angeles, CA 90095, USA

4. Lifespan Brain Institute at Penn Med and the Children’s Hospital of Philadelphia , Philadelphia, PA 19104, USA

5. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104, USA

Abstract

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.

Funder

UCLA Medical Scientist Training Program

National Institute of Mental Health

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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