Work

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

Year: 2022

Type: article

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.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btac786

Authors: Minsoo Kim, Daniel D Vo, Michi E Kumagai, Connor T Jops, Michael J Gandal

Institutions: University of California, Los Angeles, Children's Hospital of Philadelphia, University of Pennsylvania

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $3,618

Funders: National Institute of Mental Health, National Institute of General Medical Sciences