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Connor T. Jops

dblp:370/5672 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genomics
genome visualization
0.712023
GeneticsMakie.jl: a versatile and scalable toolkit for visualizing locus-level genetic and genomic data · Bioinform. 2023
Bioinformatics and computational biology › genomics
genomic data analysis
0.212023
GeneticsMakie.jl: a versatile and scalable toolkit for visualizing locus-level genetic and genomic data · Bioinform. 2023
Bioinformatics and computational biology › genomics › computational genomics
integrative genomics
0.212023
GeneticsMakie.jl: a versatile and scalable toolkit for visualizing locus-level genetic and genomic data · Bioinform. 2023

Methods — techniques the papers use, named apart from their topics

layout composition · 0.7interactive plotting · 0.7
YearPublicationVenuePosition
2023 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 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.
Daniel D. Vo, Michi E. Kumagai, Connor T. Jops, Michael J. Gandal
Bioinform.4