Gosling: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization

The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize...

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Published in:IEEE transactions on visualization and computer graphics Vol. 28; no. 1; pp. 140 - 150
Main Authors: LYi, Sehi, Wang, Qianwen, Lekschas, Fritz, Gehlenborg, Nils
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:1077-2626, 1941-0506, 1941-0506
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Abstract The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize visualizations for new analysis tasks or datasets. To address this challenge, we designed Gosling-a grammar for interactive and scalable genomics data visualization. Gosling balances expressiveness for comprehensive multi-scale genomics data visualizations with accessibility for domain scientists. Our accompanying JavaScript toolkit called Gosling.js provides scalable and interactive rendering. Gosling.js is built on top of an existing platform for web-based genomics data visualization to further simplify the visualization of common genomics data formats. We demonstrate the expressiveness of the grammar through a variety of real-world examples. Furthermore, we show how Gosling supports the design of novel genomics visualizations. An online editor and examples of Gosling.js, its source code, and documentation are available at https://gosling.js.org .
AbstractList The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize visualizations for new analysis tasks or datasets. To address this challenge, we designed Gosling-a grammar for interactive and scalable genomics data visualization. Gosling balances expressiveness for comprehensive multi-scale genomics data visualizations with accessibility for domain scientists. Our accompanying JavaScript toolkit called Gosling.js provides scalable and interactive rendering. Gosling.js is built on top of an existing platform for web-based genomics data visualization to further simplify the visualization of common genomics data formats. We demonstrate the expressiveness of the grammar through a variety of real-world examples. Furthermore, we show how Gosling supports the design of novel genomics visualizations. An online editor and examples of Gosling.js, its source code, and documentation are available at https://gosling.js.org .
The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize visualizations for new analysis tasks or datasets. To address this challenge, we designed Gosling-a grammar for interactive and scalable genomics data visualization. Gosling balances expressiveness for comprehensive multi-scale genomics data visualizations with accessibility for domain scientists. Our accompanying JavaScript toolkit called Gosling.js provides scalable and interactive rendering. Gosling.js is built on top of an existing platform for web-based genomics data visualization to further simplify the visualization of common genomics data formats. We demonstrate the expressiveness of the grammar through a variety of real-world examples. Furthermore, we show how Gosling supports the design of novel genomics visualizations. An online editor and examples of Gosling.js, its source code, and documentation are available at https://gosling.js.org.The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize visualizations for new analysis tasks or datasets. To address this challenge, we designed Gosling-a grammar for interactive and scalable genomics data visualization. Gosling balances expressiveness for comprehensive multi-scale genomics data visualizations with accessibility for domain scientists. Our accompanying JavaScript toolkit called Gosling.js provides scalable and interactive rendering. Gosling.js is built on top of an existing platform for web-based genomics data visualization to further simplify the visualization of common genomics data formats. We demonstrate the expressiveness of the grammar through a variety of real-world examples. Furthermore, we show how Gosling supports the design of novel genomics visualizations. An online editor and examples of Gosling.js, its source code, and documentation are available at https://gosling.js.org.
Author Lekschas, Fritz
LYi, Sehi
Gehlenborg, Nils
Wang, Qianwen
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Snippet The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools....
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SubjectTerms Animals
Bioinformatics
Biological cells
Computer Graphics
Data Visualization
declarative specification
Geese
Genomics
Grammar
Microbalances
Scientific visualization
Software
Source code
Toolkits
Visualization
visualization grammar
Title Gosling: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization
URI https://ieeexplore.ieee.org/document/9557192
https://www.ncbi.nlm.nih.gov/pubmed/34596551
https://www.proquest.com/docview/2615515897
https://www.proquest.com/docview/2578781263
https://pubmed.ncbi.nlm.nih.gov/PMC8826597
Volume 28
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