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Gos: a declarative library for interactive genomics visualization in Python
SUMMARY: Gos is a declarative Python library designed to create interactive multiscale visualizations of genomics and epigenomics data. It provides a consistent and simple interface to the flexible Gosling visualization grammar. Gos hides technical complexities involved with configuring web-based ge...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9891240/ https://www.ncbi.nlm.nih.gov/pubmed/36688709 http://dx.doi.org/10.1093/bioinformatics/btad050 |
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author | Manz, Trevor L’Yi, Sehi Gehlenborg, Nils |
author_facet | Manz, Trevor L’Yi, Sehi Gehlenborg, Nils |
author_sort | Manz, Trevor |
collection | PubMed |
description | SUMMARY: Gos is a declarative Python library designed to create interactive multiscale visualizations of genomics and epigenomics data. It provides a consistent and simple interface to the flexible Gosling visualization grammar. Gos hides technical complexities involved with configuring web-based genome browsers and integrates seamlessly within computational notebooks environments to enable new interactive analysis workflows. AVAILABILITY AND IMPLEMENTATION: Gos is released under the MIT License and available on the Python Package Index (PyPI). The source code is publicly available on GitHub (https://github.com/gosling-lang/gos), and documentation with examples can be found at https://gosling-lang.github.io/gos. |
format | Online Article Text |
id | pubmed-9891240 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98912402023-02-02 Gos: a declarative library for interactive genomics visualization in Python Manz, Trevor L’Yi, Sehi Gehlenborg, Nils Bioinformatics Applications Note SUMMARY: Gos is a declarative Python library designed to create interactive multiscale visualizations of genomics and epigenomics data. It provides a consistent and simple interface to the flexible Gosling visualization grammar. Gos hides technical complexities involved with configuring web-based genome browsers and integrates seamlessly within computational notebooks environments to enable new interactive analysis workflows. AVAILABILITY AND IMPLEMENTATION: Gos is released under the MIT License and available on the Python Package Index (PyPI). The source code is publicly available on GitHub (https://github.com/gosling-lang/gos), and documentation with examples can be found at https://gosling-lang.github.io/gos. Oxford University Press 2023-01-23 /pmc/articles/PMC9891240/ /pubmed/36688709 http://dx.doi.org/10.1093/bioinformatics/btad050 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Note Manz, Trevor L’Yi, Sehi Gehlenborg, Nils Gos: a declarative library for interactive genomics visualization in Python |
title | Gos: a declarative library for interactive genomics visualization in Python |
title_full | Gos: a declarative library for interactive genomics visualization in Python |
title_fullStr | Gos: a declarative library for interactive genomics visualization in Python |
title_full_unstemmed | Gos: a declarative library for interactive genomics visualization in Python |
title_short | Gos: a declarative library for interactive genomics visualization in Python |
title_sort | gos: a declarative library for interactive genomics visualization in python |
topic | Applications Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9891240/ https://www.ncbi.nlm.nih.gov/pubmed/36688709 http://dx.doi.org/10.1093/bioinformatics/btad050 |
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