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Exploratory Gene Ontology Analysis with Interactive Visualization
The Gene Ontology (GO) is a central resource for functional-genomics research. Scientists rely on the functional annotations in the GO for hypothesis generation and couple it with high-throughput biological data to enhance interpretation of results. At the same time, the sheer number of concepts (&g...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6534545/ https://www.ncbi.nlm.nih.gov/pubmed/31127124 http://dx.doi.org/10.1038/s41598-019-42178-x |
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author | Zhu, Junjie Zhao, Qian Katsevich, Eugene Sabatti, Chiara |
author_facet | Zhu, Junjie Zhao, Qian Katsevich, Eugene Sabatti, Chiara |
author_sort | Zhu, Junjie |
collection | PubMed |
description | The Gene Ontology (GO) is a central resource for functional-genomics research. Scientists rely on the functional annotations in the GO for hypothesis generation and couple it with high-throughput biological data to enhance interpretation of results. At the same time, the sheer number of concepts (>30,000) and relationships (>70,000) presents a challenge: it can be difficult to draw a comprehensive picture of how certain concepts of interest might relate with the rest of the ontology structure. Here we present new visualization strategies to facilitate the exploration and use of the information in the GO. We rely on novel graphical display and software architecture that allow significant interaction. To illustrate the potential of our strategies, we provide examples from high-throughput genomic analyses, including chromatin immunoprecipitation experiments and genome-wide association studies. The scientist can also use our visualizations to identify gene sets that likely experience coordinated changes in their expression and use them to simulate biologically-grounded single cell RNA sequencing data, or conduct power studies for differential gene expression studies using our built-in pipeline. Our software and documentation are available at http://aegis.stanford.edu. |
format | Online Article Text |
id | pubmed-6534545 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65345452019-06-03 Exploratory Gene Ontology Analysis with Interactive Visualization Zhu, Junjie Zhao, Qian Katsevich, Eugene Sabatti, Chiara Sci Rep Article The Gene Ontology (GO) is a central resource for functional-genomics research. Scientists rely on the functional annotations in the GO for hypothesis generation and couple it with high-throughput biological data to enhance interpretation of results. At the same time, the sheer number of concepts (>30,000) and relationships (>70,000) presents a challenge: it can be difficult to draw a comprehensive picture of how certain concepts of interest might relate with the rest of the ontology structure. Here we present new visualization strategies to facilitate the exploration and use of the information in the GO. We rely on novel graphical display and software architecture that allow significant interaction. To illustrate the potential of our strategies, we provide examples from high-throughput genomic analyses, including chromatin immunoprecipitation experiments and genome-wide association studies. The scientist can also use our visualizations to identify gene sets that likely experience coordinated changes in their expression and use them to simulate biologically-grounded single cell RNA sequencing data, or conduct power studies for differential gene expression studies using our built-in pipeline. Our software and documentation are available at http://aegis.stanford.edu. Nature Publishing Group UK 2019-05-24 /pmc/articles/PMC6534545/ /pubmed/31127124 http://dx.doi.org/10.1038/s41598-019-42178-x Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Zhu, Junjie Zhao, Qian Katsevich, Eugene Sabatti, Chiara Exploratory Gene Ontology Analysis with Interactive Visualization |
title | Exploratory Gene Ontology Analysis with Interactive Visualization |
title_full | Exploratory Gene Ontology Analysis with Interactive Visualization |
title_fullStr | Exploratory Gene Ontology Analysis with Interactive Visualization |
title_full_unstemmed | Exploratory Gene Ontology Analysis with Interactive Visualization |
title_short | Exploratory Gene Ontology Analysis with Interactive Visualization |
title_sort | exploratory gene ontology analysis with interactive visualization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6534545/ https://www.ncbi.nlm.nih.gov/pubmed/31127124 http://dx.doi.org/10.1038/s41598-019-42178-x |
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