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gsGator: an integrated web platform for cross-species gene set analysis
BACKGROUND: Gene set analysis (GSA) is useful in deducing biological significance of gene lists using a priori defined gene sets such as gene ontology (GO) or pathways. Phenotypic annotation is sparse for human genes, but is far more abundant for other model organisms such as mouse, fly, and worm. O...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3898093/ https://www.ncbi.nlm.nih.gov/pubmed/24423189 http://dx.doi.org/10.1186/1471-2105-15-13 |
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author | Kang, Hyunjung Choi, Ikjung Cho, Sooyoung Ryu, Daeun Lee, Sanghyuk Kim, Wankyu |
author_facet | Kang, Hyunjung Choi, Ikjung Cho, Sooyoung Ryu, Daeun Lee, Sanghyuk Kim, Wankyu |
author_sort | Kang, Hyunjung |
collection | PubMed |
description | BACKGROUND: Gene set analysis (GSA) is useful in deducing biological significance of gene lists using a priori defined gene sets such as gene ontology (GO) or pathways. Phenotypic annotation is sparse for human genes, but is far more abundant for other model organisms such as mouse, fly, and worm. Often, GSA needs to be done highly interactively by combining or modifying gene lists or inspecting gene-gene interactions in a molecular network. DESCRIPTION: We developed gsGator, a web-based platform for functional interpretation of gene sets with useful features such as cross-species GSA, simultaneous analysis of multiple gene sets, and a fully integrated network viewer for visualizing both GSA results and molecular networks. An extensive set of gene annotation information is amassed including GO & pathways, genomic annotations, protein-protein interaction, transcription factor-target (TF-target), miRNA targeting, and phenotype information for various model organisms. By combining the functionalities of Set Creator, Set Operator and Network Navigator, user can perform highly flexible and interactive GSA by creating a new gene list by any combination of existing gene sets (intersection, union and difference) or expanding genes interactively along the molecular networks such as protein-protein interaction and TF-target. We also demonstrate the utility of our interactive and cross-species GSA implemented in gsGator by several usage examples for interpreting genome-wide association study (GWAS) results. gsGator is freely available at http://gsGator.ewha.ac.kr. CONCLUSIONS: Interactive and cross-species GSA in gsGator greatly extends the scope and utility of GSA, leading to novel insights via conserved functional gene modules across different species. |
format | Online Article Text |
id | pubmed-3898093 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-38980932014-01-23 gsGator: an integrated web platform for cross-species gene set analysis Kang, Hyunjung Choi, Ikjung Cho, Sooyoung Ryu, Daeun Lee, Sanghyuk Kim, Wankyu BMC Bioinformatics Database BACKGROUND: Gene set analysis (GSA) is useful in deducing biological significance of gene lists using a priori defined gene sets such as gene ontology (GO) or pathways. Phenotypic annotation is sparse for human genes, but is far more abundant for other model organisms such as mouse, fly, and worm. Often, GSA needs to be done highly interactively by combining or modifying gene lists or inspecting gene-gene interactions in a molecular network. DESCRIPTION: We developed gsGator, a web-based platform for functional interpretation of gene sets with useful features such as cross-species GSA, simultaneous analysis of multiple gene sets, and a fully integrated network viewer for visualizing both GSA results and molecular networks. An extensive set of gene annotation information is amassed including GO & pathways, genomic annotations, protein-protein interaction, transcription factor-target (TF-target), miRNA targeting, and phenotype information for various model organisms. By combining the functionalities of Set Creator, Set Operator and Network Navigator, user can perform highly flexible and interactive GSA by creating a new gene list by any combination of existing gene sets (intersection, union and difference) or expanding genes interactively along the molecular networks such as protein-protein interaction and TF-target. We also demonstrate the utility of our interactive and cross-species GSA implemented in gsGator by several usage examples for interpreting genome-wide association study (GWAS) results. gsGator is freely available at http://gsGator.ewha.ac.kr. CONCLUSIONS: Interactive and cross-species GSA in gsGator greatly extends the scope and utility of GSA, leading to novel insights via conserved functional gene modules across different species. BioMed Central 2014-01-14 /pmc/articles/PMC3898093/ /pubmed/24423189 http://dx.doi.org/10.1186/1471-2105-15-13 Text en Copyright © 2014 Kang et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Database Kang, Hyunjung Choi, Ikjung Cho, Sooyoung Ryu, Daeun Lee, Sanghyuk Kim, Wankyu gsGator: an integrated web platform for cross-species gene set analysis |
title | gsGator: an integrated web platform for cross-species gene set analysis |
title_full | gsGator: an integrated web platform for cross-species gene set analysis |
title_fullStr | gsGator: an integrated web platform for cross-species gene set analysis |
title_full_unstemmed | gsGator: an integrated web platform for cross-species gene set analysis |
title_short | gsGator: an integrated web platform for cross-species gene set analysis |
title_sort | gsgator: an integrated web platform for cross-species gene set analysis |
topic | Database |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3898093/ https://www.ncbi.nlm.nih.gov/pubmed/24423189 http://dx.doi.org/10.1186/1471-2105-15-13 |
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