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VisHiC—hierarchical functional enrichment analysis of microarray data
Measuring gene expression levels with microarrays is one of the key technologies of modern genomics. Clustering of microarray data is an important application, as genes with similar expression profiles may be regulated by common pathways and involved in related functions. Gene Ontology (GO) analysis...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2703939/ https://www.ncbi.nlm.nih.gov/pubmed/19483095 http://dx.doi.org/10.1093/nar/gkp435 |
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author | Krushevskaya, Darya Peterson, Hedi Reimand, Jüri Kull, Meelis Vilo, Jaak |
author_facet | Krushevskaya, Darya Peterson, Hedi Reimand, Jüri Kull, Meelis Vilo, Jaak |
author_sort | Krushevskaya, Darya |
collection | PubMed |
description | Measuring gene expression levels with microarrays is one of the key technologies of modern genomics. Clustering of microarray data is an important application, as genes with similar expression profiles may be regulated by common pathways and involved in related functions. Gene Ontology (GO) analysis and visualization allows researchers to study the biological context of discovered clusters and characterize genes with previously unknown functions. We present VisHiC (Visualization of Hierarchical Clustering), a web server for clustering and compact visualization of gene expression data combined with automated function enrichment analysis. The main output of the analysis is a dendrogram and visual heatmap of the expression matrix that highlights biologically relevant clusters based on enriched GO terms, pathways and regulatory motifs. Clusters with most significant enrichments are contracted in the final visualization, while less relevant parts are hidden altogether. Such a dense representation of microarray data gives a quick global overview of thousands of transcripts in many conditions and provides a good starting point for further analysis. VisHiC is freely available at http://biit.cs.ut.ee/vishic. |
format | Text |
id | pubmed-2703939 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-27039392009-07-01 VisHiC—hierarchical functional enrichment analysis of microarray data Krushevskaya, Darya Peterson, Hedi Reimand, Jüri Kull, Meelis Vilo, Jaak Nucleic Acids Res Articles Measuring gene expression levels with microarrays is one of the key technologies of modern genomics. Clustering of microarray data is an important application, as genes with similar expression profiles may be regulated by common pathways and involved in related functions. Gene Ontology (GO) analysis and visualization allows researchers to study the biological context of discovered clusters and characterize genes with previously unknown functions. We present VisHiC (Visualization of Hierarchical Clustering), a web server for clustering and compact visualization of gene expression data combined with automated function enrichment analysis. The main output of the analysis is a dendrogram and visual heatmap of the expression matrix that highlights biologically relevant clusters based on enriched GO terms, pathways and regulatory motifs. Clusters with most significant enrichments are contracted in the final visualization, while less relevant parts are hidden altogether. Such a dense representation of microarray data gives a quick global overview of thousands of transcripts in many conditions and provides a good starting point for further analysis. VisHiC is freely available at http://biit.cs.ut.ee/vishic. Oxford University Press 2009-07-01 2009-05-29 /pmc/articles/PMC2703939/ /pubmed/19483095 http://dx.doi.org/10.1093/nar/gkp435 Text en © 2009 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Krushevskaya, Darya Peterson, Hedi Reimand, Jüri Kull, Meelis Vilo, Jaak VisHiC—hierarchical functional enrichment analysis of microarray data |
title | VisHiC—hierarchical functional enrichment analysis of microarray data |
title_full | VisHiC—hierarchical functional enrichment analysis of microarray data |
title_fullStr | VisHiC—hierarchical functional enrichment analysis of microarray data |
title_full_unstemmed | VisHiC—hierarchical functional enrichment analysis of microarray data |
title_short | VisHiC—hierarchical functional enrichment analysis of microarray data |
title_sort | vishic—hierarchical functional enrichment analysis of microarray data |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2703939/ https://www.ncbi.nlm.nih.gov/pubmed/19483095 http://dx.doi.org/10.1093/nar/gkp435 |
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