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VisHiC--hierarchical functional enrichment analysis of microarray data.

ABSTRACT: 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.

SUBMITTER: Krushevskaya D

PROVIDER: S-EPMC2703939 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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VisHiC--hierarchical functional enrichment analysis of microarray data.

Krushevskaya Darya D Peterson Hedi H Reimand Jüri J Kull Meelis M Vilo Jaak J

Nucleic acids research 20090529 Web Server issue

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 ...[more]

PMID: 19483095

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Project description:UNLABELLED: The widespread availability of microarray technology has driven functional genomics to the forefront as scientists seek to draw meaningful biological conclusions from their microarray results. Gene annotation enrichment analysis is a functional analysis technique that has gained widespread attention and for which many tools have been developed. Unfortunately, most of these tools have limited support for agricultural species. Here, we evaluate and compare four publicly available computational tools (Onto-Express, EasyGO, GOstat, and DAVID) that support analysis of gene expression datasets in agricultural species. We use AgBase as the functional annotation reference for agricultural species. The selected tools were evaluated based on i) available features, usage and accessibility, ii) implemented statistical computational methods, and iii) annotation and enrichment performance analysis. Annotation was assessed using a randomly selected test gene annotation set and an experimental differentially expressed gene-set--both from chicken. The experimental set was also used to evaluate identification of enriched functional groups.Comparison of the tools shows that they produce different sets of annotations for the two datasets and different functional groups for the experimental dataset. While DAVID, GOstat and Onto-Express annotate comparable numbers of genes, DAVID provides by far the most annotations per gene. However, many of DAVID's annotations appear to be redundant or are at very high levels in the GO hierarchy. The GOSlim distribution of annotations shows that GOstat, Onto-Express and EasyGO provide similar GO distributions to those found in AgBase while annotations from DAVID show a different GOSlim distribution, again probably due to duplication and many non-specific terms. No consistent trends were found in results of GO term over/under representation analysis applied to the experimental data using different tools. While GOstat, David and Onto-Express could retrieve some significantly enriched terms, EasyGO did not show any significantly enriched terms. There was little agreement about the enriched terms identified by the tools. CONCLUSION: Different tools for functionally annotating gene sets and identifying significantly enriched GO categories differ widely in their results when applied to a test annotation gene set and an experimental dataset from chicken. These results emphasize the need for care when interpreting the results of such analysis and the lack of standardization of approaches.

Project description:BackgroundMethods for gene-class testing, such as Gene Set Enrichment Analysis (GSEA), incorporate biological knowledge into the analysis and interpretation of microarray data by comparing gene expression patterns to pathways, systems and emergent phenotypes. However, to use GSEA to its full capability with non-mammalian model organisms, a microarray platform must be annotated with human gene symbols. Doing so enables the ability to relate a model organism's gene expression, in response to a given treatment, to potential human health consequences of that treatment. We enhanced the annotation of a microarray platform from a non-mammalian model organism, and then used the GSEA approach in a reanalysis of a study examining the biological significance of acute and chronic methylmercury exposure on liver tissue of fathead minnow (Pimephales promelas). Using GSEA, we tested the hypothesis that fathead livers, in response to methylmercury exposure, would exhibit gene expression patterns similar to diseased human livers.ResultsWe describe an enhanced annotation of the fathead minnow microarray platform with human gene symbols. This resource is now compatible with the GSEA approach for gene-class testing. We confirmed that GSEA, using this enhanced microarray platform, is able to recover results consistent with a previous analysis of fathead minnow exposure to methylmercury using standard analytical approaches. Using GSEA to compare fathead gene expression profiles to human phenotypes, we also found that fathead methylmercury-treated livers exhibited expression profiles that are homologous to human systems & pathways and results in damage that is similar to those of human liver damage associated with hepatocellular carcinoma and hepatitis B.ConclusionsThis study describes a powerful resource for enabling the use of non-mammalian model organisms in the study of human health significance. Results of microarray gene expression studies involving fathead minnow, typically used for aquatic ecological toxicology studies, can now be used to generate hypotheses regarding consequences of contaminants and other stressors on humans. The same approach can be used with other model organisms with microarray platforms annotated in a similar manner.

Dataset Information

VisHiC--hierarchical functional enrichment analysis of microarray data.

Publications

VisHiC--hierarchical functional enrichment analysis of microarray data.

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