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Identifying functional gene sets from hierarchically clustered expression data: map of abiotic stress regulated genes in Arabidopsis thaliana.

Kankainen M, Brader G, Törönen P, Palva ET, Holm L - Nucleic Acids Res. (2006)

Bottom Line: The tool also identifies a plausible cluster set, which represents the key biological functions affected by the experiment.The analysis not only identified known biological functions, but also brought into focus the less established connections to defense-related gene clusters.Thus, in comparison to analyses of manually selected gene lists, the systematic analysis of every cluster can reveal unexpected biological phenomena and produce much more comprehensive biological insights to the experiment of interest.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biotechnology, PO Box 56 (Viikinkaari 5), FIN-00014, Helsinki, Finland.

ABSTRACT
We present MultiGO, a web-enabled tool for the identification of biologically relevant gene sets from hierarchically clustered gene expression trees (http://ekhidna.biocenter.helsinki.fi/poxo/multigo). High-throughput gene expression measuring techniques, such as microarrays, are nowadays often used to monitor the expression of thousands of genes. Since these experiments can produce overwhelming amounts of data, computational methods that assist the data analysis and interpretation are essential. MultiGO is a tool that automatically extracts the biological information for multiple clusters and determines their biological relevance, and hence facilitates the interpretation of the data. Since the entire expression tree is analysed, MultiGO is guaranteed to report all clusters that share a common enriched biological function, as defined by Gene Ontology annotations. The tool also identifies a plausible cluster set, which represents the key biological functions affected by the experiment. The performance is demonstrated by analysing drought-, cold- and abscisic acid-related expression data sets from Arabidopsis thaliana. The analysis not only identified known biological functions, but also brought into focus the less established connections to defense-related gene clusters. Thus, in comparison to analyses of manually selected gene lists, the systematic analysis of every cluster can reveal unexpected biological phenomena and produce much more comprehensive biological insights to the experiment of interest.

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Expression of genes involved in the reported abiotic stress responses. Figure shows those clusters that are the largest clusters having the given GO-term as their best GO-term (response to temperature stimulus is the best GO-term of Node_11728, response to heat of Node_11805 and response to water deprivation of Node_10337) (response to cold of Node_10952 is represented by a yellow square). Clusters sharing the same best GO-term as the largest cluster are coloured in purple in the expression tree. The table shows GO-terms that are related to abiotic functions (ABA is response to abscisic acid stimulus, Abiotic is response to abiotic stimulus, Temp is response to temperature stimulus, Heat is response to heat, Cold is response to cold and Water is response to water) and that are detected as significant within the set of clusters. Notations of the monitored experimental data sets are explained in Supplementary Table 1.
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fig4: Expression of genes involved in the reported abiotic stress responses. Figure shows those clusters that are the largest clusters having the given GO-term as their best GO-term (response to temperature stimulus is the best GO-term of Node_11728, response to heat of Node_11805 and response to water deprivation of Node_10337) (response to cold of Node_10952 is represented by a yellow square). Clusters sharing the same best GO-term as the largest cluster are coloured in purple in the expression tree. The table shows GO-terms that are related to abiotic functions (ABA is response to abscisic acid stimulus, Abiotic is response to abiotic stimulus, Temp is response to temperature stimulus, Heat is response to heat, Cold is response to cold and Water is response to water) and that are detected as significant within the set of clusters. Notations of the monitored experimental data sets are explained in Supplementary Table 1.

Mentions: Another GO-term involved in abiotic stress responses and listed in both tables 1 and 2 is response to heat. This listing can then be expanded by collecting other related processes from Table 2: response to temperature stimulus, response to cold and response to water deprivation. Expression of genes involved in these abiotic stresses can be viewed in Figure 4. Figure 4 shows those clusters (Node_11728, Node_11805 and Node_10337) that are the largest clusters having the corresponding term (response to temperature stimulus, response to heat and response to water deprivation) as the best GO-term, and contain most of the other clusters sharing the same best GO-term. Note that the largest cluster having response to cold function as its most significant term (Node_10952) is a child of Node_11728 and is represented as a yellow square in the figure.


Identifying functional gene sets from hierarchically clustered expression data: map of abiotic stress regulated genes in Arabidopsis thaliana.

Kankainen M, Brader G, Törönen P, Palva ET, Holm L - Nucleic Acids Res. (2006)

Expression of genes involved in the reported abiotic stress responses. Figure shows those clusters that are the largest clusters having the given GO-term as their best GO-term (response to temperature stimulus is the best GO-term of Node_11728, response to heat of Node_11805 and response to water deprivation of Node_10337) (response to cold of Node_10952 is represented by a yellow square). Clusters sharing the same best GO-term as the largest cluster are coloured in purple in the expression tree. The table shows GO-terms that are related to abiotic functions (ABA is response to abscisic acid stimulus, Abiotic is response to abiotic stimulus, Temp is response to temperature stimulus, Heat is response to heat, Cold is response to cold and Water is response to water) and that are detected as significant within the set of clusters. Notations of the monitored experimental data sets are explained in Supplementary Table 1.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC1636450&req=5

fig4: Expression of genes involved in the reported abiotic stress responses. Figure shows those clusters that are the largest clusters having the given GO-term as their best GO-term (response to temperature stimulus is the best GO-term of Node_11728, response to heat of Node_11805 and response to water deprivation of Node_10337) (response to cold of Node_10952 is represented by a yellow square). Clusters sharing the same best GO-term as the largest cluster are coloured in purple in the expression tree. The table shows GO-terms that are related to abiotic functions (ABA is response to abscisic acid stimulus, Abiotic is response to abiotic stimulus, Temp is response to temperature stimulus, Heat is response to heat, Cold is response to cold and Water is response to water) and that are detected as significant within the set of clusters. Notations of the monitored experimental data sets are explained in Supplementary Table 1.
Mentions: Another GO-term involved in abiotic stress responses and listed in both tables 1 and 2 is response to heat. This listing can then be expanded by collecting other related processes from Table 2: response to temperature stimulus, response to cold and response to water deprivation. Expression of genes involved in these abiotic stresses can be viewed in Figure 4. Figure 4 shows those clusters (Node_11728, Node_11805 and Node_10337) that are the largest clusters having the corresponding term (response to temperature stimulus, response to heat and response to water deprivation) as the best GO-term, and contain most of the other clusters sharing the same best GO-term. Note that the largest cluster having response to cold function as its most significant term (Node_10952) is a child of Node_11728 and is represented as a yellow square in the figure.

Bottom Line: The tool also identifies a plausible cluster set, which represents the key biological functions affected by the experiment.The analysis not only identified known biological functions, but also brought into focus the less established connections to defense-related gene clusters.Thus, in comparison to analyses of manually selected gene lists, the systematic analysis of every cluster can reveal unexpected biological phenomena and produce much more comprehensive biological insights to the experiment of interest.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biotechnology, PO Box 56 (Viikinkaari 5), FIN-00014, Helsinki, Finland.

ABSTRACT
We present MultiGO, a web-enabled tool for the identification of biologically relevant gene sets from hierarchically clustered gene expression trees (http://ekhidna.biocenter.helsinki.fi/poxo/multigo). High-throughput gene expression measuring techniques, such as microarrays, are nowadays often used to monitor the expression of thousands of genes. Since these experiments can produce overwhelming amounts of data, computational methods that assist the data analysis and interpretation are essential. MultiGO is a tool that automatically extracts the biological information for multiple clusters and determines their biological relevance, and hence facilitates the interpretation of the data. Since the entire expression tree is analysed, MultiGO is guaranteed to report all clusters that share a common enriched biological function, as defined by Gene Ontology annotations. The tool also identifies a plausible cluster set, which represents the key biological functions affected by the experiment. The performance is demonstrated by analysing drought-, cold- and abscisic acid-related expression data sets from Arabidopsis thaliana. The analysis not only identified known biological functions, but also brought into focus the less established connections to defense-related gene clusters. Thus, in comparison to analyses of manually selected gene lists, the systematic analysis of every cluster can reveal unexpected biological phenomena and produce much more comprehensive biological insights to the experiment of interest.

Show MeSH
Related in: MedlinePlus