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GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies.

Zhang B, Schmoyer D, Kirov S, Snoddy J - BMC Bioinformatics (2004)

Bottom Line: GOTree Machine generates a GOTree, a tree-like structure to navigate the Gene Ontology Directed Acyclic Graph for input gene sets.This system provides user friendly data navigation and visualization.Statistical analysis helps users to identify the most important Gene Ontology categories for the input gene sets and suggests biological areas that warrant further study.

View Article: PubMed Central - HTML - PubMed

Affiliation: Graduate School in Genome Science and Technology, University of Tennessee-Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. zhangb@ornl.gov

ABSTRACT

Background: Microarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in the gene sets.

Results: We have created a web-based tool for data analysis and data visualization for sets of genes called GOTree Machine (GOTM). This tool was originally intended to analyze sets of co-regulated genes identified from microarray analysis but is adaptable for use with other gene sets from other high-throughput analyses. GOTree Machine generates a GOTree, a tree-like structure to navigate the Gene Ontology Directed Acyclic Graph for input gene sets. This system provides user friendly data navigation and visualization. Statistical analysis helps users to identify the most important Gene Ontology categories for the input gene sets and suggests biological areas that warrant further study. GOTree Machine is available online at http://genereg.ornl.gov/gotm/.

Conclusion: GOTree Machine has a broad application in functional genomic, proteomic and other high-throughput methods that generate large sets of interesting genes; its primary purpose is to help users sort for interesting patterns in gene sets.

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Sub-tree view of enriched GO categories The enriched GO categories are brought together and visualized as a sub-tree. Categories in red are enriched ones while those in black are non-enriched parents. Enriched categories are followed by four parameters, O (Observed gene number in the category); E (Expected gene number in the category), R (Ratio of enrichment for the category) and P (p value calculated from the hypergeometric test). Numbers at the left indicate the Gene Ontology annotation level.
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Figure 4: Sub-tree view of enriched GO categories The enriched GO categories are brought together and visualized as a sub-tree. Categories in red are enriched ones while those in black are non-enriched parents. Enriched categories are followed by four parameters, O (Observed gene number in the category); E (Expected gene number in the category), R (Ratio of enrichment for the category) and P (p value calculated from the hypergeometric test). Numbers at the left indicate the Gene Ontology annotation level.

Mentions: The gene/category list window shows the genes in a selected GO category, and enriched GO categories in the three main GO categories, biological process, molecular function and cellular component respectively. Each gene is represented by a LocusID, followed by the input ID. In addition, the ratio in the microarray experiment is shown if that information was included in the input file. Up-regulated genes are colored red while down-regulated genes are colored green. A flat view of enriched GO categories doesn't reveal the relationship among the GO categories. When tens or hundreds of GO categories are identified as significantly enriched, it becomes difficult for users to interpret the results. In this case, the user can press the TREE VIEW button to get a sub-tree (Figure 4) or press the DAG VIEW button to get a DAG (Figure 5) for the enriched GO categories in a new window. The GO categories in red in the sub-tree or the DAG are the enriched GO categories while the black ones are their non-enriched parents. The sub-tree and the DAG assemble related enriched GO categories together indicating important biological areas that are worth further study. By clicking on individual LocusIDs in the gene/category list window, related information for the genes will be queried from GeneKeyDB and shown in the gene information window.


GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies.

Zhang B, Schmoyer D, Kirov S, Snoddy J - BMC Bioinformatics (2004)

Sub-tree view of enriched GO categories The enriched GO categories are brought together and visualized as a sub-tree. Categories in red are enriched ones while those in black are non-enriched parents. Enriched categories are followed by four parameters, O (Observed gene number in the category); E (Expected gene number in the category), R (Ratio of enrichment for the category) and P (p value calculated from the hypergeometric test). Numbers at the left indicate the Gene Ontology annotation level.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC373441&req=5

Figure 4: Sub-tree view of enriched GO categories The enriched GO categories are brought together and visualized as a sub-tree. Categories in red are enriched ones while those in black are non-enriched parents. Enriched categories are followed by four parameters, O (Observed gene number in the category); E (Expected gene number in the category), R (Ratio of enrichment for the category) and P (p value calculated from the hypergeometric test). Numbers at the left indicate the Gene Ontology annotation level.
Mentions: The gene/category list window shows the genes in a selected GO category, and enriched GO categories in the three main GO categories, biological process, molecular function and cellular component respectively. Each gene is represented by a LocusID, followed by the input ID. In addition, the ratio in the microarray experiment is shown if that information was included in the input file. Up-regulated genes are colored red while down-regulated genes are colored green. A flat view of enriched GO categories doesn't reveal the relationship among the GO categories. When tens or hundreds of GO categories are identified as significantly enriched, it becomes difficult for users to interpret the results. In this case, the user can press the TREE VIEW button to get a sub-tree (Figure 4) or press the DAG VIEW button to get a DAG (Figure 5) for the enriched GO categories in a new window. The GO categories in red in the sub-tree or the DAG are the enriched GO categories while the black ones are their non-enriched parents. The sub-tree and the DAG assemble related enriched GO categories together indicating important biological areas that are worth further study. By clicking on individual LocusIDs in the gene/category list window, related information for the genes will be queried from GeneKeyDB and shown in the gene information window.

Bottom Line: GOTree Machine generates a GOTree, a tree-like structure to navigate the Gene Ontology Directed Acyclic Graph for input gene sets.This system provides user friendly data navigation and visualization.Statistical analysis helps users to identify the most important Gene Ontology categories for the input gene sets and suggests biological areas that warrant further study.

View Article: PubMed Central - HTML - PubMed

Affiliation: Graduate School in Genome Science and Technology, University of Tennessee-Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. zhangb@ornl.gov

ABSTRACT

Background: Microarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in the gene sets.

Results: We have created a web-based tool for data analysis and data visualization for sets of genes called GOTree Machine (GOTM). This tool was originally intended to analyze sets of co-regulated genes identified from microarray analysis but is adaptable for use with other gene sets from other high-throughput analyses. GOTree Machine generates a GOTree, a tree-like structure to navigate the Gene Ontology Directed Acyclic Graph for input gene sets. This system provides user friendly data navigation and visualization. Statistical analysis helps users to identify the most important Gene Ontology categories for the input gene sets and suggests biological areas that warrant further study. GOTree Machine is available online at http://genereg.ornl.gov/gotm/.

Conclusion: GOTree Machine has a broad application in functional genomic, proteomic and other high-throughput methods that generate large sets of interesting genes; its primary purpose is to help users sort for interesting patterns in gene sets.

Show MeSH