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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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Schemetic overview of the GOTM GOTM is flexible in the input identifier (LocusID, gene symbol, Affymetrix Probe Set ID, Unigene ID, Swiss-Prot ID and Ensembl ID). GOTM produces different kinds of visualizations for different purposes, including 1) an expandable GOTree for online browsing 2) HTML output for an archivable record and 3) a bar chart for publication. Statistical analysis is used to compare gene sets. Sub-tree and DAG (Direct Acyclic Graph) can be generated for enriched GO categories.
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Figure 1: Schemetic overview of the GOTM GOTM is flexible in the input identifier (LocusID, gene symbol, Affymetrix Probe Set ID, Unigene ID, Swiss-Prot ID and Ensembl ID). GOTM produces different kinds of visualizations for different purposes, including 1) an expandable GOTree for online browsing 2) HTML output for an archivable record and 3) a bar chart for publication. Statistical analysis is used to compare gene sets. Sub-tree and DAG (Direct Acyclic Graph) can be generated for enriched GO categories.

Mentions: GOTM is implemented in PHP. It is accessible through IE5.0 or higher and Netscape 7 or higher from multiple platforms. GOTM can be accessed from the website . Figure 1 shows the schematic overview of GOTM. After reading the input parameters and data files from the user, GOTM interacts with the local database GeneKeyDB (S.K. et al., manuscript in preparation) to convert gene symbols, Affymetrix probe set IDs, Unigene IDs, Swiss-Prot IDs or Ensembl IDs to LocusIDs. The hierarchical GOTree structure is then generated using the PHP Layers Menu System [13] and sent back to the user. It is based on the GO annotation for LocusIDs as recorded in GeneKeyDB. The user can browse or query the GOTree for desired GO categories. The GOTree can be exported and stored locally in html format. Bar charts for GO categories at different annotation levels can be generated for publication. The bar chart is created using ChartDirector [14]. Statistical analysis compares the interesting gene set and the reference gene set and provides the user with GO categories with enriched gene numbers. The enriched GO categories are presented in flat view format, sub-tree view format and DAG view format. The DAG is created using Graphviz [15]. Subsets of genes in each GO category can be displayed and additional information for each gene can be further retrieved from GeneKeyDB.


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)

Schemetic overview of the GOTM GOTM is flexible in the input identifier (LocusID, gene symbol, Affymetrix Probe Set ID, Unigene ID, Swiss-Prot ID and Ensembl ID). GOTM produces different kinds of visualizations for different purposes, including 1) an expandable GOTree for online browsing 2) HTML output for an archivable record and 3) a bar chart for publication. Statistical analysis is used to compare gene sets. Sub-tree and DAG (Direct Acyclic Graph) can be generated for enriched GO categories.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Schemetic overview of the GOTM GOTM is flexible in the input identifier (LocusID, gene symbol, Affymetrix Probe Set ID, Unigene ID, Swiss-Prot ID and Ensembl ID). GOTM produces different kinds of visualizations for different purposes, including 1) an expandable GOTree for online browsing 2) HTML output for an archivable record and 3) a bar chart for publication. Statistical analysis is used to compare gene sets. Sub-tree and DAG (Direct Acyclic Graph) can be generated for enriched GO categories.
Mentions: GOTM is implemented in PHP. It is accessible through IE5.0 or higher and Netscape 7 or higher from multiple platforms. GOTM can be accessed from the website . Figure 1 shows the schematic overview of GOTM. After reading the input parameters and data files from the user, GOTM interacts with the local database GeneKeyDB (S.K. et al., manuscript in preparation) to convert gene symbols, Affymetrix probe set IDs, Unigene IDs, Swiss-Prot IDs or Ensembl IDs to LocusIDs. The hierarchical GOTree structure is then generated using the PHP Layers Menu System [13] and sent back to the user. It is based on the GO annotation for LocusIDs as recorded in GeneKeyDB. The user can browse or query the GOTree for desired GO categories. The GOTree can be exported and stored locally in html format. Bar charts for GO categories at different annotation levels can be generated for publication. The bar chart is created using ChartDirector [14]. Statistical analysis compares the interesting gene set and the reference gene set and provides the user with GO categories with enriched gene numbers. The enriched GO categories are presented in flat view format, sub-tree view format and DAG view format. The DAG is created using Graphviz [15]. Subsets of genes in each GO category can be displayed and additional information for each gene can be further retrieved from GeneKeyDB.

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