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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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Related in: MedlinePlus

Input user interface of the GOTM Input interface for uploading analysis parameters (analysis name, ID type and analysis type) and data (interesting gene list and reference gene list).
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Figure 2: Input user interface of the GOTM Input interface for uploading analysis parameters (analysis name, ID type and analysis type) and data (interesting gene list and reference gene list).

Mentions: Figure 2 shows the input user interface of GOTM. The input identifiers for GOTM can be LocusIDs, Gene Symbols, Affymetrix probe set IDs, Unigene IDs, Swiss-Prot IDs or Ensembl IDs. GOTM currently supports Gene Symbols from human, mouse, rat and fly, and Affymetrix probe set IDs from 8 human arrays and 6 mouse arrays. The user can choose either single gene set analysis or interesting gene set vs. reference gene set analysis. For single gene set analysis, only the file of the interesting gene set is needed, and the result will be a GOTree for the gene set. For interesting gene set vs. reference gene set analysis, the user needs to upload the file of the interesting gene set, and choose an existing reference gene set from our pre-stored gene sets, including all genes in the mouse genome, all genes in the human genome and gene sets from 14 Affymetrix arrays, or upload the file of the reference gene set. The result will be a GOTree for the interesting gene set, and identified GO categories with relatively enriched gene numbers in the interesting gene set compared to the reference gene set. The user can browse his local machine for the input files. The input file should be a plain text file, including the appropriate ID (required) and corresponding microarray ratio (optional), separated by tabs in the format of one ID per row. A unique analysis name is assigned and can be used to retrieve the results for a subsequent user session. Stored results can be accessed through the RETRIEVE TREE button and deleted through the DELETE TREE button at the top of input user interface. The results will be stored until the next periodical upgrading of GeneKeyDB. An email notice will be sent to the users after the updating.


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)

Input user interface of the GOTM Input interface for uploading analysis parameters (analysis name, ID type and analysis type) and data (interesting gene list and reference gene list).
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Input user interface of the GOTM Input interface for uploading analysis parameters (analysis name, ID type and analysis type) and data (interesting gene list and reference gene list).
Mentions: Figure 2 shows the input user interface of GOTM. The input identifiers for GOTM can be LocusIDs, Gene Symbols, Affymetrix probe set IDs, Unigene IDs, Swiss-Prot IDs or Ensembl IDs. GOTM currently supports Gene Symbols from human, mouse, rat and fly, and Affymetrix probe set IDs from 8 human arrays and 6 mouse arrays. The user can choose either single gene set analysis or interesting gene set vs. reference gene set analysis. For single gene set analysis, only the file of the interesting gene set is needed, and the result will be a GOTree for the gene set. For interesting gene set vs. reference gene set analysis, the user needs to upload the file of the interesting gene set, and choose an existing reference gene set from our pre-stored gene sets, including all genes in the mouse genome, all genes in the human genome and gene sets from 14 Affymetrix arrays, or upload the file of the reference gene set. The result will be a GOTree for the interesting gene set, and identified GO categories with relatively enriched gene numbers in the interesting gene set compared to the reference gene set. The user can browse his local machine for the input files. The input file should be a plain text file, including the appropriate ID (required) and corresponding microarray ratio (optional), separated by tabs in the format of one ID per row. A unique analysis name is assigned and can be used to retrieve the results for a subsequent user session. Stored results can be accessed through the RETRIEVE TREE button and deleted through the DELETE TREE button at the top of input user interface. The results will be stored until the next periodical upgrading of GeneKeyDB. An email notice will be sent to the users after the updating.

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
Related in: MedlinePlus