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. 2004 Feb 18:5:16.
doi: 10.1186/1471-2105-5-16.

V体育平台登录 - GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies

Bing Zhang  1 , Denise Schmoyer, Stefan Kirov, Jay Snoddy
Affiliations

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

VSports app下载 - Bing Zhang et al. BMC Bioinformatics. .

Abstract

Background: Microarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly VSports手机版. 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 V体育安卓版. 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 V体育ios版. .

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Figures

Figure 1
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.
Figure 2
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).
Figure 3
Figure 3
Output user interface of the GOTM The GOTree window displays the expandable tree structure of the GO categories. Each GO category is followed by three parameters: O (Observed gene number in the category); E (Expected gene number in the category) and R (Ratio of enrichment for the category). The fourth parameter P (p value calculated from the hypergeometric test) is given for the categories with R > 1 to indicate the significance of enrichment. Categories with P < 0.01 are colored red. The gene/category list window displays genes in selected GO categories ("eye morphogenesis" in this case) and the names of enriched GO categories followed by the parameters O, E, R and P. The genes are represented by LocusIDs followed by gene symbols and ratios in the microarray experiment. The gene information window displays the gene information record for the selected gene.
Figure 4
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.
Figure 5
Figure 5
DAG view of enriched GO categories The enriched GO categories are brought together and visualized as a Directed Acyclic Graph (DAG). Categories in red are enriched ones while those in black are non-enriched parents. The list of genes in each category can be retrieved by click on the name of the categories.
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References

    1. Wain HM, Bruford EA, Lovering RC, Lush MJ, Wright MW, Povey S. Guidelines for Human Gene Nomenclature. Genomics. 2002;79:464–470. doi: 10.1006/geno.2002.6748. - DOI - PubMed
    1. The Gene Ontology Consortium Creating the Gene Ontology resource: design and implementation. Genome Res. 2001;11:1425–1433. doi: 10.1101/gr.180801. - DOI - PMC - PubMed
    1. Jenssen T-K, Legreid A, Komorowski J, Hovig E. A literature network of human genes for high-throughput analysis of gene expression. Nat Genet. 2001;28:21–28. doi: 10.1038/88213. - DOI - PubMed
    1. Masys DR, Welsh JB, Fink JL, Gribskov M, Klacansky I, Corbeil J. Use of keyword hierarchies to interpret gene expression patterns. Bioinformatics. 2001;17:319–326. doi: 10.1093/bioinformatics/17.4.319. - VSports app下载 - DOI - PubMed
    1. Chaussabel D, Sher A. Mining microarray expression data by literature profiling. Genome Biol. 2002;3:R55. doi: 10.1186/gb-2002-3-10-research0055. - DOI - PMC - PubMed

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