GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies. 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/.
Keywords for this software
References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Cao, Jing; Zhang, Song: A Bayesian extension of the hypergeometric test for functional enrichment analysis (2014)
- Ding, Min; Wang, Haiyun; Chen, Jiajia; Shen, Bairong; Xu, Zhonghua: Identification and functional annotation of genome-wide ER-regulated genes in breast cancer based on ChIP-Seq data (2012)
- Drăghici, Sorin: Statistics and data analysis for microarrays using R and Bioconductor. With CD-ROM. (2012)
- Jupiter, Daniel; Şahutoğlu, Jessica; Vanburen, Vincent: TreeHugger: a new test for enrichment of gene ontology terms (2010)
- Wang, Lily; Chen, Xi; Wolfinger, Russell D.; Franklin, Jeffrey L.; Coffey, Robert J.; Zhang, Bing: A unified mixed effects model for gene set analysis of time course microarray experiments (2009)
- Pan, Wei: Incorporating biological information as a prior in an empirical Bayes approach to analyzing microarray data (2005)