Ascribe biological significance to large lists of genes by annotating them with their corresponding GO categories
Tutorial and training materials by OpenHelix
|Learn how to use GoMiner, a set of publicly available tools that can enable you to ascribe biological significance to large lists of genes by annotating them with their corresponding Gene Ontology, or GO, categories. GoMiner extracts these annotated terms associated with each gene to provide a synopsis of the biology for you. It is available in both web-based and downloadable versions and it not only annotates gene lists with GO descriptions, but it also clusters them into groups and provides detailed statistics. Once you learn what types of biological categories your genes of interest are enriched in you can quickly understand some of the underlying biology and learn where to focus your future studies. Links to additional information from sources such as Entrez's structural database, MMDB, BioCarta, Reactome, and more are also provided. Furthermore, there are several additional tools to help convert gene lists into any format you wish, manipulate and export your data in many ways and generate fantastic visuals to display your results.|
- Use both the downloadable GUI and web-based High-Throughput GoMiner tools
- Understand and manipulate your GO annotated data
- Construct beautiful visuals to display and present your results clearly
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More about the resource:
GoMiner is a free resource brought to you by the Genomics and Bioinformatics Group of LMP, CCR and NCI. It is actively maintained by this group and they update this database frequently. You can use the tools GoMiner provides to functionally annotate lists of interesting genes, such as those under or overexpressed in microarray experiments, in terms of GO categories. It is also great for other types of users, such as those involved in proteomic studies in which there may be no expression data and only a single list of genes of interest. The GO category annotation and the accompanying rigorous clustering statistics can enable you to learn about the associated biological processes of your genes of interest. You will also find many links, help and extensive visualization tools.
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