Activate license key | Login | Register
OpenHelix

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.
Advertisement:

You'll learn:

  • 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


Categories

View additional tutorials for resources in

Recent BioMed Central research articles citing this resource

Sun Zhen et al., Differential analysis of N-glycoproteome between hepatocellular carcinoma and normal human liver tissues by combination of multiple protease digestion and solid phase based labeling. Clinical Proteomics (2014) doi:10.1186/1559-0275-11-26

Tang Jing et al., Proteomic profiling of the phosphoproteins in the rat thalamus, hippocampus and frontal lobe after propofol anesthesia General pharmacology and pharmacokinetics. BMC Anesthesiology (2014) doi:10.1186/1471-2253-14-3

Dogan V Meeshanthini et al., The effect of smoking on DNA methylation of peripheral blood mononuclear cells from African American women Human and rodent genomics. BMC Genomics (2014) doi:10.1186/1471-2164-15-151

Yoshizaki Hisayoshi et al., Elucidation of the evolutionary expansion of phosphorylation signaling networks using comparative phosphomotif analysis Comparative and evolutionary genomics. BMC Genomics (2014) doi:10.1186/1471-2164-15-546

Rotunno Melissa et al., Parity-related molecular signatures and breast cancer subtypes by estrogen receptor status. Breast Cancer Research (2014) doi:10.1186/bcr3689

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.


Click here for technical information on using OpenHelix tutorial and training materials

The materials and slides offered can not be resold or used for profit purposes. Reproduction, distribution and/or use is strictly limited to instructional purposes only and can not be used for for monetary gain or wide distribution.
Copyright 2009, OpenHelix, LLC.

design & development: biobyte solutions