[BioC] Genes and their associated GO terms and pathways

Jani, Saurin D jani at musc.edu
Fri Nov 21 15:06:10 CET 2008

Hi Daren,

If you have differentially expressed (DE) genes and you know which array type (e.g., hgu133a or any Affy/Agilent  expression arrays) you are using...try using GeneMesh. 

GeneMesh is web-based microarray analysis software at Computational Biology Resource Center (CBRC) of  Medical University of South Carolina.
GeneMesh uses R/Bioconductor packages on backend to generate Heatmap/Dotplot of DE genes. It will also provide you which of your DE genes resides in which GO terms/pathways.

Only Input Requirement for GeneMesh is: CSV file with normalized expression data. 

There are THREE unique features of GeneMesh:

1. It provides "search engine" like user interface to your DE genes, so, you can search for : e.g. "angiogenesis" or "DiGeorge syndrome" or "stem cells" etc. and view Heatmap of DE Genes. Along with Heatmap you can see which GO terms/pathways associated with those DE genes. To view Heatmap you need to upload your DE genes to GeneMesh. 

2. "One Click" analysis of DE genes.  If you are interested in Anatomy or Diseases such as "Cardiovascular Diseases" or "Immune System Diseases" or "Congenital, Hereditary, and Neonatal Diseases and Abnormalities" and more ... You can upload your data and on one click you will see how many of DE genes reside in to which Diseases or Anatomy structure.

3. If you do NOT want to upload your data, you can simply search like search engine or you can put NCBI Enrez GeneIDs and perform above two analysis. Again, if you do not upload your data you will not be able to see heatmap.

Freely Available Online at: http://proteogenomics.musc.edu/genemesh/
Watch DEMO: http://cbrc.musc.edu/homepage/jani/genemesh/help.html

From: bioconductor-bounces at stat.math.ethz.ch [bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Daren Tan [daren76 at hotmail.com]
Sent: Friday, November 21, 2008 6:53 AM
To: bioconductor at stat.math.ethz.ch
Subject: [BioC] Genes and their associated GO terms and pathways

I have a set of differentially expressed genes, and want to know what are their GO terms, and pathway that they reside in. I have installed GO.db and KEGG.db, but unsure how to get started.

For examples, genes <- c("TP53", "SOX4", "PTEN"), whats next ?

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