Genes and their associated GO terms and pathways
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Daren Tan ▴ 190
@daren-tan-3105
Last seen 10.2 years ago
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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@sean-davis-490
Last seen 3 months ago
United States
On Fri, Nov 21, 2008 at 6:53 AM, Daren Tan <daren76@hotmail.com> wrote: > > 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 ? > What expression array are you using? The simplest way to do this is to get the annotation package for that chip and then use the array identifiers (not gene symbols) to look up the information in the chip-based annotation package. Sean [[alternative HTML version deleted]]
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@saurin-d-jani-944
Last seen 10.2 years ago
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 Saurin ________________________________________ From: bioconductor-bounces@stat.math.ethz.ch [bioconductor- bounces@stat.math.ethz.ch] On Behalf Of Daren Tan [daren76@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 ? _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
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@wolfgang-huber-3550
Last seen 12 weeks ago
EMBL European Molecular Biology Laborat…
Daren Tan ha scritto: > 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 ? > > Hi Daren library("org.Hs.eg.db") genes <- c("TP53", "SOX4", "PTEN") entrez = toTable( revmap(org.Hs.egSYMBOL)[genes] ) go = toTable( org.Hs.egGO[entrez$gene_id] ) combined = merge(entrez, go) > combined gene_id symbol go_id Evidence Ontology 1 5728 PTEN GO:0000079 TAS BP 2 5728 PTEN GO:0001525 IEA BP 3 5728 PTEN GO:0006470 IDA BP . . . . 91 7157 TP53 GO:0019899 IPI MF 92 7157 TP53 GO:0046982 IPI MF 93 7157 TP53 GO:0047485 IPI MF Best wishes Wolfgang > sessionInfo() R version 2.9.0 Under development (unstable) (2008-11-27 r47025) x86_64-unknown-linux-gnu locale: LC_CTYPE=it_IT.UTF-8;LC_NUMERIC=C;LC_TIME=it_IT.UTF-8;LC_COLLATE=it_IT .UTF-8;LC_MONETARY=C;LC_MESSAGES=it_IT.UTF-8;LC_PAPER=it_IT.UTF-8;LC_N AME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=it_IT.UTF-8;LC_IDENTI FICATION=C attached base packages: [1] tools stats graphics grDevices datasets utils methods [8] base other attached packages: [1] org.Hs.eg.db_2.2.6 RSQLite_0.7-1 DBI_0.2-4 [4] AnnotationDbi_1.5.6 Biobase_2.3.3 fortunes_1.3-5 ---------------------------------------------------- Wolfgang Huber, EMBL-EBI, http://www.ebi.ac.uk/huber
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