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 ?
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]]
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 ?
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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