Transcript Document

Gene Ontology (GO)
http://www.geneontology.org/
The Gene Ontology (GO) project is a collaborative effort to address the need
for consistent descriptions of gene products in different databases. The GO
collaborators are developing three structured, controlled vocabularies
(ontologies) that describe gene products in terms of their associated
biological processes, cellular components and molecular functions in a
species-independent manner.
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Molecular Function
Biochemical activity or action of the gene product.
MF describes a capability that the gene product has and there is no
reference to where or when this activity or usage actually occurs.
Examples: enzyme transporter ligand
cytochrome c: electron transporter activity
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Biological process
A biological objective to which the gene product
contributes.
A biological process is accomplished via one or more
ordered assemblies of molecular functions.
There is generally some temporal aspect to the process
and it will often involve the transformation of some
physical thing.
Examples: cell growth and maintenance
cytochrome c oxidative phosphorylation, induction of cell
death
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Cellular Component
A component of a cell that is part of some larger object
or structure.
Examples: chromosome nucleus ribosome
cytochrome c: mitochondrial matrix, mitochondrial
inner membrane
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structure of GO terms
GO:0003673: Gene Ontology
GO:0008150: biological process
GO:0016265: death
GO:0019835:cytolysis
GO:0012501: programmed cell death
GO:0006915: apoptosis
GO:0009626: hypersensitive response
GO:0005575: cellular component
(S.G.Lee, 2004)
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GO parent-child relationship
“Child” terms are more specific than “parent” terms
“is-a” or “part-of”
A mitotic chromosome is a chromosome
A telemere is part-of a chromosome
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Induced Graph: Directed Acyclic Graphs (DAG)
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Some remarks
Genes to GO terms: many to many
GO itself has no reference to genes
GO specifies terminology and relationship between terms
GO’s real power comes from annotation of genes at different GO
terms
Each annotation is supported by certain evidence, e.g. TAS, IEP,
ISS
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Are there any GO terms over represented in our list?
Hypergeometric distribution
N balls, m white, N-m black
Draw k balls from N, X out of k balls are white
P(X=i) = iCm  (k-i)C(N-m) /kCN
k
P-value =
 (C
x
x i
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m  (k-x)C(N-m) /kCN)
148022 5928 4938 9168 10482 5893 90362 623 5187 5211 7480 9699 4826 64743 656 7320 4430 84679 3141 8310 10218 26009 3676
23072 9607 29927 51491 158 10059 6565 4687 57244 7748 10292 332 4089 7779 2277 5286 6721 6890 100 97 5786 1463 26206 6597
3883 10211 2805 11258 9842 5537 221395 10133 5582 4967 4111 8766 5598 9580 317 529 3762 9937 788 4236 6812 3199 10965
23515 9908 10552 1044 3991 4913 6599 79646 7784 9002 3192 596 1559 610 6564 11224 534 398 25939 7270 26 8639 55922 506
6612 4973 6712 11339 26109 7318 55653
library("GOstats")
library(hgu95av2)
GOHyperG(myLL, lib="hgu95av2", what="MF“)
$pvalues
GO:0015198 GO:0015197 GO:0015333 GO:0015295 GO:0005427 GO:0008122
1.486752e-06 2.899886e-05 1.320697e-04 1.320697e-04 1.320697e-04 3.932356e-04
$goCounts
GO:0015198 GO:0015197 GO:0015333 GO:0015295 GO:0005427 GO:0008122 GO:0008131
3
$numLL
[1] 6309
$numInt
[1] 73
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6
2
2
2
3
5
Gene: zinc finger protein 261 [Homo sapiens]
Chromosomal location Xq13.1”
PubMed, PMID “10486218”“9205841”“8817323”
Gene symbol “ZNF261”
GenBank accession # “X95808”
LocusLink, LocusID “9203”
Affymetrix identifier HGU95A chips “41046_s_at”
GO: "GO:0003677" "GO:0007275" "GO:0016021"
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Ensembl project
www.ensembl.org
Ensembl Gene ID
ENSMUSG00000000003.2
ENSMUSG00000000028.2
ENSMUSG00000059203.1
ENSMUSG00000000037.3
ENSMUSG00000000037.3
ENSMUSG00000000037.3
ENSMUSG00000053173.2
ENSMUSG00000000031.2
ENSMUSG00000048583.2
ENSMUSG00000048583.2
ENSMUSG00000056370.2
ENSMUSG00000000001.2
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EMBL ID
AF005204
AF081539
AF284437
AF285577
AF285577
AF285577
AK012580
BC025150
M24633
M24633
S78114
U38502
LocusLink ID
54192
12544
60367
107815
19899
16002
16002
20388
14679
GenMAPP: more biology
http://www.genmapp.org/
GenMAPP provides users with a tool for visualizing
gene expression data along pathways (called MAPPs),
creating new pathways and identifying global biological
associations within an expression dataset.
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