1. Using the Gene Ontology (GO) for analysis of expression data Jane Lomax EMBL-EBI
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2. What is the Gene Ontology?
Set of standard biological phrases (terms) which are applied to genes/proteins:
protein kinase
apoptosis
membrane
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3. What is the Gene Ontology?
Genes are linked, or associated, with GO terms by trained curators at genome databases
known as ‘gene associations’ or GO annotations
Some GO annotations created automatically
These GO phrases, or TERMS are linked to genes by expert curators at genome databses.
will talk about in more detail later
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4. genome and protein databases
GO annotations
genome and protein databases
gene -> GO term
associated genes
GO database
The individual genome and protein databases submit their genes and proteins annotated to GO terms to a central GO database.
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5. What is the Gene Ontology?
Allows biologists to make queries across large numbers of genes without researching each one individually
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6. Eisen, Michael B. et al. (1998) Proc. Natl. Acad. Sci
Eisen, Michael B. et al. (1998) Proc. Natl. Acad. Sci. USA 95,
Copyright ©1998 by the National Academy of Sciences

7. GO structure GO isn’t just a flat list of biological terms
terms are related within a hierarchy
Two arrangements for DNA replication
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8. GO structure
gene A
A gene (A) that is associated with a term ‘DNA replication’ is automatically annotated to all that terms parent terms.
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9. GO structure
This means genes can be grouped according to user-defined levels
Allows broad overview of gene set or genome
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10. How does GO work? GO is species independent
some terms, especially lower-level, detailed terms may be specific to a certain group
e.g. photosynthesis
But when collapsed up to the higher levels, terms are not dependent on species
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11. How does GO work?
What does the gene product do?
Where and does it act?
Why does it perform these activities?
What information might we want to capture about a gene product?
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12. GO structure GO terms divided into three parts: cellular component
molecular function
biological process
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13. Cellular Component
where a gene product acts
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14. Cellular Component
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15. Cellular Component
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16. Cellular Component
Enzyme complexes in the component ontology refer to places, not activities.
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17. glucose-6-phosphate isomerase activity
Molecular Function
activities or “jobs” of a gene product
glucose-6-phosphate isomerase activity
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18. insulin receptor activity
Molecular Function
insulin binding
insulin receptor activity
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19. drug transporter activity
Molecular Function
drug transporter activity
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20. Molecular Function A gene product may have several functions
Sets of functions make up a biological process.
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21. Biological Process a commonly recognized series of events
cell division
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22. Biological Process
transcription
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23. regulation of gluconeogenesis
Biological Process
regulation of gluconeogenesis
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24. Biological Process
limb development
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25. Biological Process
courtship behavior
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26. Ontology Structure Terms are linked by two relationships is-a 
part-of 
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27. mitochondrial chloroplast
Ontology Structure
cell
membrane chloroplast
mitochondrial chloroplast
membrane membrane
is-a
part-of
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28. Ontology Structure
Ontologies are structured as a hierarchical directed acyclic graph (DAG)
Terms can have more than one parent and zero, one or more children
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29. Ontology Structure cell membrane chloroplast mitochondrial chloroplast
Directed Acyclic Graph (DAG) - multiple parentage allowed
cell
membrane chloroplast
mitochondrial chloroplast
membrane membrane
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30. Anatomy of a GO term unique GO ID id: GO:0006094 name: gluconeogenesis
namespace: process
def: The formation of glucose from
noncarbohydrate precursors, such as
pyruvate, amino acids and glycerol. [
exact_synonym: glucose biosynthesis
xref_analog: MetaCyc:GLUCONEO-PWY
is_a: GO:
is_a: GO:
term name
ontology
definition
17800 terms in three ontologies
94% of terms defined
synonym
database ref
parentage
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31. GO terms Where do GO terms come from?
GO terms are added by editors at EBI and annotating databases
new terms are usually only added when they are asked for by annotators
GO editors work with experts to make major ontology developments
metabolism
pathogenesis
cell cycle
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32. GO stats over 23,000 GO terms: 13593 biological_process
1980 cellular_component
7700 molecular_function
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33. GO annotations
Where do the links between genes and GO terms come from?
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34. GO annotations Contributing databases:
Berkeley Drosophila Genome Project (BDGP)
dictyBase (Dictyostelium discoideum)
FlyBase (Drosophila melanogaster)
GeneDB (Schizosaccharomyces pombe, Plasmodium falciparum, Leishmania major and Trypanosoma brucei)
UniProt Knowledgebase (Swiss-Prot/TrEMBL/PIR-PSD) and InterPro databases
Gramene (grains, including rice, Oryza)
Mouse Genome Database (MGD) and Gene Expression Database (GXD) (Mus musculus)
Rat Genome Database (RGD) (Rattus norvegicus)
Reactome
Saccharomyces Genome Database (SGD) (Saccharomyces cerevisiae)
The Arabidopsis Information Resource (TAIR) (Arabidopsis thaliana)
The Institute for Genomic Research (TIGR): databases on several bacterial species
WormBase (Caenorhabditis elegans)
Zebrafish Information Network (ZFIN): (Danio rerio)
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35. Species coverage All major eukaryotic model organism species
Human via GOA group at UniProt
Several bacterial and parasite species through TIGR and GeneDB at Sanger
many more in pipeline
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36. Annotation coverage Add graph of % genome coverage from paper
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37. Anatomy of a GO annotation
Three key parts:
gene name/id
GO term(s)
evidence for association
And the evidence that links the GO term with the gene.
There are various types of evidence, it can be an experiment, an author statement in a paper, a Blast search or an algorithmic match - I’ll go into more detail about these later.
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38. Example annotation
Breast cancer type 1 susceptibility protein gene in humans
Open AmiGO
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39. Types of GO annotation:
 Electronic Annotation
 Manual Annotation
So there are two main types of GO annotation, those made electronically, and those made by curator.
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40. Manual annotation Created by scientific curators High quality
Small number
Manual annotations are made by curators at model organism databases
They’re consequently of a high quality, but because of the length of time it takes to make these annotations, there are a much smaller number of them than automatic annotations
But the number is increasing all the time. Some databases such as Saccaromyces and Drosophila have complete manual annotation of the whole genome, whereas some others have only a little.
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41. Manual annotation
In this study, we report the isolation and molecular characterization of the B. napus PERK1 cDNA, that is predicted to encode a novel receptor-like kinase. We have shown that like other plant RLKs, the kinase domain of PERK1 has serine/threonine kinase activity, In addition, the location of a PERK1-GTP fusion protein to the plasma membrane supports the prediction that PERK1 is an integral membrane protein…these kinases have been implicated in early stages of wound response…
This is an example of how a curator might approach a paper to find GO terms.
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42. Manual annotation
The GO browser AmiGO, which you’ll be using in the tutorial later, displays all of the manual GO annotations.
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43. Electronic Annotation
Annotation derived without human validation
mappings file e.g. interpro2go, ec2go.
Blast search ‘hits’
Lower ‘quality’ than manual codes
So electronic annotation is where a human hasn’t looked at an annotation, it’s been done entirely automatically.
This can be from a mappings file e.g. InterPro2go, spkw2go, from non-validated sequence similarity, or from a combination of different methods.
These electronic methods produce very large numbers of annotations, but because they are not individually validated by a curator, can be thought of as having a lower quality than curator approved annotations.
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44. Mappings files Fatty acid biosynthesis
( Swiss-Prot Keyword)
EC:
(EC number)
IPR000438: Acetyl-CoA carboxylase carboxyl transferase beta subunit
(InterPro entry)
GO:Fatty acid biosynthesis
(GO: )
GO:acetyl-CoA carboxylase activity
(GO: )
GO:acetyl-CoA carboxylase
activity
This is an example of how different mappings files, used to create electronic annotations,
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45. Evidence types ISS: Inferred from Sequence/structural Similarity
IDA: Inferred from Direct Assay
IPI: Inferred from Physical Interaction
IMP: Inferred from Mutant Phenotype
IGI: Inferred from Genetic Interaction
IEP: Inferred from Expression Pattern
TAS: Traceable Author Statement
NAS: Non-traceable Author Statement
IC: Inferred by Curator
ND: No Data available
These are the evidence types broken down - it looks very complicated and there’s no need to worry about this too much, but basically the top box are the manual evidence codes - mostly experimental techniques, and IEA is electronic annotation.
IEA: Inferred from electronic annotation
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46. GO tools
GO resources are freely available to anyone to use without restriction
Includes the ontologies, gene associations and tools developed by GO
Other groups have used GO to create tools for many purposes:
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47. GO tools
Affymetrix also provide a Gene Ontology Mining Tool as part of their NetAffx™ Analysis Center which returns GO terms for probe sets
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48. GO tools
Many tools exist that use GO to find common biological functions from a list of genes:
insert slides from sorin’s talk
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49. GO tools Most of these tools work in a similar way:
input a gene list and a subset of ‘interesting’ genes
tool shows which GO categories have most interesting genes associated with them i.e. which categories are ‘enriched’ for interesting genes
tool provides a statistical measure to determine whether enrichment is significant
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50. Microarray process Treat samples Collect mRNA Label Hybridize Scan
Normalize
Select differentially regulated genes
Understand the biological phenomena involved
So this is a typical process that you’d use to collect data from your microarray
Just like that - makes it look very easy!
So for a certain set of conditions, you have a set of differentially regualted genes and what you really want to know about is the underlying biological processes involved.
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51. Traditional analysis Gene 1 Apoptosis Cell-cell signaling
Protein phosphorylation
Mitosis …
Gene 2
Growth control
Oncogenesis
Gene 3
Growth control
Mitosis
Oncogenesis
Protein phosphorylation …
Gene 4
Nervous system
Pregnancy
Gene 100
Positive ctrl. of cell prolif
Glucose transport
Typically, this is the way the analysis would have been done.
Taking your differentially regualted genes, you’d analyse them one by one - researching the what is known about that gene, and what processes it is involved in.
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52. Traditional analysis gene by gene basis requires literature searching
time-consuming
So this gene by gene approach has the major disadvantage that you have to delve into the literature yourself, which is obviously very time consuming.
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53. Using GO annotations
But by using GO annotations, this work has already been done for you!
But by using GO annotations, this work has already been done for you - someone has already sat down and associated a particular gene with a particular process…
GO: : apoptosis
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54. Grouping by process Mitosis Gene 2 Gene 5 Gene45 Gene 7 Gene 35 …
Glucose transport
Gene 7
Gene 3
Gene 6 …
Apoptosis
Gene 1
Gene 53
Positive ctrl. of
cell prolif.
Gene 7
Gene 3
Gene 12 …
Growth
Gene 5
Gene 2
Gene 6 …
So you have the ability to group your differentially regulated genes by process…
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55. GO for microarray analysis
Annotations give ‘function’ label to genes
Ask meaningful questions of microarray data e.g.
genes involved in the same process, same/different expression patterns?
GO and it’s annotations are useful in microarrays mainly for comparing gene expression patterns to gene function, allowing more meaningful interpretation of microarray data.
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56. Using GO in practice statistical measure
how likely your differentially regulated genes fall into that category by chance
mitosis – 80/100
apoptosis – 40/100
p. ctrl. cell prol. – 30/100
glucose transp. – 20/100
The better ones include an statistical measure of how likely your differentially regulated genes fall into that category by chance
So why is that necessary
So imagine you do a microarray with a 1000 genes, and you find that 100 are differentially regualted
And these are the GO processes that those differentially regualted genes fall into - it looks like mitosis is overrepresented….
microarray
1000 genes
100 genes differentially regualted
experiment
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57. Using GO in practice
However, when you look at the distribution of all genes on the microarray:
Process Genes on array # genes expected in occurred random genes
mitosis /
apoptosis /
p. ctrl. cell prol / glucose transp /
you can see that 80% of them were involved in mitosis, so the number upregulated is what you’d expect by chance.
The category positive regulation of cell proliferation actually contains more differentially regualted genes than you would expect by chance
Need a statistical test e.g. Chi-squared to see if this overrepresentation or enrichment of a certain class is statistically significant.
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58. Enrichment tools
GO is developing its own enrichment tool as part of the GO browser AmiGO
Currently in testing phase, should be released next month
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59. Onto-Express walkthrough
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