Transcript ontology annotation - European Bioinformatics Institute

Introduction to the Gene Ontology and GO
Annotation Resources
EBI Bioinformatics Roadshow
13th June 2012
Rotterdam, Netherlands
Duncan Legge
EBI is an Outstation of the European Molecular Biology Laboratory.
OUTLINE OF TUTORIAL:
PART I: Ontologies and the Gene
Ontology (GO)
PART II: GO Annotations
How to access GO annotations
How scientists use GO annotations
PART I: Gene Ontology
What does an ontology
provide?
1. Consistent terminology –
controlled vocabulary.
2. Relationships between
terms – hierarchy.
Controlled vocabulary
Q: What is a cell?
A: It really depends
who you ask!
Different things
can be described
by the same
name
The same thing can be described by different
names:
•
•
•
•
•
Glucose synthesis
Glucose biosynthesis
Glucose formation
Glucose anabolism
Gluconeogenesis
Inconsistency in naming of biological concepts
• Same name for different concepts
• Different names for the same concept
 Comparison is difficult – in particular across
species or across databases
Just one reason why the Gene Ontology (GO) is
is needed…
Why do we need GO?
• Inconsistency in naming of biological
concepts
• Large datasets need to be interpreted quickly
•Increasing amounts of biological data
available
• Increasing amounts of biological data to
come
Increasing amounts of biological data
available
Search on mesoderm
development…. you get
9441 results!
Expansion of sequence
information
1700s
1606
What is an ontology?
• Dictionary:
• A branch of metaphysics concerned with the nature and relations
of being (philosophy)
• A formal representation of the knowledge by a set of concepts
within a domain and the relationships between those concepts
(computer science)
• Barry Smith:
• The science of what is, of the kinds and structures of objects,
properties, events, processes and relations in every area of
reality.
What is an ontology?
• More usefully:
• An ontology is the representation of something we know about.
“Ontologies" consist of a representation of things, that are detectable or
directly observable, and the relationships between those things.
What’s in an Ontology?
What is the Gene Ontology (GO)?
A way to capture
biological knowledge in a
written and computable
form
Describes attributes of
gene products (RNA and
protein)
E. Coli
hub
http://www.geneontology.org
Reactome
The scope of GO
What information might we want to capture about a
gene product?
• What does the gene product do?
• Where does it act?
• How does it act?
Biological Process
what does a gene product do?
A commonly recognised series of events
transcription
cell division
Cellular Component
where is a gene product located?
• plasma
membrane
• mitochondrion
• mitochondrial membrane
• mitochondrial matrix
• mitochondrial lumen
• ribosome
• large ribosomal subunit
• small ribosomal subunit
Molecular Function
how does a gene product act?
•
•
insulin binding
•
insulin receptor activity
glucose-6-phosphate isomerase activity
Three separate ontologies or one large one?
• GO was originally three completely independent
hierarchies, with no relationships between them
• As of 2009, GO have started making relationships
between biological process and molecular function in the
live ontology
Process
Function
art of
Function
sa
• GO IS:
• species independent
• covers normal processes
• GO is NOT:
• NO pathological/disease processes
• NO experimental conditions
• NO evolutionary relationships
• NOT a nomenclature system
Aims of the GO project
• Edit the ontologies
• Annotate gene products using ontology terms
• Provide a public resource of data and tools
Anatomy of a GO term
Unique identifier
Term name
Definition
Synonyms
Crossreferences
Ontology structure
Less
specific node
• Nodes = terms in the ontology
node
More
specific node
edge
• Edges = relationships between
the concepts
node
•
GO is structured as a hierarchical directed acyclic graph
(DAG)
•
Terms can have more than one parent and zero, one or
more children
•
Terms are linked by reationships, which add to the
meaning of the term
Relationships between GO terms
• is_a
• part_of
• regulates
• positively regulates
• negatively regulates
• has_part
is_a
• If A is a B, then A is a subtype of B
• mitotic cell cycle is a cell cycle
• lyase activity is a catalytic activity.
• Transitive relationship: can infer up the graph
part_of
• Necessarily part of
• Wherever B exists, it is as part of A. But not all B is part of A.
A
• Transitive relationship (can infer up the graph)
B
regulates
• One process directly affects another process or quality
• Necessarily regulates: if both A and B are present, B always regulates
A, but A may not always be regulated by B
A
B
has_part
• Relationships are upside down compared to is_a and part_of
• Necessarily has part
GO and GO Annotation, EBI Bioinformatics Roadshow.
Düsseldorf. March 2011
is_a complete
• For all terms in the ontology, you have to be able to reach
the root through a complete path of is_a relationships:
• we call this being is_a complete
• important for reasoning over the ontology, and ontology development
True path rule
• Child terms inherit the meaning of all their parent terms.
How is GO maintained?
• GO editors and annotators work with experts to remodel specific
areas of the ontology
• Signaling
• Kidney development
• Transcription
• Pathogenesis
• Cell cycle
• Deal with requests from the community
• database curators, researchers, software developers
• Some simple requests can be dealt with automatically
• GO Consortium meetings for large changes
• Mailing lists, conference calls, content workshops
Requesting changes to the ontology
• Public Source Forge (SF) tracker for term related issues
https://sourceforge.net/projects/geneontology/
Why modify the GO?
• GO reflects current knowledge of biology
• Information from new organisms can make existing terms
and arrangements incorrect
• Not everything perfect from the outset
• Improving definitions
• Adding in synonyms and extra relationships
Searching for GO terms
http://www.ebi.ac.uk/QuickGO/
http://amigo.geneontology.org
… there are more browsers available on the GO Tools page:
http://www.geneontology.org/GO.tools.browsers.shtml
The latest OBO Gene Ontology file can be downloaded from:
http://www.geneontology.org/ontology/gene_ontology.obo
Exercise
Browsing the Gene Ontology using
QuickGO
• Exercise 1
15 mins
PART II: GO Annotation
A GO annotation is…
A statement that a gene product:
1.
has a particular molecular function
Or is involved in a particular biological process
Or is located within a certain cellular component
2.
as determined by a particular evidence
3.
as described in a particular reference
Accession
Name
GO ID
GO term name
Reference
Evidence Code
P00505
GOT2
GO:0004069
Aspartate transaminase
activity
PMID:2731362
IDA
Evidence codes
http://www.geneontology.org/GO.evidence.shtml
IDA: enzyme assay
IPI: e.g. Y2H
BLASTs, orthology
comparison, HMMs
subcategories of ISS
review papers
GO evidence code decision tree
GOA makes annotations using two methods
• Electronic

• Quick way of producing large numbers of annotations
• Annotations are less detailed
• Manual 
• Time-consuming process producing lower numbers of
annotations
• Annotations are very detailed and accurate
Electronic annotation by GOA
• 1. Mapping of external concepts to GO terms
•
InterPro2GO (protein domains)
•
SPKW2GO (UniProt/Swiss-Prot keywords)
•
HAMAP2GO (Microbial protein annotation)
•
EC2GO (Enzyme Commission numbers)
•
SPSL2GO (Swiss-Prot subcellular locations)
Electronic annotation by GOA
Aspartate transaminase activity ; GO:0004069
lipid transport; GO:0006869
Electronic annotation by GOA
• 2. Automatic transfer of annotations to orthologs
Manual annotation by GOA
• High-quality, specific annotations using:
• Peer-reviewed papers
• A range of evidence codes to categorize the types of evidence
found in a paper
www.ebi.ac.uk/GOA
Finding annotations in a paper
…for B. napus PERK1 protein (Q9ARH1)
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
serine/threonine kinase
, In addition, the
PERK1 has serine/threonine
kinaseactivity
activity,
location of a PERK1-GTP fusion protein to the plasma
membrane supports the prediction that PERK1 is an
integral membrane
integral
membraneprotein
protein…these kinases have been
implicated in early stages of wound
woundresponse…
response PubMed ID: 12374299
Function:
protein serine/threonine kinase activity
GO:0004674
Component:
integral to plasma membrane
GO:0005887
Process:
response to wounding
GO:0009611
Additional information
• Qualifiers
Modify the interpretation of an annotation
•
•
•
NOT (protein is not associated with the GO term)
colocalizes_with (protein associates with complex but is not a bona fide member)
contributes_to (describes action of a complex of proteins)
• 'With' column
Can include further information on the
method being referenced
e.g. the protein accession of an interacting protein
The NOT qualifier
• NOT is used to make an explicit note that the gene
product is not associated with the GO term
• Also used to document conflicting claims in the literature
• NOT can be used with ALL three gene ontologies
In these cells, SIPP1 was mainly present in the
nucleus, where it displayed a non-uniform,
speckled distribution and appeared to be
excludedfrom
from
the
nucleoli
excluded
the
nucleoli.
The colocalizes_with qualifier
Gene products that
are transiently
associated with an
organelle or complex
ONLY used with GO component ontology
The colocalizes_with qualifier
Example (from Schizosaccharomyces pombe):
Clp1 (Q9P7H1) relocalizes from the nucleolus to the
spindle and site of cell division; i.e. it is associated
transiently with the contractile ring (evidence from GFP
fusion).
The contributes_to qualifier
• Where an individual gene product that is part of a complex can
be annotated to terms that describe the action (function or
process) of the whole complex
• contributes_to is not needed to annotate a catalytic subunit.
ONLY used with GO function ontology
whether the
the protein
.. To test whether
proteincomplex
complex consisting of PIG-A,
has
GlcNAc transferase
transferase activity
PIG-H, PIG-C and hGPI1 has
GlcNAc
activity
in vitro….
…incubation of the radiolabeled donor of GlcNAc, UDP[6-3H]GlcNAc, with lysates of JY5 cells transfected with
resultedininsynthesis
synthesis of GlcNAc-PI
GST-tagged PIG-A resulted
GlcNAc-PIand
and
itssubsequent
subsequent
deacetylation
to glucosa- minyl
Its
deacetylation
to glucosa-minyl
phosphatidylinositol
(GlcN-PI)
phosphatidylinositol
(GlcN-PI)
WITH column
• The with column provides supporting evidence for ISS,
IPI, IGI and IC evidence codes
ISS: the accession of the aligned protein/ortholog
IPI: the accession of the interacting protein
IGI: the accession of the interacting gene
IC: The GO:ID for the inferred_from term
WITH
column
How to access
GO annotation data
Where can you find annotations?
UniProtKB
Ensembl
Entrez gene
Gene Association Downloads
• 17 column files containing all information for each annotation
GO Consortium website
GOA website
GO browsers
GO Slims
GO slims
• Many GO analysis tools use GO slims to give a broad
overview of the dataset
• GO slims are cut-down versions of the GO and contain a
subset of the terms in the whole GO
• GO slims usually contain less-specialised GO terms
Slimming the GO using the ‘true path rule’
Many gene products are associated with a
large number of descriptive, leaf GO nodes:
Slimming the GO using the ‘true path rule’
…however annotations can be mapped up
to a smaller set of parent GO terms:
GO slims
• Custom slims are available for download;
http://www.geneontology.org/GO.slims.shtml
• Or you can make your own using;
•
QuickGO
• http://www.ebi.ac.uk/QuickGO
•
AmiGO's GO slimmer
• http://amigo.geneontology.org/cgi-bin/amigo/slimmer
Just some things to be aware of….
•
The GO is continually changing
• New terms created
ontology
• Existing terms obsoleted
• Re-structured
annotation
• New annotations being created
•
ALWAYS use a current version of ontology and annotations
•
If publishing your analyses, please report the versions/dates you use:
http://www.geneontology.org/GO.cite.shtml
•
Differences in representation of GO terms may be due to biological
phenomenon. But also may be due to annotation-bias or experimental assays
•
Often better to remove the ‘NOT’ annotations before doing any large-scale
analysis, as they can skew the results
How scientists use the GO,
and the tools they use for analysis
Source of annotation
• If you wanted to find out the role of a gene product
manually, you’d have to read an awful lot of papers
• But by using GO annotations, this work has already been
done for you!
GO:0006915 : apoptosis
How scientists use the GO
• Find out what a gene product does or which genes are
involved in a certain biological process/function
• Analyse high-throughput genomic or proteomic datasets
• Validation of experimental techniques
• Get a broad overview of a proteome
• Obtain functional information for novel gene products
Some examples…
time
Defense response
Immune response
Response to stimulus
Toll regulated genes
JAK-STAT regulated genes
Puparial adhesion
Molting cycle
Hemocyanin
MicroArray data analysis
Amino acid catabolism
Lipid metobolism
Peptidase activity
Protein catabolism
Immune response
Immune response
Toll regulated genes
attacked control
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Bregje Wertheim at the Centre for Evolutionary Genomics,
Department of Biology, UCL and Eugene Schuster Group, EB
Validation of experimental techniques
Rat liver plasma membrane isolation
(Cao et al., Journal of Proteome Research 2006)
Analysis of high-throughput proteomic datasets
Characterisation of proteins interacting with ribosomal protein S19
(Orrù et al., Molecular and Cellular Proteomics 2007)
Obtain functional information for novel
gene products
MPYVSQSQHIDRVRGAIEGRLPAPGNSSRLVSSWQRSYEQYRLDPGSVIGPRVLTS
SELR DVQGKEEAFLRASGQCLARLHDMIRMADYCVMLTDAHGVTIDYRIDRDRRGD
FKHAGLYI GSCWSEREEGTCGIASVLTDLAPITVHKTDHFRAAFTTLTCSASPIFAPTG
ELIGVLDAS AVQSPDNRDSQRLVFQLVRQSAALIEDGYFLNQTAQHWMIFGHASRN
FVEAQPEVLIAFD ECGNIAASNRKAQECIAGLNGPRHVDEIFDTSAVHLHDVARTDTI
MPLRLRATGAVLYAR IRAPLKRVSRSACAVSPSHSGQGTHDAHNDTNLDAISRFLHS
RDSRIARNAEVALRIAGK HLPILILGETGVGKEVFAQALHASGARRAKPFVAVNCGAIP
DSLIESELFGYAPGAFTGA RSRGARGKIAQAHGGTLFLDEIGDMPLNLQTRLLRVLA
EGEVLPLGGDAPVRVDIDVICA THRDLARMVEEGTFREDLYYRLSGATLHMPPLRER
ADILDVVHAVFDEEAQSAGHVLTLD GRLAERLARFSWPGNIRQLRNVLRYACAVCDS
TRVELRHVSPDVAALLAPDEAALRPALA LENDERARIVDALTRHHWRPNAAAEALGM
InterProScan
Annotating novel sequences
• Can use BLAST queries to find similar sequences with
GO annotation which can be transferred to the new
sequence
• Two tools currently available;
• AmiGO BLAST (from GO Consortium)
http://amigo.geneontology.org/cgi-bin/amigo/blast.cgi
• searches the GO Consortium database
• BLAST2GO (from Babelomics)
http://www.blast2go.org/
• searches the NCBI database
AmiGO BLAST
Exportin-T from Pongo abelii (Sumatran
orangutan)
Numerous Third Party Tools
• Many tools exist that use GO to find common biological
functions from a list of genes:
http://www.geneontology.org/GO.tools.microarray.shtml
GO tools: enrichment analysis
• 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
Exercises
Searching for GO annotations in QuickGO
• Exercise 2: using GO terms
• Exercise 3: using a protein ID
Using QuickGO to create a tailored set of annotations
• Exercise 4: Filtering
• Exercise 5: Statistics
Map-up annotation using a GO slim
• Exercise 6
Thanks for listening
EBI is an Outstation of the European Molecular Biology Laboratory.