Introduction to the Gene Ontology and GO Annotation Resources

EBI is an Outstation of the European Molecular Biology Laboratory.

EBI Bioinformatics Roadshow15 March 2011

Düsseldorf, Germany

Rebecca Foulger

Introduction to the Gene Ontology and GO Annotation Resources

OUTLINE OF TUTORIAL:

PART I: Ontologies and the Gene Ontology (GO)

PART II: GO AnnotationsHow to access GO annotationsHow scientists use GO annotations

GO and GO Annotation, EBI Bioinformatics Roadshow. Düsseldorf. March 2011

PART I: Gene Ontology


What’s in a name...?


Q: What is a cell?

A: It really depends who you ask!


Different things can be described

by the same name


• Glucose synthesis• Glucose biosynthesis• Glucose formation• Glucose anabolism• Gluconeogenesis

The same thing can be described by different names:


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?

• Large datasets need to be interpreted quickly

• Inconsistency in naming of biological concepts

• 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


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.

1606 1700s



• 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.



• An ontology is more than just a list of terms (a controlled vocabulary)• A vocabulary of terms• Definitions for those terms• *** Defined logical relationships between the terms ***


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)


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 Processwhat does a gene product do?

cell divisiontranscription

A commonly recognised series of events


Cellular Componentwhere is a gene product located?

• plasma membran

e

• mitochondrion• mitochondrial membrane

• mitochondrial matrix• mitochondrial lumen • ribosome

• large ribosomal subunit • small ribosomal subunit


Molecular Functionhow 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


Function

Function

Process


• GO IS:• species independent• covers normal processes

• GO is NOT:• NO pathological/disease processes• NO experimental conditions• NO evolutionary relationships• NO gene products• NOT a nomenclature system


Aims of the GO project

• Compile the ontologies

• Annotate gene products using ontology terms

• Provide a public resource of data and tools


Anatomy of a GO termUnique identifier

Term name

DefinitionSynonyms

Cross-references


• 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 relationships, which add to the meaning of the term

node

nodenode

edge

Ontology structure

• Nodes = terms in the ontology

• Edges = relationships between the concepts


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.

• Transitive relationship (can infer up the graph)

BA


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

BA


• Relationships are upside down compared to is_a and part_of

• Necessarily has part

has_part


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/

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


• Terms become obsolete when they are removed or redefined

• GO IDs are never deleted

• For each term, a comment is added to explain why the term is now obsolete

• Alternative GO terms are suggested to replace an obsoleted term

Ensuring Stability in a Dynamic Ontology


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


http://www.ebi.ac.uk/QuickGO/

http://amigo.geneontology.org/

Exercise

Browsing the Gene Ontology using QuickGO

• Exercise 1


PART II: GO Annotation


http://www.geneontology.org

Reactome

E. Coli hub

A GO annotation is…

A statement that a gene product:

1. has a particular molecular functionOr is involved in a particular biological processOr is located within a certain cellular component

2. as determined by a particular method

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

IDA: enzyme assay

IPI: e.g. Y2H

http://www.geneontology.org/GO.evidence.shtml

review papers

subcategories of ISS

BLASTs, orthology comparison, HMMs

GO evidence code decision tree


Gene Ontology Annotation (GOA)

The GOA database at the EBI is:

• The largest open-source contributor of annotations to GO

• Member of the GO Consortium since 2001

• Provides annotation for 321,998 species (February 2011 release)

• GOA’s priority is to annotate the human proteome

• GOA is responsible for human, chicken and bovine annotations in the

GO consortium


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)

• 2. Automatic transfer of annotations to orthologs

Macaque Chimpanzee Guinea Pig Rat Mouse

Cow Dog Chicken

Ensembl compara


Mappings of concepts from UniProtKB files

Aspartate transaminase activity ; GO:0004069

lipid transport; GO:006869


Automatic transfer of annotations to orthologs

Mouse Rat Zebrafish Xenopus

Macaque Chimpanzee

Guinea Pig Rat Mouse

Dog Chicken

Human

Rat

Human

Mouse

Human

Xenopus

Tetraodon

Fugu

Zebrafish

Cow

Ensembl COMPARA

• Homologies between different species calculated

• GO terms projected from MANUAL annotation only (IDA, IEP, IGI, IMP, IPI)

• One-to-one orthologies used.

Currently provides 479,961 GO annotations for

60,515 proteins from 49 species (February 2011 release)

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

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…

Process: response to wounding GO:0009611

wound response

serine/threonine kinase activity,

Function: protein serine/threonine kinase activity GO:0004674

integral membrane protein

Component: integral to plasma membrane GO:0005887

…for B. napus PERK1 protein (Q9ARH1)

PubMed ID: 12374299


• Qualifier

sModify 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' columnCan include further information on the method being referenced

e.g. the protein accession of an interacting protein

Additional information


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 excluded from the nucleoli. excluded from the nucleoli


The colocalizes_with qualifier

ONLY used with GO component ontology

Gene products that are transiently or

peripherally associated with an

organelle or complex


Immunoblot analysis with anti-PSI polyclonal antibodies of U1 snRNP particles affinity purified from Drosophila embryonic nuclear extracts showed that PSI is physically associated with U1 snRNP (Figure 1A, top panel).

Association of U1 snRNP with GST-PSI was detected by ethidium bromide staining of the selected snRNAs and was confirmed by blot hybridization with an antisense U1 snRNA riboprobe (Figure 1C, lane 4).

PSI is physically associated with U1 snRNP

Association of U1 snRNP with GST-PSI


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. Furthermore, contributes_to may be used for any non-catalytic subunit, whether the subunit is essential for the activity of the complex or not

• Annotations to contributes_to often use the IC evidence code, but others may also be used.

ONLY used with GO function ontology


..we next examined whether a complex of four proteins can be formed…. As shown in Figure 4, FLAG-tagged PIG-C was precipitated efficiently with anti- FLAG beads in four combinations with other proteins (Figure 4A, lanes 1–4)….. These results strongly suggest that all four proteins form a complex.


.. To test whether the protein complex consisting of PIG-A, PIG-H, PIG-C and hGPI1 has GlcNAc transferase activity in vitro….

…incubation of the radiolabeled donor of GlcNAc, UDP-[6-3H]GlcNAc, with lysates of JY5 cells transfected with GST-tagged PIG-A resulted in synthesis of GlcNAc-PI and its subsequent deacetylation to glucosa- minyl phosphatidylinositol (GlcN-PI)

whether the protein complexhas GlcNAc transferase activity

resulted in synthesis of GlcNAc-PI andIts subsequent deacetylation to glucosa-minyl phosphatidylinositol (GlcN-PI)


Unknown v.s. Unannotated• When there is no existing data to support an

annotation, gene is annotated to the ROOT (top level) term

• NOT the same as having no annotation at all • No annotation means that no one has looked yet


WITH column

• The with column provides supporting evidence for ISS, IPI, IGI and IC evidence codes

ISS: the accession of the aligned protein/orthologIPI: the accession of the interacting proteinIGI: the accession of the interacting geneIC: 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 Files• 17 column files containing all information for each annotation

GO Consortium website

GOA website

GO browsers

QuickGO browser

Search GO terms or proteins

Find sets of GO annotations


Exercise

Searching for GO annotations in QuickGO

• Exercise 2• Exercise 3


Exercise

Using QuickGO to create a tailored set of annotations

• Exercise 4: Filtering• Exercise 5: Statistics


How scientists use the GO, and the tools they use for analysis


Using GO annotations

• 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

• Access gene product functional information

• 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…


Selec ted Gene Tree: pears on lw n3d ...Branc h c olor c las s ific ation: Set_ LW_ n3d_ 5p_ ...

Co lored by : Copy of Copy o f C5_ RMA (Defa...Gene L is t: all genes (14010)attacked

time

control

Puparial adhesionMolting cycleHemocyanin

Defense responseImmune response

Response to stimulusToll regulated genes

JAK-STAT regulated genes

Immune responseToll regulated genes

Amino acid catabolismLipid metobolism

Peptidase activityProtein catabolismImmune response

Selec ted Gene Tree: pears on lw n3d ...Branc h c o lor c la s s ification : Set_ LW_ n3d_ 5p_ ...

Colored by: Copy of Copy o f C5_ RMA (Defa...Gene L is t: a ll genes (14010)

Bregje Wertheim at the Centre for Evolutionary Genomics, Department of Biology, UCL and Eugene Schuster Group, EBI.

MicroArray data analysis


Validation of experimental techniques

(Cao et al., Journal of Proteome Research 2006)Rat liver plasma membrane isolation


Analysis of high-throughput proteomic datasets

(Orrù et al., Molecular and Cellular Proteomics 2007)Characterisation of proteins interacting with ribosomal protein S19


Obtain functional information for novel gene products

MPYVSQSQHIDRVRGAIEGRLPAPGNSSRLVSSWQRSYEQYRLDPGSVIGPRVLTSSELR DVQGKEEAFLRASGQCLARLHDMIRMADYCVMLTDAHGVTIDYRIDRDRRGDFKHAGLYI GSCWSEREEGTCGIASVLTDLAPITVHKTDHFRAAFTTLTCSASPIFAPTGELIGVLDAS AVQSPDNRDSQRLVFQLVRQSAALIEDGYFLNQTAQHWMIFGHASRNFVEAQPEVLIAFD ECGNIAASNRKAQECIAGLNGPRHVDEIFDTSAVHLHDVARTDTIMPLRLRATGAVLYAR IRAPLKRVSRSACAVSPSHSGQGTHDAHNDTNLDAISRFLHSRDSRIARNAEVALRIAGK HLPILILGETGVGKEVFAQALHASGARRAKPFVAVNCGAIPDSLIESELFGYAPGAFTGA RSRGARGKIAQAHGGTLFLDEIGDMPLNLQTRLLRVLAEGEVLPLGGDAPVRVDIDVICA THRDLARMVEEGTFREDLYYRLSGATLHMPPLRERADILDVVHAVFDEEAQSAGHVLTLD GRLAERLARFSWPGNIRQLRNVLRYACAVCDSTRVELRHVSPDVAALLAPDEAALRPALA 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


http://amigo.geneontology.org/cgi-bin/amigo/blast.cgi

http://www.blast2go.org/

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


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

• Try exercise 7 at home


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


http://www.ebi.ac.uk/QuickGO

Slimming with QuickGO

www.ebi.ac.uk/QuickGO

Map-up annotations with GO slims

Search GO terms or proteins

Find sets of GO annotations


Exercise

Map-up annotation using a GO slim

• Exercise 6


Just some things to be aware of….• The GO is continually changing

• New terms created• Existing terms obsoleted• Re-structured• 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

ontology

annotation


http://www.geneontology.org/GO.cite.shtml

EBI is an Outstation of the European Molecular Biology Laboratory.

Thank you

Introduction to the Gene Ontology and GO Annotation Resources

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