Emily Dimmer edimmer@ebi.ac.uk GOA group European Bioinformatics Institute

Emily Dimmeredimmer@ebi.ac.uk

GOA group

European Bioinformatics Institute

Wellcome Trust Genome Campus

Cambridge

Gene Ontology (GO)

• Introduction to GO

• Description of the GO ontologies

• How groups annotate to GO

• Practical:

• Investigating the GO and OBO web sites

• Browsing the GO using the AmiGO Browser.

• Open Biomedical Ontologies

• How GO is being used

• Available Tools

• GO slims

• Practical:

• Creating your own GO slim

GO Tutorial Outline:

• Practical:

• Available Tools

• GO slims

• Practical:

• Available Tools

• GO slims

• Practical:

• Available Tools

• GO slims

• Practical:

Why is GO needed ?THE PROBLEM:

• Huge body of knowledge with an extremely large vocabulary to describe it

• Vocabulary used is poorly defined – i.e. one word can have different meanings– or different names for the same concept

• Biological systems are complex and our knowledge of such systems is incomplete

RESULT: Large databases which are difficult to manage and

impossible to mine computationally

• A (part of the) solution:

“a controlled vocabulary that can be applied to all organisms even as knowledge of gene and protein roles in cells is accumulating and changing”

What is GO?

• Access gene product functional information

• Provide a link between biological knowledge and …

•gene expression profiles

• proteomics data

• Find how much of a proteome is involved in a process/ function/ component in the cell

• using a GO-Slim

(a slimmed down version of GO to summarize biological attributes of a proteome)

• Map GO terms and incorporate manual GOA annotation into own databases

• to enhance your dataset

• or to validate automated ways of deriving information about gene function (text-mining).

What can scientists do with GO?

TactitionTactile sense

Taction

perception of touch ; GO:0050975

Tactition Tactile senseTaction

•Molecular Function: elemental activity or taske.g. DNA binding, catalysis of a reaction

•Biological Process: broad objective or goale.g. mitosis, signal transduction, metabolism

•Cellular Component: location or complexe.g. nucleus, ribosome

GOThree (Orthogonal) Ontologies

How does GO work?

• Provides a standard, species-neutral way of representing biology

• GO covers ‘normal’ functions and processes– No pathological processes– No experimental conditions

Molecular Function 7,493 terms Biological Process 9,640 terms Cellular Component 1,634 terms

Total 18,767 terms

Definitions: 16,696 (93.9 %)

Content of GO

What is GO?• NOT a system of nomenclature or a list of gene

products

• GO doesn’t attempt to cover all aspects of biology or evolutionary relationships

Open Biomedical Ontologieshttp://obo.sourceforge.net

• NOT a dictated standard

• NOT a way to unify databases

http://www.geneontology.org

Reactome

Anatomy of a GO term

• GO terms are composed of:

• Term name• Unique GO ID• Definition (93 % of GO terms are

defined)• Synonyms (optional)• Database references (optional)• Relationships to other GO terms

Ontologies• “Ontologies provide controlled, consistent

vocabularies to describe concepts and relationships, thereby enabling knowledge sharing” (Gruber 1993)

I. The GO Ontologies

Can be used to:

• Formalise the representation of biological knowledge• Describe a common and defined vocabulary for

database annotation• Standardise database submissions• Provide unified access to information through ontology-

based querying of databases, both human and computational

• Improve management and integration of data within databases.

• Facilitate data mining

Ontology applications

• Ontologies can be represented as graphs, where the vertices (nodes and leaves) are connected by edges.

• The nodes are concepts in the ontology.

• The edges are the relationships between the concepts

nodenode

Ontology Structure

• The Gene Ontology is structured as a hierarchical directed acyclic graph (DAG).

• Terms are linked by two relationships– is-a– part-of

• Terms can have more than one parent

Simple hierarchies Directed Acyclic (Trees) Graphs

Directed Acyclic Graph

membrane chloroplast

mitochondrial chloroplastmembrane membrane

is-apart-of

True Path Rule

• The path from a child term all the way up to its top-level parent(s) must always be true

cell cytoplasm

chromosome nuclear chromosome

nucleus nuclear chromosome

part-of

• Terms become obsolete when they are removed or redefined

• GO IDs are never deleted

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

Ensuring Stability in a Dynamic Ontology

Obsolete Cellular ComponentObsolete Molecular FunctionObsolete Biological Process

Biological ProcessMolecular FunctionCellular Component

Access to the Gene Ontology• Downloads

• formats available:

OBO GO

XML OWL

(http://www.geneontology.org/GO.downloads)

• Web-based tools

• AmiGO (http://www.godatabase.org)

• QuickGO (http://www.ebi.ac.uk/ego)

II. Annotating to GO

Use of GO terms to represent the activities and localizations of gene products.

Basic information needed:

1. Database object (e.g. a protein or gene identifier)e.g. Q9ARH1

2. Reference IDe.g. PubMed ID: 12374299

3. GO term IDe.g. GO:0004674

4. Evidence codee.g. TAS

GenNav: http://etbsun2.nlm.nih.gov:8000/perl/gennav.pl

J. Clark et al. Plant Physiology 2005 (in press)

Two types of GO Annotation:

Electronic Annotation

Manual Annotation

All annotations must:

• be attributed to a source.

• indicate what evidence was found to support the GO term-gene/protein association.

• Provides large-coverage

• High-quality

• BUT annotations tend to use high-level GO terms and provide little detail.

1. Assignment of GO terms to gene products using existing information within database entries

• Manual mapping of GO terms to concepts external to GO (‘translation tables’).

• Proteins then electronically annotated with the relevant GO term(s).

2. Automatic sequence analyses to transfer annotations between highly similar gene products

Fatty acid biosynthesis ( Swiss-Prot Keyword)

EC:6.4.1.2 (EC number)

IPR000438: Acetyl-CoA carboxylase carboxyl transferase beta subunit (InterPro entry)

MF_00527: Putative 3-methyladenine DNA glycosylase(HAMAP)

GO:Fatty acid biosynthesis(GO:0006633)

GO:acetyl-CoA carboxylase activity

(GO:0003989)

GO:acetyl-CoA carboxylaseactivity

(GO:0003989)

GO:DNA repair (GO:0006281)

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

Mappings of external concepts to GO

Evaluation of precision of annotation electronic techniques (InterPro2GO,

SPKW2GO, EC2GO)

• Compared manually-curated test set of GO annotated proteins with the electronic annotations

• InterPro2GO = most coverage

• EC2GO = 67 % of predictions exactly match the manual GO annotation.

• 91-100 % of time the 3 mappings predicted GO terms within the same lineage

Camon et al. BMC Bioinformatics 2005 in press

Manual Annotation

• High–quality, specific gene/gene product associations made, using:

• Peer-reviewed papers

• Evidence codes to grade evidence

BUT – is very time consuming and requires trained biologists

Finding GO terms

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

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

wound response

GO Evidence Codes

*With column required

Manuallyannotated

Code Definition*IEA Inferred from Electronic Annotation

IDA Inferred from Direct Assay

IEP Inferred from Expression Pattern

*IGI Inferred from Genetic Interaction

IMP Inferred from Mutant Phenotype

*IPI Inferred from Physical Interaction

*ISS Inferred from Sequence Similarity

TAS Traceable Author Statement

NAS Non-traceable Author Statement

*IC Inferred from Curator

RCA Inferred from Reviewed Computational Analysis

ND No Data

IDA:•Enzyme assays

•In vitro reconstitution (transcription)

•Immunofluorescence

•Cell fractionation

TAS:•In the literature source the original experiments referred to are traceable (referenced).

GO Evidence Codes

*With column required

Manuallyannotated

• additional needed identifier for annotations using certain evidence codes

Code Definition*IEA Inferred from Electronic Annotation

IDA Inferred from Direct Assay

IEP Inferred from Expression Pattern

*IGI Inferred from Genetic Interaction

IMP Inferred from Mutant Phenotype

*IPI Inferred from Physical Interaction

*ISS Inferred from Sequence Similarity

TAS Traceable Author Statement

NAS Non-traceable Author Statement

*IC Inferred from Curator

RCA Inferred from Reviewed Computational Analysis

ND No Data

• a gene identifier for the "other" gene involved in the interaction

• a gene or protein identifier for the "other" protein involved in the interaction

• GO term from another annotation used as the basis of a curator inference

• Annotation of a gene product to one ontology is independent from its annotation to other ontologies.

• Terms reflecting a normal activity or location are only annotated to.

• Usage of ‘unknown’ GO terms

(e.g. Molecular function unknown GO:0005554)

…some extra things:

A set of ‘Qualifier’ terms is also available to curators modify the interpretation of an annotation.

Allowable values:

1. NOT• a gene product is not associated with the GO term • to document conflicting claims in the literature.

2. Contributes to• distinguishes between individual subunits functions and whole

complex functions• (used with GO Function Ontology)

3. Colocalizes with• Transiently or peripherally associated with an organelle or

complex • where the resolution of an assay is not accurate.

(used with GO Component Ontology)

…some extra things: Qualifier Information

• The Qualifier column can be used to modify the interpretation of an annotation.

Allowable values:

2. Contributes to• distinguishes between individual subunit functions and whole

Accessing annotations to the Gene Ontology

1. Downloads

• Annotations – gene association files

• Ontologies and annotations – MySQL and XML

2. Web-based access

…among others…

Gene Association File

Calcyclin IPI00027463 protein taxon:9606 20040426 UniProt Calcyclin IPI00027463 protein taxon:9606 20030721 UniProt Calcyclin IPI00027463 protein taxon:9606 20030721 UniProt

UniProt P06703 S106_HUMAN GO:0008083 GOA:spkw IEA FUniProt P06703 S106_HUMAN NOT GO:0007409 PMID:12152788 NAS PUniProt P06703 S106_HUMAN GO:0005515 PMID:12577318 IPI UniProt:P50995 F

• via web (GO consortium page)http://www.geneontology.org/GO.current.annotations.shtml

DB DB_Object_ID DB_Object_Symbol Qualifier GOid DB:Reference Evidence With Aspect

DB_Object_Name DB_Object_Synonym DB_Object_Type taxon Date Assigned by

http://www.geneontology.org/GO.current.annotations.shtml

Summary

• GO is still being developed and updated - it requires a serious and ongoing effort.– the biological community is involved

• New model organism databases are joining the GO Consortium annotation effort

Practical session

1. Visit the GO website

2. Visit the OBO website

3. Browse the ontologies using the official GO Consortium Browser – AmiGO

GO web site: www.geneontology.orgPart 1.

OBO web site: http://obo.sourceforge.net

AmiGO: http://www.godatabase.org

GO terms with no children

Filter queries by organism, data source or evidence

Search for GO terms or by Gene symbol/name

Querying the GO

GOst tool

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

OBO and Gene Ontology Uses and

AnatomyPhysiology

Phenotype

Pathway

Disease

Molecular

MetabolicDevelopmental

Ontologies

Beyond GO – Open Biomedical Ontologies

• Orthogonal to existing ontologies to facilitate combinatorial approaches

- Share unique identifier space- Include definitions

• Anatomies• Cell Types• Sequence Attributes• Temporal Attributes• Phenotypes• Diseases• More….

http://obo.sourceforge.net

Sequence Ontology

http://song.sourceforge.net

• Ontology of ‘small molecular entities’

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

http://www.fruitfly.org/cgi-bin/ex/go.cgi

Access to GO and its annotations

How to access the Gene ontology and its annotations

1. Downloads

• Ontologies – (various – GO, OBO, XML, OWL MySQL)

• Annotations – gene association files

• Ontologies and Annotations – MySQL and XML

2. Web-based access

among others…

http://www.ncbi.nlm.nih.gov/entrez

www.uniprot.org/

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

SRS view…

http://srs.ebi.ac.uk

www.ensembl.org/ www.ensembl.org/

www.ensembl.org/

• Access gene product functional information

• Provide a link between biological knowledge and …

•gene expression profiles

• proteomics data

• Find how much of a proteome is involved in a process/ function/ component in the cell

• using a GO-Slim

(a slimmed down version of GO to summarize biological attributes of a proteome)

• Map GO terms and incorporate manual GOA annotation into own databases

• to enhance your dataset

• or to validate automated ways of deriving information about gene function (text-mining).

What can scientists do with GO?

Selected Gene Tree: pearson lw n3d ...Branch color classification:Set_LW_n3d_5p_...

Colored by: Copy of Copy of C5_RMA (Defa...Gene List: all genes (14010)

attacked

control

Puparial adhesionMolting cyclehemocyanin

Defense responseImmune responseResponse to stimulusToll regulated genesJAK-STAT regulated genes

Immune responseToll regulated genes

Amino acid catabolismLipid metobolism

Peptidase activityProtein catabloismImmune response

Selected Gene Tree: pearson lw n3d ...Branch color classification:Set_LW_n3d_5p_...

Colored by: Copy of Copy of C5_RMA (Defa...Gene List: all genes (14010)

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

…analysis of high-throughput data according to GOMicroArray data analysis

Proteomics data analysis

Kislinger T et al, Mol Cell Proteomics, 2003

GO classification

…analysis of high-throughput data according to GO

http://www.geneontology.org/GO.tools

Analysis of Data: Clustering

Color indicates up/down regulation

GoMiner Tool, John Weinstein et al, Genome Biol. 4 (R28) 2003

Compare annotations associated with the test set to the entire set of GO annotations….

DNA Repair seems to be a common theme.

Example of VLAD Output

…overview proteome with GO Slim

http://www.ebi.ac.uk/integr8

http://go.princeton.edu/cgi-bin/GOTermMapper

map2slim.pl

• distributed as part of the go-perl package

• maps a set of annotations up to their parent GO slim terms

Off-the-shelf GO slims

Summary

The Gene Ontology project precipitated a generalized implementation for ontologies for molecular biology

Bio-ontologies such as GO have facilitated development of systems for hypothesis generation in biological systems

Further integration – creation of cross-products between different ontologies

Practical II – Creation of GO slims using the DAG-Edit tool.

http://sourceforge.net/projects/geneontology/

…loading the GO

ftp://ftp.geneontology.org/pub/go/ontology/gene_ontology.obo

…loading the GO

…browsing the GO

…viewing GO terms

…searching for GO terms

…creating a new GO slim

…creating a renderer for the GO slim

…adding terms to the GO slim

…filtering GO for terms in the GO slim

…removing filters/renderers

…saving the newly created GO slim

Emily Dimmer edimmer@ebi.ac.uk GOA group European Bioinformatics Institute

open biomedical ontologies

process function component

knowledge of gene

slimgo tutorial outline

obo web sites

available tools

amigo browser

reactionbiological process

Documents

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