Introduction to the Gene Ontology and GO annotation resources

Rachael Huntley UniProt-GOA EBI Programmatic Access to Biological Databases 3 rd October 2012

What is an Ontology?

What is the difference between an ontology and a controlled vocabulary?

An

ontology

formally represents knowledge as a set of concepts within a domain, and the relationships among those concepts. It can be used to reason about the entities within that domain and may be used to describe the domain.

A

Controlled vocabularies

provide a way to organize knowledge for subsequent retrieval but does not allow reasoning about the entities.

Ontologies and CVs in Biology

Ontologies/CVs are heavily used in biological databases • Allow organisation of data within a database • Enable linking between databases • Enable searches across databases

www.ebi.ac.uk/ols

What is GO?

The Gene Ontology

• A way to capture biological knowledge for Less specific concepts individual gene products in a written and computable form • A set of concepts and their relationships to each other arranged as a hierarchy More specific concepts www.ebi.ac.uk/QuickGO

The Concepts in GO 1. Molecular Function

An elemental activity or task or job • • protein kinase activity insulin receptor activity

3. Cellular Component

Where a gene product is located

2. Biological Process

A commonly recognised series of events • cell division • mitochondrion • mitochondrial matrix • mitochondrial inner membrane

Anatomy of a GO term

Unique identifier Term name Synonyms Cross-references Definition

Ontology structure

• Directed acyclic graph Terms can have more than one parent • Terms are linked by relationships is_a part_of regulates (and +/- regulates) has_part occurs_in www.ebi.ac.uk/QuickGO These relationships allow for complex analysis of large datasets

Relations Between GO Terms

is_a If A is a B, then A is a subtype of B part_of Wherever B exists, it is as part of A. But not all A is part of B.

A B

All replication forks are part of a chromosome Not all chromosomes have replication forks

http://www.geneontology.org/GO.ontology-ext.relations.shtml

Relations Between GO Terms

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 All cell cycle checkpoints regulate the cell cycle.

The cell cycle is not solely regulated by cell cycle checkpoints http://www.geneontology.org/GO.ontology-ext.relations.shtml

B

Process-Function Links in GO

• GO was originally three completely independent hierarchies, with no relationships between them • Biological processes are ordered assemblies of molecular functions • As of 2009 we have started making relationships between biological process and molecular function in the live ontology functions that regulate processes e.g. transcription regulator regulates transcription

process process function

functions that are part_of processes e.g. transporter part_of transport

function

Searching for GO terms

Search GO terms or proteins

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

Exercise

Search for a GO term Exercise 1 (pg.16)

17

Why do we need GO?

Reasons for the Gene Ontology

• Inconsistency in English language

www.geneontology.org

Inconsistency in English languauge

•

Same

name for

different

concepts

Cell or ??

•

Different

names for the

same

concept

Eggplant Brinjal Aubergine Melongene Same for biological concepts



Comparison is difficult – in particular across species or across databases Just one reason why the Gene Ontology (GO) is is needed…

Reasons for the Gene Ontology

• Inconsistency in English language • Increasing amounts of biological data available • Increasing amounts of biological data to come

www.geneontology.org

Increasing amounts of biological data available

Search on ‘ DNA repair ’ ...

get over 68,000 results Expansion of sequence information

Reasons for the Gene Ontology

• Inconsistency in English language • Increasing amounts of biological data available • Increasing amounts of biological data to come • Large datasets need to be interpreted quickly

www.geneontology.org

Aims of the GO project

• Compile the ontologies - currently over 38,000 terms - constantly increasing and improving • Annotate gene products using ontology terms - around 30 groups provide annotations • Provide a public resource of data and tools - regular releases of annotations - tools for browsing/querying annotations and editing the ontology

http://www.geneontology.org

Reactome

GO Annotation

UniProt-Gene Ontology Annotation (UniProt-GOA) project at the EBI

• Largest open-source contributor of annotations to GO • Provides annotation for more than 350,000 species • Our priority is to annotate the human proteome

A GO annotation is …

…a statement that a gene product; 1.

or

has a particular molecular function is involved in a particular biological process

or

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

P00505 GOT2 GO:0004069 aspartate transaminase activity

Reference

PMID:2731362

Evidence code

IDA

UniProt-GOA incorporates annotations made using two methods

Electronic Annotation • • • Quick way of producing large numbers of annotations Annotations use less-specific GO terms Only source of annotation for many non-model organism species Manual Annotation • Time-consuming process producing lower numbers of annotations • Annotations tend to use very specific GO terms

Electronic annotation methods 1. Mapping of external concepts to GO terms

GO:0005634: Nucleus GO:0004707: MAP kinase activity GO:0009734: Auxin mediated signaling pathway

Electronic annotation methods 2. Automatic transfer of manual annotations to orthologs e.g. Human

Ensembl compara

Macaque Chimpanzee Guinea Pig Rat ...and more Dog Chicken Arabidopsis

Ensembl compara

…and more Poplar Brachypodium Maize Grape Rice

Annotations are high-quality and have an explanation of the method (GO_REF)

http://www.geneontology.org/cgi-bin/references.cgi

Manual annotation by GOA

High –quality, specific annotations made using: • Full text peer-reviewed papers • A range of evidence codes to categorise the types of evidence found in a paper e.g. IDA, IMP, IPI

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

Number of annotations in UniProt-GOA database Electronic annotations Manual annotations*

102,205,043 1,149,802

Sep 2012 Statistics

* Includes manual annotations integrated from external model organism and specialist groups

How to access and use GO annotation data

Where can you find annotations?

UniProtKB Ensembl Entrez gene

UniProt vs. QuickGO annotation display

UniProt QuickGO

UniProt vs. QuickGO annotation display Filtering mechanism

• Exclude root terms (e.g. Molecular Function) • Exclude annotations with qualifier (e.g. NOT, contributes_to) • Exclude annotations to less granular terms • Exclude annotations to GO:0005515 protein binding • Exclude lower quality assignments for same data, e.g. UniProt taken in preference to MGI • Add electronic annotations that cover ground not covered by manual annotation

Gene Association Files

17 column files containing all information for each annotation GO Consortium website UniProt-GOA website Numerous species-specific files http://www.ebi.ac.uk/GOA/downloads.html

GO browsers

The EBI's QuickGO browser

Search GO terms or proteins Find sets of GO annotations

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

Exercise

Find annotations to a protein Find annotations to a list of proteins Exercise 2 (pg.16) Exercise 1 and 2 (pg.22)

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…

Term enrichment

• Most popular type of GO analysis • Determines which GO terms are more often associated with a specified list of genes/proteins compared with a control list or rest of genome • Many tools available to do this analysis • User must decide which is best for their analysis

Selected Gene Tree:

Analysis of high-throughput genomic datasets

time

Defense response Immune response Response to stimulus Toll regulated genes JAK-STAT regulated genes Puparial adhesion Molting cycle Hemocyanin Amino acid catabolism Lipid metobolism MicroArray data analysis

attacked control

Peptidase activity Protein catabolism Immune response Immune response Toll regulated genes

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

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

– searches the GO Consortium database

BLAST2GO

– searches the NCBI database

Using the GO to provide a functional overview for a large dataset

• 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

The EBI's QuickGO browser

Search GO terms or proteins Find sets of GO annotations Map-up annotations with GO slims

www.ebi.ac.uk/QuickGO

Precautions when using GO annotations for analysis

• The Gene Ontology is always changing and GO annotations are continually being created - always use a current version of both - if publishing your analyses please report the versions/dates you used http://www.geneontology.org/GO.cite.shtml

• Recommended that ‘NOT’ annotations are removed before analysis - only ~3000 out of 57 million annotations are ‘NOT’ - can confuse the analysis

Precautions when using GO annotations for analysis

• Unannotated is

not

unknown - where there is no evidence in the literature for a process, function or location the gene product is annotated to the appropriate ontology ’s root node with an ‘ND’ evidence code (no biological data), thereby distinguishing between unannotated and unknown • Pay attention to under-represented GO terms - a strong under-representation of a pathway may mean that normal functioning of that pathway is necessary for the given condition

Exercise

Use the QuickGO web services to retrieve annotations Exercise 1 Pg.46

The UniProt-GOA group

Project leader: Curators: Software developer: Rachael Huntley Yasmin Alam-Faruque Prudence Mutowo Tony Sawford Team leaders: Rolf Apweiler Claire O ’ Donovan Email:

[email protected]

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

Members of; UniProtKB InterPro IntAct HAMAP Acknowledgements Ensembl Ensembl Genomes GO Consortium Funding National Human Genome Research Institute (NHGRI) British Heart Foundation EMBL