Getting Started: a user’s guide to the

GO

TAMU GO Workshop17 May 2010

Introduction to GO

1. Annotation2. Bio-ontologies3. the Gene Ontology (GO)

a GO annotation example GO evidence codes literature biocuration & computation analysis ND vs no GO sources of GO

4. Using the GO

Genomic Annotation Genome annotation is the process of

attaching biological information to genomic sequences. It consists of two main steps:

1. identifying functional elements in the genome: “structural annotation”

2. attaching biological information to these elements: “functional annotation”

biologists often use the term “annotation” when they are referring only to structural annotation

CHICK_OLF6

DNA annotation

Protein annotation

Data from Ensembl Genome browser

TRAF 1, 2 and 3 TRAF 1 and 2

Structural annotation:

catenin

Functional annotation:

Structural & Functional Annotation

Structural Annotation: Open reading frames (ORFs) predicted during genome

assembly predicted ORFs require experimental confirmation the Sequence Ontology (SO) provides a structured controlled

vocabulary for sequence annotation

Functional Annotation: annotation of gene products = Gene Ontology (GO)

annotation initially, predicted ORFs have no functional literature and GO

annotation relies on computational methods (rapid) functional literature exists for many genes/proteins prior to

genome sequencing GO annotation does not rely on a completed genome

sequence!

1. Provides structural annotation for agriculturally important genomes

2. Provides functional annotation (GO)3. Provides tools for functional modeling4. Provides bioinformatics & modeling

support for research community

Avian Gene Nomenclature

1. Bio-ontologies

Bio-ontologies Bio-ontologies are used to capture biological

information in a way that can be read by both humans and computers. necessary for high-throughput “omics” datasets allows data sharing across databases

Objects in an ontology (eg. genes, cell types, tissue types, stages of development) are well defined.

The ontology shows how the objects relate to each other.

Bio-ontologies:http://www.obofoundry.org/

Ontologies

digital identifier(computers)

description(humans)

relationships between terms

2. The Gene Ontology

What is the Gene Ontology?

assign functions to gene products at different levels, depending on how much is known about a gene product is used for a diverse range of species structured to be queried at different levels, eg:

find all the chicken gene products in the genome that are involved in signal transduction

zoom in on all the receptor tyrosine kinases human readable GO function has a digital tag to allow computational analysis of large datasets

COMPUTATIONALLY AMENABLE ENCYCLOPEDIA OF GENE FUNCTIONS AND THEIR RELATIONSHIPS

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

changing”

GO annotation example

NDUFAB1 (UniProt P52505)Bovine NADH dehydrogenase (ubiquinone) 1, alpha/beta subcomplex, 1, 8kDa

Biological Process (BP or P)GO:0006633 fatty acid biosynthetic process TASGO:0006120 mitochondrial electron transport, NADH to ubiquinone TASGO:0008610 lipid biosynthetic process IEA

Cellular Component (CC or C)GO:0005759 mitochondrial matrix IDAGO:0005747 mitochondrial respiratory chain complex I IDAGO:0005739 mitochondrion IEA

NDUFAB1

Molecular Function (MF or F)GO:0005504 fatty acid binding IDAGO:0008137 NADH dehydrogenase (ubiquinone) activity TASGO:0016491 oxidoreductase activity TASGO:0000036 acyl carrier activity IEA

GO annotation example

NDUFAB1 (UniProt P52505)Bovine NADH dehydrogenase (ubiquinone) 1, alpha/beta subcomplex, 1, 8kDa

aspect or ontologyGO:ID (unique)

GO term nameGO evidence code

GO EVIDENCE CODESDirect Evidence CodesIDA - inferred from direct assayIEP - inferred from expression patternIGI - inferred from genetic interactionIMP - inferred from mutant phenotypeIPI - inferred from physical interaction

Indirect Evidence Codesinferred from literatureIGC - inferred from genomic contextTAS - traceable author statementNAS - non-traceable author statementIC - inferred by curatorinferred by sequence analysisRCA - inferred from reviewed computational analysisIS* - inferred from sequence*IEA - inferred from electronic annotation

OtherNR - not recorded (historical)ND - no biological data available

ISS - inferred from sequence or structural similarity ISA - inferred from sequence alignment ISO - inferred from sequence orthology ISM - inferred from sequence model

Guide to GO Evidence Codes http://www.geneontology.org/GO.evidence.shtml

GO Mapping Example

NDUFAB1

GO EVIDENCE CODESDirect Evidence CodesIDA - inferred from direct assayIEP - inferred from expression patternIGI - inferred from genetic interactionIMP - inferred from mutant phenotypeIPI - inferred from physical interaction

Indirect Evidence Codesinferred from literatureIGC - inferred from genomic contextTAS - traceable author statementNAS - non-traceable author statementIC - inferred by curatorinferred by sequence analysisRCA - inferred from reviewed computational analysisIS* - inferred from sequence*IEA - inferred from electronic annotation

OtherNR - not recorded (historical)ND - no biological data available

Biocuration of literature• detailed function • “depth”• slower (manual)

P05147

PMID: 2976880

Find a paperabout the protein.

Biocuration of Literature:detailed gene function

Read paper to get experimental evidence of function

Use most specific termpossible

experiment assayed kinase activity:use IDA evidence code

GO Mapping Example

NDUFAB1

GO EVIDENCE CODESDirect Evidence CodesIDA - inferred from direct assayIEP - inferred from expression patternIGI - inferred from genetic interactionIMP - inferred from mutant phenotypeIPI - inferred from physical interaction

Indirect Evidence Codesinferred from literatureIGC - inferred from genomic contextTAS - traceable author statementNAS - non-traceable author statementIC - inferred by curatorinferred by sequence analysisRCA - inferred from reviewed computational analysisIS* - inferred from sequence*IEA - inferred from electronic annotation

OtherNR - not recorded (historical)ND - no biological data available

ISS - inferred from sequence or structural similarity ISA - inferred from sequence alignment ISO - inferred from sequence orthology ISM - inferred from sequence model

Biocuration of literature• detailed function • “depth”• slower (manual)

Sequence analysis• rapid (computational)• “breadth” of coverage • less detailed

Unknown Function vs No GO ND – no data

Biocurators have tried to add GO but there is no functional data available

Previously: “process_unknown”, “function_unknown”, “component_unknown”

Now: “biological process”, “molecular function”, “cellular component”

No annotations (including no “ND”): biocurators have not annotated this is important for your dataset: what % has

GO?

1. Primary sources of GO: from the GO Consortium (GOC) & GOC members

most up to date most comprehensive

2. Secondary sources: other resources that use GO provided by GOC members

public databases (eg. NCBI, UniProtKB) genome browsers (eg. Ensembl) array vendors (eg. Affymetrix) GO expression analysis tools

Sources of GO

Different tools and databases display the GO annotations differently.

Since GO terms are continually changing and GO annotations are continually added, need to know when GO annotations were last updated.

Sources of GO annotation

EXAMPLES: public databases (eg. NCBI, UniProtKB) genome browsers (eg. Ensembl) array vendors (eg. Affymetrix)

CONSIDERATIONS: What is the original source? When was it last updated? Are evidence codes displayed?

Secondary Sources of GO annotation

For more information about GO

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

gene association file information: http://www.geneontology.org/GO.format.annotation.shtml

tools that use the GO: http://www.geneontology.org/GO.tools.shtml

GO Consortium wiki: http://wiki.geneontology.org/index.php/Main_Page

All websites are listed on the AgBase workshop website.

3. Using the GO

http://www.geneontology.org/

However…. many of these tools do not support non-model

organisms the tools have different computing requirements may be difficult to determine how up-to-date the

GO annotations are…

Need to evaluate tools for your system.

Some useful expression analysis tools:

Database for Annotation, Visualization and Integrated Discovery (DAVID)

http://david.abcc.ncifcrf.gov/ agriGO -- GO Analysis Toolkit and Database for

Agricultural Community http://bioinfo.cau.edu.cn/agriGO/ used to be EasyGO chicken, cow, pig, mouse, cereals, dicots includes Plant Ontology (PO) analysis

Onto-Express http://vortex.cs.wayne.edu/projects.htm#Onto-Express can provide your own gene association file

Funcassociate 2.0: The Gene Set Functionator http://llama.med.harvard.edu/funcassociate/ can provide your own gene association file

Evaluating GO toolsSome criteria for evaluating GO Tools:1. Does it include my species of interest (or do I have to

“humanize” my list)?2. What does it require to set up (computer usage/online)3. What was the source for the GO (primary or secondary)

and when was it last updated?4. Does it report the GO evidence codes (and is IEA

included)?5. Does it report which of my gene products has no GO?6. Does it report both over/under represented GO groups and

how does it evaluate this?7. Does it allow me to add my own GO annotations?8. Does it represent my results in a way that facilitates

discovery?

Functional Modeling Considerations

Should I add my own GO? use GOProfiler to see how much GO is available for your species use GORetriever to find existing GO for your dataset Does analysis tool allow me to add my own GO?

Should I do GO analysis and pathway analysis and network analysis? different functional modeling methods show different aspects about

your data (complementary) is this type of data available for your species (or a close ortholog)?

What tools should I use? which tools have data for your species of interest? what type of accessions are accepted? availability (commercial and freely available)

Protein/Gene identifiers

GORetriever

GO annotations

Genes/Proteins with no GO annotations

GOanna

Pathways and network analysis

GO Enrichment analysis

ArrayIDer

Microarray Ids

GOSlimViewer

Yellow boxes represent AgBase toolsGreen/Purple boxes are non-AgBase resources

Ingenuity Pathways Analysis (IPA)Pathway StudioCytoscapeDAVID

Ingenuity Pathways Analysis (IPA)Pathway StudioCytoscapeDAVIDEasyGOOnto-ExpressOnto-Express-to-go (OE2GO)

Overview of functional modeling strategy

All workshop materials are available at AgBase.