Getting Started: a user’s guide to the GO TAMU GO Workshop 17 May 2010.
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Transcript of Getting Started: a user’s guide to the GO TAMU GO Workshop 17 May 2010.
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.