COST Functional Modeling Workshop
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Transcript COST Functional Modeling Workshop
Introduction to the Gene
Ontology: A User’s Guide
COST Functional Modeling Workshop
22-24 April, Helsinki
Introduction to GO
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•
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The Gene Ontology Consortium
The Gene ontology
A GO annotation example
GO evidence codes
• no GO vs ND
• Making Annotations
• Multiple annotations - the gene association (ga) file
• Sources of GO
THE GENE ONTOLOGY CONSORTIUM
http://www.geneontology.org/
The GO Consortium provides:
• central repository for ontology updates and annotations
• central mechanism for changing GO terms (adding, editing,
deleting)
• quality checking for annotations
• consistency checks for how annotations are made by different
groups
• central source of information for users
• co-ordination of annotation effort
GO Consortium and GO Groups:
• groups decide gene product set to annotate
• biocurator training
• tool development mostly by groups
• many non-consortium groups
• education and training by groups
• outreach to biocurators/databases by GOC
Annotation Strategy
• Experimental data
• many species have a body of published, experimental
data
• Detailed, species-specific annotation: ‘depth’
• Requires manual annotation of literature - slow
• Computational analysis
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Can be automated - faster
Gives ‘breadth’ of coverage across the genome
Annotations are general
Relatively few annotation pipelines
Releasing GO Annotations
GO annotations are stored at individual databases
Sanity checks as data is entered – is all the data
required filled in?
Databases do quality control (QC) checks and
submit to GO
GO Consortium runs additional QC and collates
annotations
Checked annotations are picked up by GO users
eg. public databases, genome browsers, array vendors,
GO expression analysis tools
AgBase Quality Checks & Releases
AgBase
Biocurators
‘sanity’ check
AgBase
biocuration
interface
‘sanity’
check
& GOC
QC
AgBase
database
‘sanity’ check
EBI GOA
Project
‘sanity’ check: checks
to ensure all appropriate
information is captured,
no obsolete GO:IDs are
used, etc.
GO analysis tools
Microarray developers
‘sanity’ check
& GOC QC
GO Consortium
database
UniProt db
QuickGO browser
GO analysis tools
Microarray developers
Public databases
AmiGO browser
GO analysis tools
Microarray
developers
THE GENE ONTOLOGY
Gene Ontology (GO)
• Not about genes!
• Gene products: genes, transcripts,
ncRNA, proteins
• The GO describes gene product
function
• Not a single ontology
• Biological Process (BP or P)
• Molecular Function (MF or F)
• Cellular Component (CC or C)
• de facto method for functional
annotation
• Widely used for functional genomics
(high throughput).
What the GO doesn’t do:
• Does not describe individual gene products
• e.g. cytochrome c is not in the GO but oxidoreductase activity is
• Does not describe mutants or diseases, e.g. oncogenesis.
• Does not include sequence attributes, e.g., exons, introns,
protein domains.
• Is not a database of sequences.
What is the Gene Ontology?
“a controlled vocabulary that can be applied to all organisms even as
knowledge of gene and protein roles in cells is accumulating and
changing”
• 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
relationships
between terms
Ontologies
digital identifier
(computers)
description
(humans)
A GO ANNOTATION EXAMPLE
A 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 TAS
GO:0006120 mitochondrial electron transport, NADH to ubiquinone TAS
GO:0008610 lipid biosynthetic process IEA
NDUFAB1
GO:0005504
GO:0008137
GO:0016491
GO:0000036
Molecular Function (MF or F)
fatty acid binding IDA
NADH dehydrogenase (ubiquinone) activity TAS
oxidoreductase activity TAS
acyl carrier activity IEA
Cellular Component (CC or C)
GO:0005759 mitochondrial matrix IDA
GO:0005747 mitochondrial respiratory chain complex I IDA
GO:0005739 mitochondrion IEA
A GO Annotation Example
NDUFAB1 (UniProt P52505)
Bovine NADH dehydrogenase (ubiquinone) 1, alpha/beta subcomplex, 1, 8kDa
GO:ID (unique)
aspect or ontology
GO evidence code
GO term name
GO EVIDENCE CODES
& MAKING ANNOTATIONS
Why record GO evidence code?
• GO did not initially record evidence for functional
assertion:
• NR: Not Recorded
• “inferred from…”
• deduce or conclude (information) from evidence
and reasoning
• provides information about the support for
associating a gene product with a function
• different experiments allow us to draw different
conclusions
• reliability
Types of GO Evidence Codes
1.
2.
3.
4.
5.
6.
Experimental Evidence Codes
Computational Analysis Evidence Codes
Author Statement Evidence Codes
Curator Statement Evidence Codes
Automatically-assigned Evidence Codes
Obsolete Evidence Codes
GO EVIDENCE CODES
Direct Evidence Codes
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
Guide to GO
Evidence Codes
http://www.gene
ontology.org/GO.e
vidence.shtml
Indirect Evidence Codes
inferred from literature
IGC - inferred from genomic context
TAS - traceable author statement
NAS - non-traceable author statement
IC - inferred by curator
inferred by sequence analysis
RCA - inferred from reviewed computational analysis
IS* - inferred from sequence*
IEA - inferred from electronic annotation
Other
NR - 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
GO EVIDENCE CODES
Direct Evidence Codes
GO
Mapping
IDA
- inferred
fromExample
direct assay
IEP - inferred from expression pattern
IGI - inferred from genetic interaction
IMP - inferred from mutant phenotype
IPI - inferred from physical interaction
Indirect Evidence Codes
inferred from literature
IGC - inferred from genomic context
TAS - traceable author statement
NAS - non-traceable author statement
IC - inferred by curator
inferred by sequence analysis
RCANDUFAB1
- inferred from reviewed computational analysis
IS* - inferred from sequence*
IEA - inferred from electronic annotation
Other
NR - not recorded (historical)
ND - no biological data available
Biocuration of literature
• detailed function
• “depth”
• slower (manual)
P05147
Biocuration of Literature:
detailed gene function
Find a paper
about the protein.
PMID: 2976880
Read paper to get experimental evidence of
function
Use most specific term
possible
experiment assayed kinase activity:
use IDA evidence code
GO EVIDENCE CODES
Direct Evidence Codes
GO
Mapping
IDA
- inferred
fromExample
direct assay
IEP - inferred from expression pattern
IGI - inferred from genetic interaction
IMP - inferred from mutant phenotype
IPI - inferred from physical interaction
Biocuration of literature
• detailed function
• “depth”
• slower (manual)
Indirect Evidence Codes
inferred from literature
IGC - inferred from genomic context
TAS - traceable author statement
NAS - non-traceable author statement
IC - inferred by curator
inferred by sequence analysis
RCANDUFAB1
- inferred from reviewed computational analysis
IS* - inferred from sequence*
IEA - inferred from electronic annotation
Other
NR - not recorded (historical)
ND - no biological data available
Sequence analysis
• rapid (computational)
• “breadth” of coverage
• less detailed
ISS - inferred from sequence or structural similarity
ISA - inferred from sequence alignment
ISO - inferred from sequence orthology
ISM - inferred from sequence model
Computational Analysis Evidence
In the beginning:
• IGC: Inferred from Genomic Context
• e.g. operons
• RCA: inferred from Reviewed Computational Analysis
• computational analyses that integrate datasets of
several types
• ISS: Inferred from Sequence or Structural Similarity
Computational Analysis Evidence
• Then different types of sequence analysis added:
ISS: Inferred from Sequence or Structural Similarity
• ISO: Inferred from Sequence Orthology
• ISA: Inferred from Sequence Alignment
• ISM: Inferred from Sequence Model
Computational Analysis Evidence
• Phylogenetic analysis codes added:
• IBA: Inferred from Biological aspect of Ancestor
• IBD: Inferred from Biological aspect of Descendant
• IKR: Inferred from Key Residues
• characterized by the loss of key sequence residues - implies a NOT
annotation
• IRD: Inferred from Rapid Divergence
• characterized by rapid divergence from ancestral sequence –
implies a NOT annotation
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?
MULTIPLE ANNOTATIONS: GENE
ASSOCIATION FILES
The gene association (ga) file
• standard file format used to capture GO annotation
data
• tab-delimited file containing 17* fields of
information:
• Information about the gene product (database, accession,
name, symbol, synonyms, species)
• information about the function:
• GO ID, ontology, reference, evidence, qualifiers, context
(with/from)
• data about the functional annotation
• date, annotator
* GO Annotation File Format 2.0 has two additional
columns compared to GAF 1.0: annotation extension
(column 16) and gene product form ID (column 17).
http://www.geneontology.org/GO.format.gaf-2_0.shtml
(additional column
added to this
example)
gene product information
metadata: when & who
function information
Used to give more specific information
about the evidence code
(not always displayed)
Used to qualify the annotation
(not always displayed)
Gene association files
• GO Consortium ga files
• many organism specific files
• also includes EBI GOA files
• EBI GOA ga files
• UniProt file contains GO annotation for all species
represented in UniProtKB
• AgBase ga files
• organism specific files
• AgBase GOC file – submitted to GO Consortium &
EBI GOA
• AgBase Community file – GO annotations not yet
submitted or not supported / annotations provided
by researchers
• all files are quality checked
http://www.geneontology.org
http://www.ebi.ac.uk/GOA/
http://www.agbase.msstate.edu/
Sources of 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 annotation
• 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.
Secondary 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?
Differences in displaying GO annotations:
secondary/tertiary sources.
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.