Transcript Tools for functional annotation

Modeling Functional Genomics
Datasets
CVM8890-101
Lesson 3
13 June 2007
Fiona McCarthy
Lesson 3: Tools for functional
annotation. Accessing
functional data;
computational strategies to
obtain more complete
functional annotation; the
AgBase GO annotation
pipeline.
Lesson 3 Outline
1. Review: Functional Annotation
2. Tools for functional annotation
– Accessing functional data
– Computational strategies to obtain more functional
data
3. Example: The AgBase GO annotation pipeline
4. Other GO annotation tools
Review: Functional Annotation
• biologists refer to both the annotation of the
genome and functional annotation of gene
products:
“structural” AND “functional” annotation
• Functional annotation is required to make
biological sense of high throughput datasets eg.
genomics, arrays, proteomics
• COGs, KOGs, GO
Tools for Functional Annotation
• Need to be able to access functional annotation
for your dataset
– Breadth and depth
– Date updated
– No annotation vs function unknown
• Need to be able to add more annotation
• Need to be able to use the annotations to
model your data
– Depth or detail
– Compatibility with other programs (eg pathway
analysis)
– Comparative data?
Tools for Functional Annotation
•
•
•
•
•
•
•
Clusters of Orthologous Groups (COGs)
euKaryotic Orthologous Groups (KOGs)
UniProt Knowledgebase (UniProtKB)
Bioinformatic Harvester
FANTOM
Puma
Gene Ontology (GO)
COGs & KOGs
• Accessible at
http://www.ncbi.nlm.nih.gov/COG/
• ftp download
• Available for many prokaryotes and 7
eukaryotes
• Add more annotation using the KOGinator?
• Modeling:
– Has breadth but not always depth
– Good for prokaryote comparative analysis?
COGs & KOGs
COGs & KOGs
http://www.ncbi.nlm.nih.gov/COG/
Automated tools for large
numbers of comparisons??
UniProtKB
• Accessible at
http://www.pir.uniprot.org/
• ftp download & sophisticated search &
download capabilities
• Available for > 132,000 species
• Annotation across both literature (for
selected species) and biological databases
• Modeling:
– Has breadth but not always depth; many
proteins not represented in UniProtKB
– Those that are represented have a detailed
summary of function from a range of sources
– Rapid help and feedback from the database
help
UniProtKB
http://www.pir.uniprot.org/
UniProtKB
http://www.pir.uniprot.org/
UniProtKB
http://www.pir.uniprot.org/
Bioinformatic Harvester
• Accessible at
http://harvester.fzk.de/harvester/
• no download
• Available for 6 model species
• Integrates data from multiple sources
• Modeling:
– Has breadth and depth; not useful for large
datasets
– Updates?
Bioinformatic Harvester
http://harvester.fzk.de/harvester/
FANTOM
http://www.gsc.riken.go.jp/e/FANTOM/
Mouse only
PUMA
http://compbio.mcs.anl.gov/puma2/
Gene Ontology
• Accessible at
http://www.geneontology.org/
• updated downloads for 34 species + downloads
for UniProtKB species (>130,000)
• UniProtKB species annotation: some depth,
less breadth
• GO data mapped from other databases
• Modeling:
– Many tools available for modeling using the GO
– Can use computational or manual curation to add
annotations
Gene Ontology
http://www.geneontology.org/
Accessing GO Data
EBI-GOA Project
http://www.ebi.ac.uk/GOA/
The AgBase GO Annotation Pipeline
• Accessible at
http://www.agbase.msstate.edu/
• Access available annotations for agriculturally
important species
• Provide your own GO annotations
• Model GO for your dataset
Coming soon; GOModeler
quantitative hypothesis driven modeling using GO
Other GO Annotation Tools
http://www.geneontology.org/GO.tools.shtml
Other GO Annotation Tools
Evaluate:
• Can I run it from my computer?
• Does it include my species of interest?
• When was it last updated?
• Does it display evidence codes?
• Does it display IEA annotations?
• What are the inputs it accepts?
• Does it do batch searches?
Using GO to Analyze Array Data
Using GO to Analyze Array Data
Evaluate:
• Does it include my species of interest?
• When were the annotations last updated?
• Can I add my own annotations?
• Does it tell me how many of my genes are used for
the analysis?
• Does it account for “not” annotations?
• Does it display IEA annotations?
• What are the input IDS it accepts?
• Does it analyze both over & under-represented
terms?
• What statistics does it use for the analysis?
• Does it do a graphical representation?
ANY tool will only be as good as the annotations.