Chibucos annot go_final

Marcus C. Chibucos, Ph.D.
Ontology
Evidence
Annotation
Arabidopsis thaliana ATPase
HMA4 zinc binding domain
GO:0006829 : zinc ion transport (BP)
GO:0005886 : plasma membrane (CC)
GO:0005515 : protein binding (MF)
Gene Annotation And Ontology

Outline of this talk
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Background: the language of biology
Gene Ontology: overview, terms & structure
Annotating with GO and Evidence
Using annotation to facilitate your research

About screenshots in this talk
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AmiGO web-based ontology browser
http://amigo.geneontology.org
OBO-Edit stand-alone editor
http://oboedit.org

What is annotation? Who is involved?
Term confusion (what’s in a name?)
Scale: the sea of data
Controlled vocabularies & ontologies
The Gene Ontology Consortium
Background: the language of biology
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Annotation
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annotate – to make or furnish critical or explanatory notes or comment.
(Merriam-Webster dictionary)
genome annotation – the process of taking the raw DNA sequence produced by the genome-sequencing projects and adding the layers of analysis and interpretation necessary to extract its biological significance and place it into the context of our understanding of biological processes.
(Lincoln Stein, PMID 11433356)
Gene Ontology annotation – the process of assigning GO terms to gene products… according to two general principles: first, annotations should be attributed to a source; second, each annotation should indicate the evidence on which it is based.
(http://www.geneontology.org)

Diverse parties involved
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End-users, including various researchers
Small-scale laboratory projects
Whole genome sequencing projects
Annotators
From reading papers to computational analysis
Ontology developers
Create terms that reflect scientific knowledge
Make interoperable ontologies, database links
Developers of tools & resources
Standards for storing & sharing data
Web interfaces for data analysis & sharing
Many areas of expertise
Laboratory sciences – biology, chemistry, medicine, and many other disciplines
Computational science – bioinformatics, genomics, statistics
Software development & web design
Philosophy – ontology & logic

Term confusion: synonyms
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Do biologists use precise & consistent language?
Mutually understood concepts – DNA, RNA, or protein
Synonym (one thing known by more than one name) – translation and protein synthesis
Enzyme Commission reactions
Standardized id, official name & alternative names
http://www.expasy.ch/enzyme/2.7.1.40

Term confusion: homonyms
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Homonyms common in biology – different things known by the same name
Sporulation
Vascular (plant vasculature, i.e. xylem & phloem, or vascular smooth muscle, i.e. blood vessels?)
Endospore formation Bacillus anthracis
“Sporulation”
Reproductive sporulation
Asci & ascospores, Morchellaelata(morel)
http://www.microbelibrary.org/ASMOnly/details.asp?id=1426&Lang=
©L Stauffer 2003 (accessed 17-Sep-09)
http://en.wikipedia.org/wiki/File:Morelasci.jpg
©PG Warner 2008 (accessed 17-Sep-09)

Term confusion: homonyms and biological complexity
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AmiGO query “vascular”  51 terms
In biology, many related phenomena are described with similar terminology

The problem of scale
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Small data sets, small experiments & isolated scientific communities?
Enormous data sets
Microarray experiments
Whole genome sequencing projects
Comparative genomics of multiple diverse taxa
Computers don’t understand nuance
Millions of proteins to annotate
How to effectively search?
How to draw meaningful comparisons?
http://en.wikipedia.org/wiki/File:Microarray2.gif
(accessed 17-Sep-09)

The Gene Ontology (GO)
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Way to address the problems of synonyms, homonyms, biological complexity, increasing glut of data
GO provides a common biological language for protein functional annotation
www.geneontology.org

Controlled vocabulary (CV)
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An official list of precisely defined terms that can be used to classify information and facilitate its retrieval
Think of flat list like a thesaurus or catalog
Benefits of CVs
Allow standardized descriptions of things
Remedy synonym & homonym issues
Can be cross-referenced externally
Facilitate electronic searching
A CV can be “…used to index and retrieve a body of literature in a bibliographic, factual, or other database. An example is the MeSH controlled vocabulary used in MEDLINE and other MEDLARS databases of the NLM.”
http://www.nlm.nih.gov/nichsr/hta101/ta101014.html

Ontology is a type of CV with defined relationships
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Ontology – formalizes knowledge of a subject with precise textual definitions
Networked terms where child more specific (“granular”) than parent
Less specific
GO terms describe biological attributes of gene products…
More granular

How GO works
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GO Consortium develops & maintains:
Ontologies and cross-links between ontologies and different resources
Tools to develop and use the ontologies
SourceForge tracker for development
People studying organisms at databases annotate gene products with GO terms
Groups share files of annotation data about their respective organisms
Because a common language was used to describe gene products and this information was shared amongst databases…
We can search uniformly across databases
Do comparative genomics of diverse taxa

GO on SourceForgesourceforge.net/projects/geneontology
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The Gene Ontology Consortium
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Collaboration began 1998 among model organism databases mouse (MGI), fruit fly (FlyBase) and baker’s yeast (SGD)
Michael Ashburner of FlyBase contributed the base vocabulary
Today > 20 members & associates
First publication 2000 (PMID 10802651)
Today, PubMed query “gene ontology” yields 3,347 papers (27-Jun-2011)
Organisms represented by GO annotations from every kingdom of life
Many groups use GO in many different ways for their research
Among eight OBO-Foundry ontologies
ZFIN
Reactome
IGS

OBO Foundry ontologieswww.obofoundry.org
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Collaboration among developers of science-based ontologies
Establish principles for ontology development
Goal of creating a suite of orthogonal interoperable reference ontologies in the biomedical domain.
many others…

What the GO is not
GO comprises three ontologies
Anatomy & storage of GO terms
Ontology structure
Detail of a term in AmiGO
True path rule
Gene Ontology:overview, terms & structure
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Caveats – what GO is not
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Not gene naming system or gene catalog
GO describes attributes of biological objects – “oxidoreductase activity” not “cytochromec”
The three ontologies have limitations
No sequence attributes or structural features
No characteristics unique to mutants or disease
No environment, evolution or expression
No anatomy features above cellular component
Not dictated standard or federated solution
Databases share annotations as they see fit
Curators evaluate differently
GO is evolving as our knowledge evolves
New terms added on daily basis
Incorrect/poorly defined terms made obsolete
Secondary ids – terms with same meaning merged

GO comprises three ontologies
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Cellular component ontology (CC)
“cytoplasm”
Molecular function ontology (MF)
“protein binding”
“peptidase activity”
“cysteine-type endopeptidase activity”
Biological process ontology (BP)
“proteolysis”
“apoptosis”
Terms describe attributes of gene products (GPs)
Any protein or RNA encoded by a gene
Species-independent context, e.g. “ribosome”
Could describe GPs found in limited taxa, e.g. “photosynthesis” or “lactation”
One GP can be associated with ≥ 1 CC, BP, MF
Example: Caspase-6 from Bostaurus

Cellular component ontology
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Describes location at level of subcellular structure & macromolecular complex
GP subcomponent of or located in particular cellular component, with some exceptions:
No individual proteins or nucleic acids
No multicellular anatomical terms
For annotation purposes, a GP can be associated with or located in ≥ one cellular component
Multi-subunit enzyme or protein complex
ribosome
proteasome
ubiquitinligase complex
Anatomical structure
rough endoplasmic reticulum
nucleus
nuclear inner membrane

Molecular function ontology
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Describe gene product activity at molecular level
Describes attributes of entities
Adenylate cyclase (E.C. 4.6.1.1)
Catalyzes a specific reaction:
ATP = 3',5'-cyclic AMP + diphosphate
Described by the Gene Ontology term:
“adenylate cyclase activity” (GO:0004016)
http://www.ebi.ac.uk/pdbsum/1ab8
[accessed 4-Feb-2010]
Usually single GP, sometimes a complex
“ferritin receptor activity”
Definition: “combining with ferritin, an iron-storing protein complex, to initiate a change in cell activity”
Broad functions
“catalytic activity”
“transporter activity”
“binding”
Specific functions
“adenylatecyclase activity”
“protein-DNA complex transmembrane transporter activity”
“Fc-gamma receptor I complex binding”

Biological process ontology
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Describes recognized series of events or molecular functions with a defined beginning and end
“GO does not try to represent the dynamics or dependencies that would be required to fully describe a pathway” (from GO documentation)
Mutant phenotypes often reflect disruptions in BP
Specific process
“pyrimidine metabolism”
“α-glucosidase transport
General considerations
The Cell Cycle
The Development Node
Multi-Organism Process
Metabolism
Regulation
Detection of and Response to Stimuli
Sensory Perception
Signaling Pathways
Transport and Localization
Transporter activity (molecular function)
Other Misc. Standard Defs
Broad process
“cellular physiological process”
“signal transduction”
http://www.geneontology.org/GO.process.guidelines.shtml

Anatomy of a GO term
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Term name
goid (unique numerical identifier)
Synonyms (broad or narrow) for searching, alternative names, misspellings…
Precise textual definition with reference stating source
GO slim
Ontology placement

Storage and cross referencing of GO terms
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Storage in flat file (text)
Database cross reference for mappings to GO
GO term identical to object in other database

Ontology structure:parent-child relationship
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Parent term (broader)
Child term (specialized)
hexose metabolism
monosaccharide biosynthesis
hexose biosynthesis
Up in the tree is more general; down in the tree is more specific:
Annotation of genes
Start with terms denoting broad functional categories
Use more specific term as knowledge warrants

Ontology structure:terms arranged in DAGs
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GO terms structured as hierarchical-like directed acyclic graphs (DAGs)
Tree-like, but each term can have more than one parent (pseudo-hierarchy)
Each term may have one or more child terms (“siblings” share same parent)
parents
child term
parent
child terms
“siblings”

GO has three term relationships
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is_a - child is instance of parent (“A is_a B”)
Class-subclass relationship
part_of - child part of parent (“C part_of D”)
When C present, part of D; but C not always present
Nucleus always part_of cell; not all cells have nuclei
regulates
Child term regulates parent term
(Zoomed in view of biological process ontology depicted here.)

AmiGO for viewing terms
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Open source HTML-based application developed by the GO Consortium
Interface for browsing, querying and visualizing OBO data
Users can search GO terms or annotations
Available via website or download for local install
http://amigo.geneontology.org
Example query with
keyword “hemolysis” or goid GO:0019836
GO:0019836

AmiGO search results
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Click

Term information in AmiGO
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Webpage continues…

AmiGO view continued
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Several informative views
Click
Number of gene products in GO annotation collection annotated to that term or one of its child terms
Relationship between term and its parent
Our term is much further down…

Graph view
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Alternative view of network of terms

A term with two parents
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amine group
carboxylic acid group
generic amino acid
Name: amino acid transmembrane transporter activity
ID number: GO:0015171
Definition: Catalysis of the transfer of amino acids from one side of a membrane to the other. Amino acids are organic molecules that contain an amino group and a carboxyl group. [source: GOC:ai, GOC:mtg_transport, ISBN:0815340729]
parent term: amine transmembrane transporter activity (GO:0005275)
relationship to parent: “is_a”
parent term: carboxylic acid transmembrane transporter activity (GO:0046943)
relationship to parent: “is_a”

Multiple paths to root:graphical view in OBO-Edit
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“True path rule”
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The pathway from a term all the way up to its top-level parent(s) must always be true for any gene product that could be annotated to that term (“if true for the child, then true for the parent”)
Incorrect for Bacteria
cell
organelle
mitochondrion
proton-transporting ATP synthase complex
Correct for Bacteria (and Eukaryotes)
cell
intracellular
proton-transporting ATP synthase complex
plasma membrane proton-transporting ATP synthase complex
mitochondrial proton-transporting ATP synthase complex
membrane
plasma membrane
plasma membrane proton-transporting ATP synthase complex
organelle
mitochondrion
mitochondrial inner membrane
mitochondrial proton-transporting ATP synthase complex
(Abbreviated versions of the actualtrees)

What is GO annotation?
Literature curation at model organism databases
The annotation file
Evidence – critical for annotation
Sequence similarity-based annotation
Annotation specificity
Annotating with GO and Evidence
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GO annotation overview
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Associating a GO term with a gene product
Goal is to select GO terms from all three ontologies to represent what, where, and how
Linking a GO term to a gene product asserts that it has that attribute
For example, 6-phosphofructokinase
Molecular function
GO:0003872 6-phosphofructokinase activity
Biological process
GO:0006096 glycolysis
Cellular component
GO:0005737 cytoplasm
Annotation, whether based on literature or computational methods, always involves:
Learning something about a gene product
Selecting an appropriate GO term
Providing an appropriate evidence code
Citing a [preferably open access] reference
Entering information into GO annotation file

Chaperone DnaK, one protein/multiple annotations
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Molecular function
ATP binding (GO:0005524)
ATPase activity (GO:0016887)
unfolded protein binding (GO:0051082)
misfolded protein binding (GO:0051787)
denatured protein binding (GO:0031249)
Biological process
protein folding (GO:0006457)
protein refolding (GO:0042026)
protein stabilization (GO:0050821)
response to stress (GO:0006950)
Cellular component
cytoplasm (GO:0005737)

Literature curation performed at model organism databases
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From the abstract:

Results section indicates a “direct assay” annotation
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They document the findings of a direct assay performed on purified protein:
They further document the methods used, and evaluate the findings in the Discussion section…

Query AmiGO with “DNA ligase” & “DNA ligation”
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All “ligation” in biological process ontology

Resulting annotations
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Name: DNA ligase (stated in paper)
Gene symbol: ligA (stated in paper)
EC: 6.5.1.2 (queried enzyme for “DNA ligase”)

Gene annotation file captures annotations
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Evidence

Evidence
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Essential to base annotation on evidence
Conclusions more robust and traceable
With evidence, a GO annotation is standard operating procedure (SOP)-independent
Many types of evidence exist
For example, experiment described in literature
What method (e.g. direct assay, mutant phenotype, et cetera) was used?
Did author cite references?
Did author provide details of analyses?
Perhaps you used a sequence-based method
What were the methods of manual curation?
Give accession numbers of similar sequences
Provide any references describing methods
Controlled vocabularies help here, too!

GO standard references
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GO_REF:0000011
A Hidden Markov Model (HMM) is a statistical representation of patterns found in a data set. When using HMMs with proteins, the HMM is a statistical model of the patterns of the amino acids found in a multiple alignment of a set of proteins called the "seed". Seed proteins are chosen based on sequence similarity to each other. Seed members can be chosen with different levels of relationship to each other...
GO_REF:0000011
A Hidden Markov Model (HMM) is a statistical representation of patterns found in a data set. When using HMMs with proteins, the HMM is a statistical model of the patterns of the amino acids found in a multiple alignment of a set of proteins called the "seed". Seed proteins are chosen based on sequence similarity to each other. Seed members can be chosen with different levels of relationship to each other. They can be members of a superfamily (ex. ABC transporter, ATP-binding proteins), they can all share the same exact specific function (ex. biotin synthase) or they could share another type of relationship of intermediate specificity (ex. subfamily, domain). New proteins can be scored against the model generated from the seed according to how closely the patterns of amino acids in the new proteins match those in the seed. There are two scores assigned to the HMM which allow annotators to judge how well any new protein scores to the model. Proteins scoring above the "trusted cutoff" score can be assumed to be part of the group defined by the seed. Proteins scoring below the "noise cutoff" score can be assumed to NOT be a part of the group. Proteins scoring between the trusted and noise cutoffs may be part of the group but may not. One of the important features of HMMs is that they are built from a multiple alignment of protein sequences, not a pairwise alignment. This is significant, since shared similarity between many proteins is much more likely to indicate shared functional relationship than sequence similarity between just two proteins. The usefulness of an HMM is directly related to the amount of care that is taken in chosing the seed members, building a good multiple alignment of the seed members, assessing the level of specificity of the model, and choosing the cutoff scores correctly. In order to properly assess what functional relevance an above-trusted scoring HMM match has to a query, one must carefully determine what the functional scope of the HMM is. If the HMM models proteins that all share the same function then it is likely possible to assign a specific function to high-scoring match proteins based on the HMM. If the HMM models proteins that have a wide variety of functions, then it will not be possible to assign a specific function to the query based on the HMM match, however, depending on the nature of the HMM in question, it may be possible to assign a more general (family or subfamily level) function. In order to determine the functional scope of an HMM, one must carefully read the documentation associated with the HMM. The annotator must also consider whether the function attributed to the proteins in the HMM makes sense for the query based on what is known about the organism in which the query protein resides and in light of any other information that might be available about the query protein. After carefully considering all of these issues the annotator makes an annotation.

GO evidence codeswww.geneontology.org/GO.evidence.shtml
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EXP - inferred from experiment
IDA - inferred from direct assay
IEP inferred from expression pattern
IGI - inferred from genetic interaction
IPI - inferred from physical interaction
IMP - inferred from mutant phenotype
ISS - inferred from sequence or structural similarity
ISA - inferred from sequence alignment
ISO - inferred from sequence orthology
ISM - inferred from sequence model
IGC - inferred from genomic context
ND - no biological data available
IC - inferred by curator
TAS - traceable author statement
NAS - non-traceable author statement
IEA - inferred from electronic annotation
GO codes are a subset of yet another ontology!

Types of sequence similarity-based annotations
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Find similarity between gene product & one that is experimentally characterized
BLAST-type alignments
Shared synteny to establish orthology of genomic regions between species
Find similarity between gene product and defined protein family
HMMs (Pfam, TIGRFAMS)
Prosite
InterPro
Find motifs in gene product with prediction tools
TMHMM
SignalP
Many (most?) information you find is based on transitive annotation and much of it has never been looked at by a human being!

Evaluation of sequence similarity-based information
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Visually inspect alignments & criteria
Length & identity
Conservation of catalytic sites
Check HMM scores with respect to cutoff
Look at available metabolic analysis
Pathways, complexes?
Information from neighboring genes
Gene in an operon (common prokaryotes) can supplement weak similarity evidence
Sequence characteristics
Transmembraneregions?
Signal peptide?
Known motifs that give a clue to function?
Paralogous family member

An example: HI0678, a protein from H. influenzae…
...high quality alignment to experimentally characterized triosephosphateisomerase from Vibrio marinus
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Information from Swiss-Prot database on experimentally characterized match protein
further down the page
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High quality…..
…. full-length match, high percent identity (67.8%), conserved active and binding sites (boxed in red).
52

53
name:triosephosphateisomerase
gene symbol:tpiA
EC: 5.3.1.1
(This, and the following annotations, came from the match protein.)

KEGG pathway for glycolysis core
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KEGG pathway for glycolysis core
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name: triosephosphateisomerase
gene symbol: tpiA
EC: 5.3.1.1

And another annotation
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The biologist knows that glycolysis takes place in the cytoplasm in bacteria, and so infers a cytoplasmic location for that protein (“inferred by curator” evidence code).

Annotation specificity should reflect knowledge
58
GO trees
(very abbreviated)
Function
catalytic activity
kinase activity
carbohydrate kinase activity
ribokinase activity
glucokinase activity
fructokinase activity
Process
metabolism
carbohydrate metabolism
monosaccharide metabolism
hexose metabolism
glucose metabolism
fructose metabolism
pentose metabolism
ribose metabolism
Available evidence for three genes
#1
-good match to an HMM for “kinase”
#2
-good match to an HMM for “kinase”
-a high-quality BER match to an experimentally characterized “glucokinase’ AND a ‘fructokinase’
#3
-good match to an HMM specific for “ribokinase”
-a high-quality BER match to an experimentally characterized ribokinase
#1
#2
#3
#1
#2
#3

Using shared annotations
Search for GO terms at databases
Slims for broad classification
GO tools
Working with GO-limited data sets
Summary
Using annotation to facilitate your research
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Sharing annotations
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Annotation file sent to GO, put in repository
All these data free to anyone
Hundreds of thousands of GP annotations
Annotation files all in same format
Facilitates easy use of data by everyone
Most of your favorite organism databases use these annotation files

Searching for GO terms at EuPathDB
61

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Ontology slim
www.geneontology.org/GO.slims.shtml
Slim is a distilled (reduced) ontology
Made by manually pruning low-level terms with an ontology editor
Selected high-level terms remain
Slims reduce ontology complexity
Reduce clutter & see general trends
Microarray experiments
Comparative whole genome analyses
Remove irrelevant terms
Looking at specific taxa, such as yeast or plant
Go offers script to bin more granular annotations up to higher levels

Comparing genomes with a GO slim
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High-levelbiological process terms used to compare Plasmodium and Saccharomyces
MJ Gardner, et al. (2002) Nature 419:498-511

GO slim: manual/orthology-based gene annotations
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Nucleic Acids Res. 2010 January; 38(Database issue): D420–D427.

GO toolswww.geneontology.org/GO.tools.shtml
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The real challenge is finding the right one for your needs
For example, statistical representation of GO terms:
http://go.princeton.edu/cgi-bin/GOTermFinder

GO & analysis of RNA-seqdata
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Young et al. Genome Biology 2010, 11:R14 http://genomebiology.com/2010/11/2/R14
We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.

When GO is limited
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Food for thought: what happens when we have limited GO (or other)annotation data?
New and interesting genomes often see this problem

Comparative analysis of orthologs in syntenic blocks
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The more genomes we have at our disposal, the better
Structural rearrangements, absence of intron, gene duplication, intron structure, gene deletion/creation
Nucleic Acids Res. 2010 January; 38(Database issue): D420–D427.

Summary GO analyses
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GO remedies problems of synonyms & homonyms in biological nomenclature
Queries based on IDs linked to precise definitions, not less reliable text-matching
GO can help you to:
Find all genes that share a particular function regardless of sequence
Do comparisons across any species annotated with GO
Summarize major classes of genes in a newly sequenced genome
Characterize expressed genes is a study
Drive hypotheses to test in the laboratory
GO is not a panacea but it should be a valuable tool in your genomics toolbox

The title slide revisited…
Ontology
Evidence
Annotation
Arabidopsis thaliana ATPase
HMA4 zinc binding domain
GO:0006829 : zinc ion transport (BP)
GO:0005886 : plasma membrane (CC)
GO:0005515 : protein binding (MF)
Thank you.

Chibucos annot go_final

by Sucheta Tripathy

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