Transcript Gene Ontology

AgBase:
bioinformatics enabling
knowledge generation from
agricultural omics data
Fiona McCarthy
Summary
‘omics’ technologies: the ‘data deluge’
 organising data: bioinformatics and
biocuration
 data sharing and analysis: bio-ontologies
 from data to knowledge
 making sense of agricultural data
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Databases and Biological Data
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The number of databases has increased
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Sequence repositories: NCBI, EMBL, DDJB
Model Organism Databases (MODs)
Specialist biological databases or ‘knowledge
databases’ (eg, InterPro, interaction
databases, gene expression data)
Need to connect information in different
databases
 Databases are increasing in size and
complexity
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Generating Biological Data
Amount of biological data is increasing
exponentially
 Completed and ongoing genome
sequencing projects
 High throughput “omics” technologies
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New sequencing technologies
Existing microarrays
Proteomics
Biocomputing
Technologies enable ‘omics’ technologies
to move from large database/consortiums
into individual laboratories
 Managing this data:
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acquire
store
access
analyze
visualize
share
NIH WORKING DEFINITION OF BIOINFORMATICS AND
COMPUTATIONAL BIOLOGY
Bioinformatics: Research, development, or application of
computational tools and approaches for expanding the use
of biological, medical, behavioral or health data, including
those to acquire, store, organize, archive, analyze, or
visualize such data.
Computational Biology: The development and application of
data-analytical and theoretical methods, mathematical
modeling and computational simulation techniques to the
study of biological, behavioral, and social systems.
Bioinformatics
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Managing data
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Adding value
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different file formats
linking between different databases
multiple levels of information from one ‘omics’
data set
re-analysis
linking data sets
Organizing
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annotating data
biocuration - annotation
Annotation
ANNOTATE: to denote or demarcate
 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”
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Community Annotation
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Researchers are the domain experts – but
relatively few contribute to annotation
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time
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'reward' & 'employer/funding agency recognition'
training – easy to use tools, clear instructions
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Required submission
Community annotation
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Groups with special interest do focused
annotation or ontology development
As part of a meeting/conference or distributed
(eg. wikis)
Students!
Biocuration
biocurators are biologists who are trained
to annotate biological data (using
database structures, bio-ontologies, etc).
 databases use biocuration to enhance
value of biological data
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“knowledge databases”
but how to ensure data consistency
between databases?
What Are Ontologies?
“An ontology is a controlled vocabulary of well defined terms
with specified relationships between those terms, capable of
interpretation by both humans and computers.”
 Bio-ontologies are used to capture biological
information in a way that can be read by both
humans and computers
 annotate data in a consistent way
 allows data sharing across databases
 allows computational analysis of high-throughput
“omics” datasets
 Objects in an ontology (eg. genes, cell types, tissue
types, stages of development) are well defined.
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The ontology shows how the objects relate to each
other
relationships
between terms
Ontologies
digital identifier
(computers)
Gene Ontology version 1.1348 (27/07/2010):
description
(humans)
32,091 terms, 99.3% defined
19,169 biological process
2,745 cellular component
8,736 molecular function
1,441 obsolete terms (not included in figures above)
Relationships: the True Path Rule
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Why are relationships between terms
important?
TRUE PATH RULE: all attributes of
children must hold for all parents
so if a protein is annotated to a term, it
must also be true for all the parent
terms
this enables us to move up the ontology
structure from a granular term to a
broader term
Premise of many GO anaylsis tools
Genomic Annotation
Structural Annotation:
 Open reading frames (ORFs) predicted during
genome assembly
 predicted ORFs require experimental confirmation
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
Gene Ontology annotation does not rely on a
completed genome sequence
Genomic Annotation
Other
annotations
using other bioontologies e.g.
Anatomy
Ontology
Structural Annotation
including Sequence Ontology
Functional annotation using
Gene Ontology
Nomenclature
(species’ genome
nomenclature
committees)
http://obo.sourceforge.net/
Gene Ontology
Plant Ontology
Sequence Ontology
Trait Ontology
Expression/Tissue Ontologies
Infectious Disease Ontology
Cell Ontology
Bio-ontology requirements
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bio-ontologies (Open Biomedical Ontologies)
computational pipelines (‘breadth’)
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manual biocuration (‘depth’)
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for computational annotations
useful for gene products without published information
requires trained biocurators
community annotation efforts
each species has its own body of literature
biocuration co-ordination
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MODs? Consortium? Community?
biocuration prioritization
co-ordination with existing Dbs, annotation, nomenclature
initiatives
data updates
Gene Ontology (GO)
de facto method for functional annotation
 Assigns functions based upon Biological
Process, Molecular Function, Cellular
Component
 Widely used for functional genomics (high
throughput)
 Many tools available for gene expression
analysis using GO
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http://www.geneontology.org
Plant Ontology (PO)
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describes plant structures and growth and
developmental stages
Currently used for Arabidopsis, maize, rice – more
being added (soybean, tomato, cotton, etc)
Plant Structure: describes morphological and
anatomical structures representing organ, tissue and
cell types
Growth and developmental stages: describes (i)
whole plant growth stages and (ii) plant structure
developmental stages
http://www.plantontology.org/
Use GO for…….
1.
2.
3.
4.
Determining which classes of gene products
are over-represented or under-represented.
Grouping gene products.
Relating a protein’s location to its function.
Focusing on particular biological pathways
and functions (hypothesis-testing).
Ontologies
Pathways &
Networks
GO Cellular Component
Pathway Studio 5.0
GO Biological Process
Ingenuity Pathway Analyses
GO Molecular Function
Cytoscape
BRENDA
Interactome Databases
Functional Understanding
http://www.agbase.msstate.edu/
1.
2.
3.
4.
Provides structural annotation for
agriculturally important genomes
Provides functional annotation (GO)
Provides tools for functional modeling
Provides bioinformatics & modeling
support for research community
Avian Gene Nomenclature
GO & PO: literature annotation for rice,
computational annotation for rice,
maize, sorghum, Brachypodia
1. Literature annotation for Agrobacterium
tumefaciens, Dickeya dadantii,
Magnaporthe grisea, Oomycetes
2. Computational annotation for
Pseudomonas syringae pv tomato,
Phytophthora spp and the nematode
Meloidogyne hapla.
Literature annotation for chicken,
cow, maize, cotton;
Computational annotation for
agricultural species & pathogens.
literature annotation for human;
computational annotation for
UniProtKB entries (237,201 taxa).
Comparing AgBase & EBI-GOA Annotations
14,000
computational
Gene Products
annotated
12,000
manual - sequence
10,000
manual - literature
8,000
Complementary to
EBI-GOA: Genbank
proteins not
represented in UniProt
& EST sequences on
arrays
6,000
4,000
2,000
0
AgBase
Chick
EBI-GOA AgBase
Chick
Cow
Project
EBI-GOA
Cow
Contribution to GO Literature Biocuration
AgBase
EBI GOA
Chicken
97.82%
EBI-IntAct
Roslin
HGNC
< 0.50%
UCL-Heart project
MGI
Reactome
Cow
88.78%
< 1.50%
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
Quality improvement Microarray annotations
IITA Crops
cowpea – “reduced representation” sequencing
underway
 soybean - preliminary assembly
 banana - sequencing in progress
 yam - genome sequencing for Dioscorea alata
– EST development (IITA & VSU)
 cassava - genome sequencing in progress
 maize - genome sequencing completed; other
subspecies being sequenced
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Cowpea
54,123 genome sequences
 187,483 ESTs
 Annotated via homology to Arabidopsis &
other plants
 GO annotation via homology – availability?
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Soybean
NCBI: 1,459,639 ESTs, 34,946 proteins,
2,882 genes
 UniProt: 12,837 proteins (EBI GOA
automatic GO annotation)
 UniGene assemblies available
 multiple microarrays available
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Banana
7,102 genome sequences
 14,864 ESTs
 1,399 NCBI proteins; 680 UniProt
 Musa acuminata (sweet banana): 3,898
GO annotations to 491 proteins
 Musa acuminata AAA Group (Cavendish
banana): 579 annotations to 96 proteins
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Plantain
Musa ABB Group (taxon:214693) cooking banana or plantain
 11,070 ESTs, 112 proteins
 173 GO annotations to 53 proteins
 functional genomics based on banana?
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Yams
55577
55571
29710
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Dioscorea rotundata
Dioscorea alata
Dioscorea cayenensis
white yam
water yam
yellow yam
Dioscorea (taxon:4672) & subspecies
NCBI: 31 ESTs, 623 proteins
Genome sequencing for Dioscorea alata – EST
development (IITA & VSU)
183 GO annotations to 25 proteins
Cassava
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ESTs: 80,631
NCBI proteins: 568, UniProt:253
2,251 GO annotations assigned to 218 proteins
2 Euphorbia esula (leafy spurge) /cassava arrays
Maize
Zea mays (taxon:4577)
 Genome sequencing completed by
Washington University – other subspecies
being sequenced
 Active GO annotation project - 131,925
GO annotations to 20,288 proteins
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AgBase Collaborative Model
How can we help you?
 Can make GO annotations public via the
GO Consortium
 Have computational pipelines to do rapid,
first pass GO annotation (including
transcript/EST sequences)
 Provide bioinformatics support for
collaborators
 Developing new tools
 Training/support for modeling data
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Dr Teresia Buza
Dr Susan Bridges
Cathy Grisham
Divya Pedinti
Lakshmi Pillai
Philippe Chouvarine
Seval Ozkan
Hui Wang