1. AgBase:
bioinformatics enabling knowledge generation from agricultural omics data
Fiona McCarthy

2. 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

3. Databases and Biological Data
The number of databases has increased
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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5. Generating Biological Data
Amount of biological data is increasing exponentially
Completed and ongoing genome sequencing projects
High throughput “omics” technologies
New sequencing technologies
Existing microarrays
Proteomics

7. Biocomputing
Technologies enable ‘omics’ technologies to move from large database/consortiums into individual laboratories
Managing this data:
acquire
store
access
analyze
visualize
share

8. 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.

9. Bioinformatics Managing data Adding value Organizing
different file formats
linking between different databases
Adding value
multiple levels of information from one ‘omics’ data set
re-analysis
linking data sets
Organizing
annotating data
biocuration - annotation

10. Annotation ANNOTATE: to denote or demarcate
Genome annotation is the process of attaching biological information to genomic sequences. It consists of two main steps:
identifying functional elements in the genome: “structural annotation”
attaching biological information to these elements: “functional annotation”

11. Community Annotation
Researchers are the domain experts – but relatively few contribute to annotation
time
'reward' & 'employer/funding agency recognition'
training – easy to use tools, clear instructions
Required submission
Community annotation
Groups with special interest do focused annotation or ontology development
As part of a meeting/conference or distributed (eg. wikis)
Students!

12. 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
“knowledge databases”
but how to ensure data consistency between databases?

13. 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.
The ontology shows how the objects relate to each other

14. relationships between terms
Ontologies
relationships between terms
digital identifier
(computers)
description
(humans)
Gene Ontology version (27/07/2010):
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)

16. Relationships: the True Path Rule
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

17. 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

18. Other annotations using other bio-ontologies e.g.
Genomic Annotation
Structural Annotation
including Sequence Ontology
Other annotations using other bio-ontologies e.g.
Anatomy
Ontology
Nomenclature
(species’ genome nomenclature committees)
Functional annotation using
Gene Ontology

19. Expression/Tissue Ontologies Infectious Disease Ontology Cell Ontology
Gene Ontology
Plant Ontology
Sequence Ontology
Trait Ontology
Expression/Tissue Ontologies
Infectious Disease Ontology
Cell Ontology

20. Bio-ontology requirements
bio-ontologies (Open Biomedical Ontologies)
computational pipelines (‘breadth’)
for computational annotations
useful for gene products without published information
manual biocuration (‘depth’)
requires trained biocurators
community annotation efforts
each species has its own body of literature
biocuration co-ordination
MODs? Consortium? Community?
biocuration prioritization
co-ordination with existing Dbs, annotation, nomenclature initiatives
data updates

21. 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

22. Plant Ontology (PO)
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

23. Use GO for…….
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).

24. Functional Understanding
Pathways & Networks
Ontologies
Functional Understanding
GO Cellular Component
GO Biological Process
GO Molecular Function
BRENDA
Pathway Studio 5.0
Ingenuity Pathway Analyses
Cytoscape
Interactome Databases

26. Provides structural annotation for agriculturally important genomes
Provides functional annotation (GO)
Provides tools for functional modeling
Provides bioinformatics & modeling support for research community

27. Avian Gene Nomenclature

29. GO & PO: literature annotation for rice, computational annotation for rice, maize, sorghum, Brachypodia
Literature annotation for Agrobacterium tumefaciens, Dickeya dadantii, Magnaporthe grisea, Oomycetes
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).

31. Gene Products annotated
Comparing AgBase & EBI-GOA Annotations
14,000
computational
12,000
manual - sequence
10,000
manual - literature
Gene Products annotated
8,000
Complementary to EBI-GOA: Genbank proteins not represented in UniProt & EST sequences on arrays
6,000
4,000
2,000
AgBase
EBI-GOA
AgBase
EBI-GOA
Chick
Chick
Cow
Cow
Project

32. Contribution to GO Literature Biocuration
AgBase
EBI GOA
Chicken
97.82%
EBI-IntAct
Roslin
HGNC
< 0.50%
UCL-Heart project
MGI
Cow
Reactome
88.78%
< 1.50%

33. AgBase Quality Checks & Releases
AgBase Biocurators
‘sanity’ check
AgBase
biocuration
interface
‘sanity’ check
& GOC QC
AgBase database
GO analysis tools
Microarray developers
‘sanity’ check
UniProt db
QuickGO browser
GO analysis tools
Microarray developers
EBI GOA Project
‘sanity’ check: checks to ensure all appropriate information is captured, no obsolete GO:IDs are used, etc.
‘sanity’ check
& GOC QC
Public databases
AmiGO browser
GO analysis tools
Microarray developers
GO Consortium
database

34. Quality improvement Microarray annotations

36. 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

37. Cowpea 54,123 genome sequences 187,483 ESTs
Annotated via homology to Arabidopsis & other plants
GO annotation via homology – availability?

38. 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

41. 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

42. 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?

43. Yams 55577 Dioscorea rotundata white yam
55571 Dioscorea alata water yam
29710 Dioscorea cayenensis 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

44. Cassava ESTs: 80,631 NCBI proteins: 568, UniProt:253
2,251 GO annotations assigned to 218 proteins
2 Euphorbia esula (leafy spurge) /cassava arrays

45. 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

49. 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

50. Dr Teresia Buza
Dr Susan Bridges
Cathy Grisham
Divya Pedinti
Lakshmi Pillai
Philippe Chouvarine
Seval Ozkan
Hui Wang