bioinformatics enabling knowledge generation from agricultural omics data
AgBase: bioinformatics enablingknowledge 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
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
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
Biocomputing Technologies enable „omics‟ technologies to move from large database/consortiums into individual laboratories Managing this data: acquire store access analyze visualize share
NIH WORKING DEFINITION OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGYBioinformatics: Research, development, or application ofcomputational tools and approaches for expanding the useof biological, medical, behavioral or health data, includingthose to acquire, store, organize, archive, analyze, orvisualize such data.Computational Biology: The development and application ofdata-analytical and theoretical methods, mathematicalmodeling and computational simulation techniques to thestudy of biological, behavioral, and social systems.
Bioinformatics Managing data 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
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”
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!
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?
What Are Ontologies?“An ontology is a controlled vocabulary of well defined termswith specified relationships between those terms, capable ofinterpretation 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
Ontologies relationshipsbetween terms digital identifier (computers) description (humans) Gene Ontology version 1.1348 (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)
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
Genomic AnnotationStructural Annotation: Open reading frames (ORFs) predicted during genome assembly predicted ORFs require experimental confirmationFunctional 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 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
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
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 http://www.geneontology.org
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 http://www.plantontology.org/
Use GO for…….1. Determining which classes of gene products are over-represented or under-represented.2. Grouping gene products.3. Relating a protein‟s location to its function.4. Focusing on particular biological pathways and functions (hypothesis-testing).
Pathways &Ontologies NetworksGO Cellular Component Pathway Studio 5.0GO Biological Process Ingenuity Pathway AnalysesGO Molecular Function Cytoscape BRENDA Interactome Databases Functional Understanding
1. Provides structural annotation for agriculturally important genomes2. Provides functional annotation (GO)3. Provides tools for functional modeling4. Provides bioinformatics & modeling support for research community
GO & PO: literature annotation for rice, computational annotation for rice, maize, sorghum, Brachypodia1. Literature annotation for Agrobacterium tumefaciens, Dickeya dadantii, Magnaporthe grisea, Oomycetes2. 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 forUniProtKB entries (237,201 taxa).
Comparing AgBase & EBI-GOA Annotations 14,000 computational 12,000 manual - sequence Gene Products 10,000 manual - literature annotated 8,000 Complementary to EBI-GOA: Genbank 6,000 proteins not represented in UniProt 4,000 & EST sequences on arrays 2,000 0 AgBase EBI-GOA AgBase EBI-GOA Chick Chick Cow Cow Project
Contribution to GO Literature Biocuration AgBase EBI GOAChicken 97.82% EBI-IntAct Roslin HGNC < 0.50% UCL-Heart project MGI Cow Reactome 88.78% < 1.50%
AgBase Quality Checks & Releases AgBaseBiocurators‘sanity’ check AgBase ‘sanity’ check AgBase GO analysis toolsbiocuration & GOC database Microarray developers interface QC ‘sanity’ check UniProt db EBI GOA QuickGO browser Project GO analysis tools‘sanity’ check: checks Microarray developersto ensure all appropriate ‘sanity’ checkinformation is captured, & GOC QCno obsolete GO:IDs are Public databasesused, etc. AmiGO browser GO Consortium GO analysis tools database Microarray developers
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
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?
Yams55577 Dioscorea rotundata white yam55571 Dioscorea alata water yam29710 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
Cassava 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
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
Dr Teresia BuzaDr Susan Bridges Cathy Grisham Divya Pedinti Lakshmi Pillai Philippe Chouvarine Seval Ozkan Hui Wang