Creating an integrated Ondex knowledge base for comparative gene function analysis


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Keywan Hassani-Pak
Plant Systems Biology meeting
Edinburgh, March 2011

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  • Years from: 2% of all sequenced genomes are plant genomes. Over 20 plant genomes. Evolutionary distance between them rather quite large. Comparative approaches focus on transfering knowledge across species.We only have few genomes (taxonomy sampling) and their evolutionary distance is important (big)Challenging to transfer knowledge cross-speciesComparative bioinformatics approaches for predictingthe function of genes and their role in complex traits
  • Ontologies gives us a mean to structure the functional knowledge andBridge the gab across species and databases.In this recent work we looked at functional gene annotations between orthologous genes
  • Example of BC1 and COBL4 genes in Arabidopsis and rice. Both genes related to cellulose synthesis.Find orthologous genes in Arabidopsis and rice genomesGather all functional knowledge in form of GO, PO and TO annotationsFilter IEA annotations (not manually currated)
  • Experimental Evidence Codes EXP: Inferred from Experiment IDA: Inferred from Direct Assay IPI: Inferred from Physical Interaction IMP: Inferred from Mutant Phenotype IGI: Inferred from Genetic Interaction IEP: Inferred from Expression Pattern Change order of bullet points PO, GO, TOPut figure in separate slide
  • Can we transfer annotations from model plant to cropsChange side of ontology terms according to their conservation/transferability
  • QTL are genomic regions that assign variations observed in a phenotype to a region on the genetic mapBiomass traits: branching, height, leaf number etc.What is going on underneath of a QTL? We are going from Willow to Poplar to Arabidopsis and other species
  • Systematically search for all trait-based related genes in the genome.
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  • Creating an integrated Ondex knowledge base for comparative gene function analysis

    1. 1. Creating an integrated Ondex knowledge base for comparative gene function analysis<br />Keywan Hassani-Pak<br />UK Plant Systems Biology Workshop 2011<br /><br />
    2. 2. 2% of fully sequenced genomes are plant genomes<br />Sequenced Plant and Crop Genomes<br /><br />Arabidopsis<br />Rice<br />?<br />Functional knowledge<br />
    3. 3. Functional knowledge in plant and crop databases <br />Source: Mochida “Genomics and bioinformatics resources for crop improvement.” (2010) <br />
    4. 4. Ontologies – Bridging the gap across species and databases<br />Major ontologies used in plant databases<br />Gene Ontology (GO)<br />Plant Ontology (PO)<br />Gramene Trait Ontology (TO)<br />Crop Ontology (CO)<br />Functional annotations use terms from ontologies<br />Annotations must haveevidence types, e.g. IMP, IEA<br />Metrics exist to computesemantic similarity of terms<br />
    5. 5. IEA<br />Comparing functional annotations between orthologous genes<br />Conserved<br />Arabidopsis<br />Rice<br />IMP<br />
    6. 6. A knowledge base of functional annotations in plants<br />October 2010<br />Collected data from TAIR and Gramene<br />Used Ondex to create a knowledgebase of functional gene annotations in plants<br />Measured conservation and transferability of ontology terms<br />
    7. 7. Conservation of Plant Ontology terms between Arabidopsis and rice<br />Conserved<br />?<br />NotConserved<br />A function is conserved if genes from two species are annotated with the same term and are orthologous<br />Conservation of ontology terms can be used as background knowledge for cross-species annotation transfer<br />Conserved terms suited for automated annotation transfer<br />Not conserved terms need further investigation<br />Defoin-Platel M, Hassani-Pak K and Rawlings CJ (2011)<br />
    8. 8. Cross-species annotation transfer<br />Newly sequenced crop gene<br />Arabidopsis<br />Rice<br />IMP<br />
    9. 9. Bioenergy application case<br />Linked<br />References<br />Arabidopsis, Rice etc.<br />Poplar<br />Willow<br />Phenotypes<br />Plant Hormones<br />Physical <br />map<br />QTL Map<br />Orthologous Genes<br />Expression Patterns<br />Pathways<br />Cross-species annotation transfer<br />List of candidate genes linked to biomass traits<br />
    10. 10. Is Gene X a possible biomass candidate?<br />IEA<br />E-Value: 8.3E-17<br />MSA<br />IGI<br />IMP<br />
    11. 11. Phenotypic information in literature<br />Keyword: Shoot branching<br />
    12. 12. Summary<br />Study of gene function needs integration of multiple sources of information<br />Cross-species knowledge is one (sometimes the only) source of information we have available<br />Ontologies are essential for comparing annotations across species and databases<br />Developed a procedure to measure transferability of Gene Ontology (GO) and Plant Ontology (PO) terms <br />QTL analysis and candidate gene prediction using integrative and comparative approaches<br />
    13. 13. Acknowledgements<br />Rothamsted<br />Chris Rawlings<br />Jan Taubert<br />Catherine Canevet<br />KeywanHassani-Pak<br />Andrea Splendiani<br />Matthew Hindle<br />Artem Lysenko<br />Michael Defoin-Platel<br />Angela Karp<br />Steve Hanley<br />Kim Hammond-Kosack<br />Martin Urban<br />Manchester<br />Robert Stevens<br />Carole Goble<br />Pedro Mendes <br />Paul Dobson<br />Paul Fisher<br />David Withers<br />Georgina Moulton<br />Katy Wolstenholme<br />NaCTem<br />Sophia Ananiadou<br />Gina-Anne Levow<br />RaheelNawaz<br />Newcastle<br />Neil Wipat<br />Phil Lord<br />Darren Wilkinson<br />Jochen Weile<br />Matthew Pocock<br />Simon Cockell<br />James Dewar<br />Katherine James<br />Eva Holstein<br />Edinburgh<br />Igor Goryanin<br />Andrew Millar<br />Luna de Ferrari<br />
    14. 14. From QTL to candidate genes<br />Rae et al., 2008<br />Gene<br />Prioritisation<br />Can help to reduce hypothesis space from 100 potential candidates to few hot candidates.<br />Next step experimental validation: Cloning and transformation in models (Arabidopsis/Poplar).<br />