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Dr Xavier Sirault 1 Dr Bob Furbank 1 1: CSIRO Plant Industry, Black Mountain Cnr Clunies Ross St & Barry Drive Canberra, ACT 2601

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Presentation on theme: "Dr Xavier Sirault 1 Dr Bob Furbank 1 1: CSIRO Plant Industry, Black Mountain Cnr Clunies Ross St & Barry Drive Canberra, ACT 2601"— Presentation transcript:

1 Dr Xavier Sirault 1 Dr Bob Furbank 1 1: CSIRO Plant Industry, Black Mountain Cnr Clunies Ross St & Barry Drive Canberra, ACT 2601 Novel High Resolution tools at the HRPPC An Ontology-centric Architecture for Extensible Scientic Data Management Systems Gavin Kennedy 1,2 Dr Yuan-Fang Li 3 2: School of ITEE, University of Queensland, St Lucia, QLD 3: Clayton School of IT, Monash University, Clayton, VIC

2 What is Plant Phenomics? Phenome = Genome X Environment Genomics is accelerating gene discovery but how do we capitalise on these data sets to establish gene function and development of new genotypes for agriculture? High throughput and high resolution analysis capacity now the factor limiting discovery of new traits and varieties In the next 50 years we must produce more food than we have consumed in the history of mankind Megan Clarke, CSIRO CEO 2009

3 Phenomics from the Leaf to the Field Imagine a plant breeder walking his trials logging plant performance distributed sensors with his mobile phone or logging on to Phenonet from home to view his wheat in real time

4 HRPPC: Canberra node of the Australian Plant Phenomics Facility Infrastructure: 1500 m 2 lab space 245 m 2 greenhouse 260 m 2 growth cabinets Analytical tools packaged in: 1- Model Plant Module (HTP) 2- Crop-Plant Shoot Module (MTP) 3- Crop-Plant Root Module (MTP) 4- Crop-Plant Field Module (HTP) Brachypodium distachyon Arabidopsis thaliana Gossypium species Triticum and Hordeum species, Vigna unguiculata (cowpea), Cicer arietinum (chickpea), Zea mays (maize), Sorghum bicolor, … Role Deep phenotyping Development of next generation tools to probe plant function and performance (come and see us)

5 Far Infrared imaging Canopy / leaf temperature Water use / salt tolerance Capitalising on new imaging technologies Visible imaging Plant area, biomass, structure Senescence, relative chlorophyll content, pathogenic lesions Near IR imaging Tissue water content Soil water content Chlorophyll Fluorescence imaging Physiological state of photosynthetic machinery FTIR Imaging Spectroscopy / Hyperspectral imaging Cellular localisation of metabolites (sugars, protein, aromatics) Carbohydrates, pigments and proteins Plant FunctionPlant Morphology

6 Light Detection and Ranging (LiDAR) Micro-bolometer sensors (Far- Infrared) 4-CCD line scanner (NIR and visible split) PlantScan: next generation phenotyping platform for n-dimensional Models Addressing issues with fluorescence and environmental control

7 Automated features extraction and quantification of n-dimensional models Jurgen Fripp CSIRO ICT E-Health Brisbane Automated segmentation – extracted stem Bounding box extraction and Delauney triangulation for convex 3D hull Height and total volume extraction Volume over time Sirault, Fripp and Furbank (in preparation)

8 An integrated phenotyping platform for Model Plants PAM Fluorescence imaging Far Infrared imaging Visible imaging for growth Climate controlled in equilibration chamber and imaging chambers 2500 plants per day Applications: 1001 genomes project - 65 re-sequenced Arabidopsis thaliana ecotypes under analysis - with Detlef Weigel USDA Brachypodium distachyon project

9 Distributed Sensor Network for Phenomics Measure and log range of environmental factors on field trials. Zigby wireless transmitters: Thermopile Temp Sensor Humidity Ambient Temp Soil Moisture Imaging: Estimate biomass; greeness index for fertilization; detect flowering; estimate yield. Imaging constrained: Develop smarter portable platforms.

10 Ontologies Ontologies are a set of formalised terms that allow us to represent knowledge about concepts and relationships in a domain. Annotating with ontologies means describing a domain object or process. Modelling with ontologies means classifying a domain object or process, and its relationship to other domain concepts. This image shows the wheat plant on the left has increased salt tolerance (TO: ) OBI: : platform A platform is an object_aggregate that is the set of instruments and software needed to perform a process.

11 Ontologies Evolutionary Changes in Domain, Model & Data Expressed in OWL (& RDF Schema) Provides syntax & semantics - enables reasoning Expressivity vs decidability Validation via reasoning Designed to be open & interoperable Facilitates sharing, reuse & Integration Maturing technology stacks APIs, reasoners, triple stores, query engines

12 TrayScan PODD PlantScan Phenonet Phenomobile PODD Data Stores PODD Data Stores PODD Metadata Repository PODD Metadata Repository Data Metadata Data Metadata The Phenomics Ontology Driven Data repository A research data and metadata repository. Managing Phenomics Data from Multiple Heterogeneous High Volume High Resolution Data Generation Platforms A methodology for managing and publishing research data outputs. A semantic web data resource.

13 Putting the OD in PODD Basics: Ontologies as domain models for research data Model domain objects as ontological objects Base ontology: domain independent Phenomics ontology: domain specific Organizes data logically Represented as metadata objects Parent-child relationship Referential relationship Drives all operations in the data lifecycle Domain ConceptsOWL Classes Attributes and relationsOWL Predicates Domain ObjectsOWL Individuals Comments, descriptionsOWL Annotations

14 The PODD Ontology Platform Project Project Plan Investigation Analysis Event Genotype Material Treatment Material Treatment Material Container Data Gene Sequence Treatment Observation/ Phenotype Measurement Parameter Measurement Parameter Environment Sex Archive Data Archive Data Design

15 PODD Architecture Objects represented semantically Semantics (metadata) captured in RDF Repository operations on RDF: Ingestion, retrieval, update, query & search, export Backend Object Management: Fedora Commons Fedora objects mapped to Java objects for: Business Logic Layer Interface Layer

16 Future Work Annotation Services Ontological tagging of PODD objects Annotation tools, search/discovery tools, browsers, etc. Virtual Laboratory Environment Support Phenome to Genome (and back) discovery processes Analyse linkages across data resources Workflows for statistical inferences & mathematical modelling. Visualisation tools etc...

17 Resources Plant Phenomics Test Instance: Plant Phenomics Production Instance: Mouse Phenomics Production Instance: PODD Project Website: Contact: Ph: This work is part of a National eResearch Architecture Taskforce (NeAT) project, supported by the Australian National Data Service (ANDS) through the Education Investment Fund (EIF) Super Science Initiative, and the Australian Research Collaboration Service (ARCS) through the National Collaborative Research Infrastructure Strategy Program.

18 The Team PODD Project Manager Gavin Kennedy University of Queensland eResearch Lab: Faith Davies (Developer) Simon McNaughton (Developer) Jane Hunter (eResearch Lab Leader) APPF/HRPCC/CSIRO Xavier Sirault (Science Leader, HRPPC) Xueqin Wang (Tester, Documentor) Bob Furbank (APPF HRPPC Leader) APPF/Plant Accelerator/Uni of Adelaide Bogdan Masznicz (Bioinformatician) Mark Tester (APPF TPA Leader) APN Philip Wu (Developer) Martin Hamilton (Developer) Adrienne McKenzie (APN Head of Network Services) Monash Univesity Yuan-Fang Li (Designer) NeAT Andrew Treloar (Deputy Director ANDS) Paul Coddington (Projects Manager, ARCS) ALA Donald Hobern (Director, ALA)

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