1. An Ontology-centric Architecture for Extensible
Scientic Data Management Systems
Gavin Kennedy1,2
Dr Yuan-Fang Li3
2: School of ITEE, University of Queensland, St Lucia, QLD
3: Clayton School of IT, Monash University, Clayton, VIC
Novel High Resolution tools at the HRPPC
Dr Xavier Sirault1
Dr Bob Furbank1
1: CSIRO Plant Industry, Black Mountain
Cnr Clunies Ross St & Barry Drive
Canberra, ACT 2601

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
Role
Deep phenotyping
Development of next generation tools to probe plant function and performance (come and see us)
Brachypodium distachyon
Arabidopsis thaliana
Infrastructure:
1500 m2 lab space
245 m2 greenhouse
260 m2 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)
Gossypium species
Triticum and Hordeum species,
Vigna unguiculata (cowpea),
Cicer arietinum (chickpea),
Zea mays (maize),
Sorghum bicolor, … 4

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

6. Addressing issues with fluorescence and environmental control
PlantScan: next generation phenotyping platform for n-dimensional Models
Light Detection and Ranging (LiDAR)
Micro-bolometer sensors (Far-Infrared)
4-CCD line scanner (NIR and visible split)
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
Volume over time
Height and total volume extraction
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. www.phenonet.com 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. PODD The Phenomics Ontology Driven Data repository
PlantScan
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.
Phenonet
Data
Phenomobile
TrayScan
Metadata
PODD
Metadata
Repository
PODD
Data Stores
Data
Metadata

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 Concepts
OWL Classes
Attributes and relations
OWL Predicates
Domain Objects
OWL Individuals
Comments, descriptions
OWL Annotations

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

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)