ppt - Phenotype RCN

Report
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
[email protected]
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
[email protected]
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
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
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)
Infrastructure:
1500 m2 lab space
 245 m2 greenhouse
 260 m2 growth cabinets
Analytical tools packaged in:
Brachypodium
distachyon
Arabidopsis thaliana
Triticum and Hordeum species,
Vigna unguiculata (cowpea),
Cicer arietinum (chickpea),
Zea mays (maize),
Sorghum bicolor, …
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
Capitalising on new imaging technologies
Plant Morphology
Visible imaging
•Plant area, biomass, structure
•Senescence, relative chlorophyll
content, pathogenic lesions
Plant Function
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
PlantScan: next generation phenotyping
platform for n-dimensional Models
•
•
•
Light Detection and Ranging (LiDAR)
Micro-bolometer sensors (FarInfrared)
4-CCD line scanner (NIR and visible
split)
Addressing issues with fluorescence
and environmental control
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)
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
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
Imaging: Estimate biomass; greeness index for
Ambient Temp
fertilization; detect flowering; estimate yield.
Soil Moisture
Imaging constrained: Develop smarter portable
platforms.
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.
This image shows the wheat plant on
the left has increased “salt tolerance
(TO:0006001)”
Modelling with ontologies means classifying a domain object or
process, and its relationship to other domain concepts.
OBI:0000050 : “platform”
“A platform is an object_aggregate that is
the set of instruments and software needed
to perform a process. “
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
PODD
PlantScan
The Phenomics Ontology Driven Data
repository
Phenonet
Data
Phenomobile TrayScan
Metadata
PODD
Metadata
Repository
Data
PODD
Data Stores
Metadata
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.
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
The PODD Ontology
Project
Project Plan
Treatment
Material
Investigation
Material
Platform
Container
Analysis
Event
Data
Environment
Genotype
Design
Gene
Sex
Observation/
Phenotype
Treatment
Archive
Data
Sequence
Measurement
Measurement
Parameter
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
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...
Resources
Plant Phenomics Test Instance: http://poddtest.plantphenomics.org.au/
Plant Phenomics Production Instance: http://podd.plantphenomics.org.au/
Mouse Phenomics Production Instance: http://podd.australianphenomics.org.au
PODD Project Website: http://projects.arcs.org.au/trac/podd
Contact: [email protected]
Ph: +61413 337 819
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.
The Team
PODD Project Manager
Gavin Kennedy
University of Queensland eResearch Lab:
Faith Davies (Developer)
Simon McNaughton (Developer)
Jane Hunter (eResearch Lab Leader)
APN
Philip Wu (Developer)
Martin Hamilton (Developer)
Adrienne McKenzie (APN Head of Network
Services)
Monash Univesity
Yuan-Fang Li (Designer)
APPF/HRPCC/CSIRO
Xavier Sirault (Science Leader, HRPPC) NeAT
Xueqin Wang (Tester, Documentor)
Andrew Treloar (Deputy Director ANDS)
Bob Furbank (APPF HRPPC Leader)
Paul Coddington (Projects Manager, ARCS)
APPF/Plant Accelerator/Uni of Adelaide
Bogdan Masznicz (Bioinformatician)
Mark Tester (APPF TPA Leader)
ALA
Donald Hobern (Director, ALA)

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