ppt - Phenotype RCN

farmpaintlickInternet και Εφαρμογές Web

21 Οκτ 2013 (πριν από 3 χρόνια και 10 μήνες)

75 εμφανίσεις

Dr Xavier Sirault
1

Dr Bob Furbank
1


1
: CSIRO
Plant Industry, Black Mountain

Cnr

Clunies

Ross St & Barry Drive

Canberra, ACT 2601


Xavier.sirault@csiro.au


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


Gavin.kennedy@csiro.au

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

t
han
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

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)


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 Function

Plant Morphology


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

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)

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


Ambient Temp


Soil Moisture

Imaging: Estimate biomass;
greeness

index for
fertilization; detect flowering; estimate yield.

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.





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:0006001
)”


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

TrayScan

PODD

PlantScan

Phenonet

Phenomobile

PODD

Data Stores

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
.

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

Platform

Project

Project Plan

Investigation

Analysis

Event

Genotype

Material

Treatment

Material

Container

Data

Gene

Sequence

Treatment

Observation/
Phenotype

Measurement

Measurement

Parameter

Environment

Sex

A
rchive

Data

Design

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:
Gavin.Kennedy@csiro.au

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)


APPF/HRPCC/CSIRO


Xavier
Sirault

(Science Leader, HRPPC)


Xueqin

Wang (Tester,
D
ocumentor
)


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)