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Issues in Plant Phenomics
The Challenge of inference from Genome to Phenome 25-27th March 2015
CSIRO AGRICULTURE FLAGSHIP, HIGH RESOLUTION PLANT PHENOMICS CENTRE
Dr Xavier Sirault (and Jose Berni-Jimenez)
Scientific Director (A/g), High Resolution Plant Phenomics Centre Research Team Leader – Phenomics Informatics and Growth Modelling
Phenomics in the post-genomics era addressing the G2P problem
The Economist - Biology 2.0 (2010)
“In the age of the genotype the phenotype is king!” Mike Coffey (Scotland Rural College)
400M bp
16000M bp
2500M bp Expensive
Slow
Complex (E)
Phenotyping
bottleneck
Modern sequencer: one human genome
every 14 minutes at a cost of US $5,000
P = G×E (×M)
Vision of the Australian Plant Phenomics Facility (APPF)
The APPF is a world leading centre underpinning innovative plant phenomics research to accelerate the development of new and improved crops, healthier food and more sustainable agricultural
practice.
AU$32 m (2010)
Infrastructure and Services
State-of-the-art imaging technologies and data analysis tools are combined to help measure the attributes of plants in different environments and relate this knowledge to their genetic make-up.
AU$21 m (2009)
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Prof Justin Borevitz Assoc. Prof Rachel Burton Dr Xavier Sirault
A new paradigm: phenotypes are dynamic!
Time of day
PAM fluorescence and Laser Induced
Fluorescent Transient (LIFT)
Pt = G×Et (×M) with t: time
Time constant
Seconds Hours Days Season Years
Dealing with time variation when measuring canopy
temperature – when is a phenotype a trait?
Mundah (salt tolerant)
Keel (salt sensitive)
Control conditions
Saline conditions
Time of day
Yanco, NSW, early Oct 12:00, 350 m alt. H2 ~ 0.1 (Rebetzke et al.)
H2 ~ 0.6
IR and stomatal closure: Sirault et al, 2009 FPB 36:970-977
Gravitropism: Stem
growing straight up in
response to gravity and light
(Auxin)
Thigmotropism: thigmotropic
response of twining stems
causes them to coil around the
object with which they have
come in contact - e.g. Pea
(Pisum sativum) - tendrils
Plant growth is dynamically affected by environmental cues
Phototropism: Corn (Zea mays)
coleoptile bending towards the
light
Tropism is a growth response between a plant and
an external stimulus
The Omega Garden™
Farmdominium/Vertical Farming
Growth analysis in response to light
Pengelly et al. (2010) J. Exp. Bot.61: 4109-4122
e.g. Flaveria bidentis: harvested over 36 days: acclimation of growth at low irradiance (150
mol.m−2.s−1 vs. 500 mol.m−2.s−1)
Temporal distribution of nitrogen in leaves and
vertical light distribution in the canopy
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ectr
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irra
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μW
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m)
Wavelength (nm)
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Incoming
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Main tiller
Ontologies: definition and concepts
Phenomics and data integration | Dr Xavier Sirault | Page 9
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 are a set of formalised terms that allow one 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
(Uniform Resource Identifier)
Ontology for Biomedical Investigations (OBI)
Data Metadata
Modelling phenomics data/metadata with PODD ontologies (re-usability / interoperability)
In the PODD Ontology every thing is
modelled as objects:
•Experiments (Investigations)
•Plants (Materials)
•Treatments
•Environments
•Measurement Platforms
•Temporal Events
•Raw Data (Data)
•Result of analysis
We then define the relationships between
objects:
•Investigation has Environments
•Material has Observations
•Material references Genotype
•Data references Material (Subset objects)
A semantic web data resource
Challenge: linking “phenotype” to genetic make-ups
How to deal with emerging properties when considering hierarchical data?
Acknowledgements
Robert Furbank (Agriculture flagship)
Jurgen Fripp (DP&S flagship)
Helen Daily (Agriculture)
Peter Kuffner (Agriculture)
Peter Ansell (Agriculture)
Julio Hernandez-Zaragoza (Agriculture)
Dac Nguyen (Agriculture)
Robert Coe (IRRI)
Chuong Nguyen (ANU)
Anthony Paproki (DP&S)
Anne Bernhart (TelecomParis)
Jose Berni-Jimenez (Agriculture)
David Deery (Agriculture)
Michael Salim (Agriculture)
Jamie Scarrow (Agriculture)
Paul Hutchinson (Agriculture)
(lots of students…)
Christophe Pradal (INRIA, CIRAD)
Christian Fournier (INRA)
Christophe Godin (INRIA, CIRAD)
Francois Tardieu (INRA)
Frederic Baret (INRA)
Pascal Neveu (INRA)
Paul Quick (IRRI)
Xinguang Zhu (Plant Systems Biology / CAS)
Justin Borevitz (ANU)
Hamlyn Jones (University of Dundee)
Take-home messages
• Phenotypes/phenes are dynamic –> increased dimensionality = increased complexity
• What metadata to record: essential for the discovery of how these phenes/phenotypes
interact with their environment
• Phenomics data is “BIG data” and requires sophisticated informatics to transform data
into information (digital acquisition of data not rate limiting) – distinction to be made
between observable phenotypes and genetic traits
Data analysis and integration of data into modelling platform (data assimilation –
which methods? Neural networks, Look-up table, Marcov Chain Monte Carlo) to
address the issue of different level of spatial and temporal resolution
Statistical treatment of time resolved data is a challenge (poorly developed
statistics and methods to integrate environmental data
• Building a data fabric: transformational capability by facilitating data exchange and re-
usability (eg Phenomics Ontology Driven Data repository PODD)