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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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)