# Plant Data Managment for Phenotyping Experiments

Cyril Pommier, Anne-Françoise Adam-Blondon, Célia Michotey

► To cite this version:

Cyril Pommier, Anne-Françoise Adam-Blondon, Célia Michotey. Plant Data Managment for Phenotyping Experiments: Data standards and use cases for plant scientists and informaticians. École thématique. France. 2020. hal-03102944

HAL Id: hal-03102944

https://hal.inrae.fr/hal-03102944

Submitted on 7 Jan 2021

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Plant Data Managoment for Phenotyping Experiments

Data standards and use cases for plant scientists
and informaticians

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# Overview

Plant Phenotyping standards : why and who
MIAPPE overview and Crop & Forest use case

Questions

Crop ontology

Questions

MIAPPE tools & web services: BrAPI, PPEO Ontology

Questions

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PLANT PHENOTYPING STANDARDS : WHY

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# Why should we standardize phenotyping data?

* To enable anyone (including yourself) to reuse it: **metadata about the experiment** (who did it, for what purpose, where and how)

* To enable the (automatic) integration with other types of data: **unique identification of the concepts used to link different data sets**

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* To enable knowledge discovery: **metadata about the experiment, controlled vocabularies, ontologies**
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> Genome / Environment / Phenome

Genetics & Genomics

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Homogenous (Mostly)
Central Access (Mostly)

Phenomics

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Heterogenous
Distributed

Plant Breeding
Genetic variations by Traits

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Climate Change Studies
Genotype by Environment
Phenology

Environment

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Heterogenous
Distributed

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> Europe open access and interoperability policy

Open Access

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FAIR

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"Facilitating access to results encourages the re-use of research outputs and supports Open Science. This is essential for Europe's ability to enhance its economic performance and improve the capacity to compete through knowledge. [...] Results of publicly-funded research can therefore be disseminated more broadly and faster, to the benefit of researchers, innovative industry and citizens.

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All funded projects are asked to add a WP supporting a FAIR
compliant Data Management Plan

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## 5 stars Open Data

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Progressing towards FAIR and Open Data requires a multidisciplinary cooperation :

- Biologists

- Bioinformaticians

- Specialists of ontologies/semantics

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FAIR Data Principles

Wilkinson et al., The FAIR Guiding Principles for
scientific data management and stewardship.
*Scientific Data 3 (2016)*

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FINDABLE

Unique identifiers
and metadata are
used to allow
data to be located
quickly and
efficiently

Ids
Metadata
Index

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ACCESSIBLE

Data is open, free
and universally
available for
research
discovery efforts

Open Protocole
Perennial
Metadata

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INTER-
OPERABLE

A common
programming
language is used
to allow use in a
broad range of
applications

Semantics
Linked Data
Vocabularies

REUSABLE

All data is clearly
described and
outlines associated
data-use standards

License
Well described
Provenance
Standards
Plant phenomics Data

* Heterogenous data

* Different spatial scales

* Metabolites

* organ (leaf)

* group of plants

* whole experimental field

* Different time scales

* Single measure

* Time series : every 15 minutes

* Environment

* Complex life cycle

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# Phenotyping data life cycle

## Raw data

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<table><thead><tr><td>Genotype</td><td>Treatment</td><td>N input</td><td>Date</td><td>Rep</td><td>Fusariose</td></tr></thead><tbody><tr><td>Soissons</td><td>low input</td><td>15,32253129</td><td>15/11/2011</td><td>1</td><td>5</td></tr><tr><td>Soissons</td><td>low input</td><td>15,31430556</td><td>16/11/2011</td><td>2</td><td>7</td></tr></tbody></table>

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## Elaborated data

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<table><thead><tr><td>Genotype</td><td>Treatment</td><td>Fusariose</td></tr></thead><tbody><tr><td>Soissons</td><td>low input</td><td>6</td></tr></tbody></table>

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# Plant Phenotyping Life cycle

## Raw data long term conservation

### Data acquisition

* Traceability

* Raw measures

* Data Cleaning

* Platform IS (Emphasis IS, PHIS, ...)

* Reproducibility

* Traceability

* Provenance

### Data computation

* New computation for each scientific question

* One raw dataset → many computed datasets

### Data publication

* One Data Publication by datasets.

* Platform IS

* Raw

* FAIR Data Repositories

* Computed

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Data

Date

<table><thead><tr><th>Genotype</th><th>traitement</th><th>Fusariose</th></tr></thead><tbody><tr><td>Wasson</td><td>low input</td><td>5</td></tr><tr><td>Wasson</td><td>high</td><td>1</td></tr><tr><td>Charger</td><td>low input</td><td>1</td></tr><tr><td>Charger</td><td>high N</td><td>2</td></tr></tbody></table>

Variety charger

Knowledge

Intensive cultural practice
> Challenges for phenomics data

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**Findable**

* Distributed model: no central phenomic data archive

* Agreed or compatible ID and Metadata policy

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**Accessible**

* Complex life cycle

* Different types of data

* Complex DMP

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**Interoperable**

* Compatible standards

* Issues in relation with big data

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**Reusable**

* Phenotype = Genotype x Environment x Cultural practice => different silos of metadata definition

* Provenance is complex

**Data portals**

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FAIDARE

**Data Archives**

eIDAL
electronic data archive library

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Data

INRAO

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BrAPI

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Crop Ontology
for agricultural data

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MCPD

**Platform IS**

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...
> PLANT DATA STANDARDS : WHO

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## Interoperability in International network

### National Networks

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ifb

de

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NBI

GERMAN NETWORK FOR BIOINFORMATICS INFRASTRUCTURE

PHENOME
French Plant
Phenomics Infrastructure

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lomaizing

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FRANCE
GRILLES

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Breed

wheat

### Global Networks

WheatIS

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CGIAR

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WHEAT
INITIATIVE

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### European Networks

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### International data standards

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BrAPI

Web services

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RESEARCH DATA ALLIANCE

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Minimal information

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Crop Ontology
for agricultural data

Controlled vocabularies

Trait dictionaries

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MCPD

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Biodiversity
International

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European
Plant
Phenotyping
Network

EPPN
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# Plant data standards contributors

Gathered to solve interoperability standard problem

* ELIXIR

* European Infrastructure for life sciences

* Germany, France, Belgium, Italy, Portugal, UK, Netherland, Slovenia, ...

* https://elixir-europe.org/communities/plant-sciences

* CGIAR

* Consortium of International Agricultural Research Centers

* https://www.cgiar.org/

* EMPHASIS

* European Infrastructure for Plant Phenotyping

* France, Germany, Belgium, UK, ...

* https://www.plant-phenotyping.eu

* NAPPN

* North American Plant Phenotyping Network

* https://nappn.plant-phenotyping.org/

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> Community driven recommendations

* WheatIS:
http://wheatis.org/DataStandards.php

Wheat Data Interoperability Guidelines

Home

Guidelines v

Ontologies & Vocabularies

Use cases v

Getting involved

About v

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Sequence variations

Genome annotations

Phenotypes

Germplasm

Gene expression

Physical Maps

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Wel

These recommendations have been prepared by members of the Wheat Data Interoperability Working Group (WG), one of the WGs of the Research Data Alliance and the only WG of the Agriculture Data Interoperability Interest Group. The group is coordinated by members of the Wheat Initiative, a global

* Community story

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> PLANT DATA STANDARDS

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> Data standards for FAIR

Semantic

* Description of the data

* Controlled vocabularies: term name and definitions

* Ontologies: semantic links between terms

* Biologist driven

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Structure

* Formatting and Organizing the data

* Data Models

* Standards : CSV, VCF, GFF, MIAPPE (www.miappe.org), etc...

* Biologist & Computer scientist driven

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Persistent Unique
Identifiers

URI, gene ID,
accessions ID, Trait
ID, DOI,...

Technical

* Data integration and sharing

* Interoperability : tools and systems

* GA4GH

* Breeding API www.brapi.org

* Computer scientist driven

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AgroPortal
LIRMM

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Ontology Lookup Service

Home

Ontologies

Documentation

About

> Phenotype Semantic Standard: Ontologies

* Describing traits/features in specific plant species

* Crop Ontology Trait + Method + Scale Semantic model

* Dedicated presentation
> **Phenotype Structure Standard**

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## Minimal Information About Plant Phenotyping Experiment : version 1.1 (Jan 2019)
www.miappe.org

* Many stakeholders

* Elixir, Emphasis, Bioversity, North American PPN

* Open Community:

* Request for comments

* Github Feature requests

* Mailing lists

* Meetings & Workgroups

* Crops and woody plants

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> **Phenotype Structure Standard : MIAPPE**

Minimum Information for Biological and Biomedical Investigations

A collection of the historical MIBBI foundry reporting guidelines. The minimum information standard is a set of guidelines for reporting data derived by relevant methods in biosciences. If followed, it ensures that the data can be easily verified, analysed and clearly

* Biologist Friendly

* Minimal and sufficient list of metadata:

* The objective of the experiment

* Who contributed to the experiment

* What were the experimental procedures

* What was the biological material experimented

* ...

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# Phenotype Structure Standard

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miappe

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Computer scientist friendly :

* Explicit Data Model: ISA-Tools and Breeding API (BrAPI) compatibility.

* Validation framework and toolbox

* Semantic representation in OWL: Phenotyping Experiment Ontology (PPEO) using OWL (http://agroportal.lirmm.fr/ontologies/PPEO)

Biologist friendly

* Clear definitions and examples

* Excel templates

* Trainings

Specification with multiple implementations

* File Archive: ISA Tab

* Semantic: Plant Phenotyping Experiment Ontology

* Web Service: Breeding API

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Papoutsoglou et al. (2020) Enabling reusability and interoperability of plant phenomic datasets with MIAPPE 1.1. New Phytol, 227:260-273; https://doi.org/10.1111/nph.16544

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> **Phenotype Technical Standard**

* Breeding API http://brapi.org/

* International collaboration

* Standard Open Web Service API

* Information Exchange, Main target: Breeding

* Excellence in Breeding platform (CGIAR, Peter Selby)

* Major Elixir, Emphasis Contribution

* Phenotyping specification

* Connect data repositories and tools:

* Genotype visualization (Flapjack)

* Studies graph preview and filtering

* BrAPPS: Tools integrable in any BrAPI compliant System

* https://www.brapi.org/brapps.php

* R analysis environment

* Field data capture

* FAIR Data discovery → Elixir FAIDARE

BrAPI

T3

CIRAD

GOBII

Wageningen

Cornell

iPlant

BILL & MELINDA
GATES foundation

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CGIAR

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Biodiversity
International

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INRA
SCIENCE & IMPACT

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The James
Hutton
Institute

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WAGENINGEN
UNIVERSITY & RESEARCH

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Selby et al. Bioinformatics (2019),
doi.org/10.1093/bioinformatics/btz190

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> Questions

Plant Phenotyping standards : why and who

**MIAPPE overview through Crop & Forest use case**

Crop ontology

MIAPPE tools & web services: BrAPI, PPEO Ontology

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> MINIMUM INFORMATION ABOUT A PLANT PHENOTYPING EXPERIMENT

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New Phytologist

Methods

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Open Access

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Enabling reusability of plant phenomic datasets with MIAPPE 1.1

Evangelia A. Papoutsoglou ✉, Daniel Faria, Daniel Arend, Elizabeth Arnaud, Ioannis N. Athanasiadis, Inês Chaves, Frederik Coppens, Guillaume Cornut, Bruno V. Costa, Hanna Ćwiek-Kupczyńska, Bert Droesbeke, Richard Finkers, Kristina Gruden, Astrid Junker, Graham J. King, Paweł Krajewski, Matthias Lange, Marie-Angélique Laporte, Célia Michotey, Markus Oppermann, Richard Ostler, Hendrik Poorter, Ricardo Ramírez-Gonzalez, Živa Ramšak, Jochen C. Reif, Philippe Rocca-Serra, Susanna-Assunta Sansone, Uwe Scholz, François Tardieu, Cristobal Uauy, Björn Usadel, Richard G. F. Visser, Stephan Weise, Paul J. Kersey, Célia M. Miguel, Anne-Françoise Adam-Blondon, Cyril Pommier ✉... See fewer authors ^

MIAPPE Overview and use cases

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# Capturing important information about phenotyping experiments using the MIAPPE standard

## Two examples to illustrate how to capture important information about the phenotyping experiments:

* **MAIZE**:[1] Millet *et al*. 2019 (https://doi.org/10.15454/IASSTN): A multi-site experiment in a network of European fields for assessing the maize yield response to environmental scenarios.

* **POPLAR**:[2] Monclus *et al*. 2012 (http://dx.doi.org/10.1186/1471-2229-12-173): Integrating genome annotation and QTL position to identify candidate genes for productivity, architecture and water-use efficiency in Populus spp

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# Millet et al 2019 [1] - Material&methods section on phenotyping experiments

* A panel of **256 maize hybrids** was grown with **two water regimes** (irrigated or rainfed).

* **Location:** seven fields in 2012 and 2013, plus one site in Chile in 2013

* This resulted in 29 experiments defined as the combination of one year, one site and one **water regime**, with two and three **repetitions** for rainfed and irrigated treatments, respectively.

* A detailed **environmental characterisation** was carried out, with hourly records of micrometeorological data and soil water status, and associated with precise measurement of phenology.

* «grain.yield»: yield adjusted at 15% grain moisture, in ton per hectare (t ha-1). «grain.number»: number of grain per square meter. «grain.weight»: individual grain weight (mg). «anthesis»: male flowering (pollen shed), in thermal time cumulated since emergence (d20°C). «silking»: female flowering (silking emergence), in thermal time cumulated since emergence (d20°C). «plant height»: plant height, from ground level to the base of the flag leaf (highest) leaf (cm). «tassel height»: plant height including tassel, from ground level to the highest point of the tassel (cm). «ear height»: ear insertion height, from ground level to ligule of the highest ear leaf (cm).

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Monclus et al 2012 [2] - Material&methods section on
phenotyping experiments

* **3 Field trial** established in April 2003 **located** in France (Ardon, 47°49'41"N, 1°54'39"E, 110 m), Italy Cavallermaggiore ((44° 43' 0" N), (7° 41' 0" E)), UK Headley ((51° 7' 0" N), (-1° 10' 0" W))

* The **biological material** consisted of a cloned 336 F1 progeny from an interspecific cross between the female *Populus deltoides* (Bartr. Ex Marsh.) '73028-62' from Illinois and the male *P. trichocarpa* (Torr. and Gray) '101-74' from Washington State.

* The **trial was established** from 25 cm- homogenous hardwood cuttings planted at a plant density of 6670 trees per ha. The trial was and consisted in 6 randomized complete blocks where each F1 genotype and each parent was represented by one replicate.

* **Circumference and stem height** were measured at the end of the first (winter 2003–2004) and second (winter 2004–2005) as described in Dillen et al. Forest Ecol Manag. 2007, 252 (1–3): 12-23). Growth increment in height and circumference during the second growing season were calculated.

* **Leaf traits** where measured in 2003: one fully illuminated mature leaf was collected on each tree according to Monclus et al. [http://doi.org/10.1111/j.1469-8137.2005.01407.x](http://doi.org/10.1111/j.1469-8137.2005.01407.x)). Six calibrated discs of lamina were cut from this leaf, dried at 50 °C during 48 °C and weighed, and specific leaf area (SLA, cm² g⁻¹) was computed. Leaf discs were ground to fine powder for analysis of leaf carbon isotope composition (δ¹³C), carbon (Cₚ) and nitrogen (Nₚ) contents. One-milligram subsamples of ground material were used for measuring the CO₂ produced by combustion and its ¹³CO₂/¹²CO₂ ratio by a continuous flux isotope ratio mass spectrometer. The discrimination between atmospheric CO₂ and plant material was calculated.
> **MIAPPE 1.1 Overview**

Typical Dataset: Monclus *et al.*, 2012, [2]

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6 randomized blocks

1 observation unit = one tree

No treatment

6 replicates defined by their position in each block: row and column
> MIAPPE V1.1 Overview
the (ISA) backbone

* **Investigation**: whole dataset

* **Study**: one experiment in one location for one to several year

* **Assay**: Level + Trait + Method + Scale/Unit

* Level:

* Plant

* Microplot

* Block

* Trial

* ...

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# MIAPPE V1.1 Overview
## Data file content

* Any format (Near Infra Red Spectrum, Images, Image Archives references, ...)

* Mostly tabular

* Metadata on each column header

<table><thead><tr><th>A</th><th>B</th><th>C</th><th>D</th><th>E</th><th>F</th><th>G</th><th>H</th><th>I</th></tr><tr><th>Accession Number</th><th>Trial Site</th><th>Campaign</th><th>Circum1: Tree circumference at 1 year</th><th>Date [Circum1]</th><th>Height1: Tree total height at 1 year</th><th>Date [Height1]</th><th>Shoots3: Number of resprouts at 3 years</th><th>Date [Shoots3]</th></tr></thead><tbody><tr><td>661300270</td><td>Ardon</td><td>2004</td><td>45.645632645603683</td><td>12/01/2004</td><td>284.3</td><td>12/01/2004</td><td></td><td></td></tr><tr><td>661300270</td><td>Ardon</td><td>2005</td><td></td><td></td><td></td><td></td><td>14.630625</td><td>12/05/2005</td></tr><tr><td>661300444</td><td>Ardon</td><td>2004</td><td>38.96112577281653</td><td>12/01/2004</td><td>228.8</td><td>12/01/2004</td><td></td><td></td></tr><tr><td>661300444</td><td>Ardon</td><td>2005</td><td></td><td></td><td></td><td></td><td>8.5030559999999991</td><td>12/05/2005</td></tr><tr><td>661300312</td><td>Cavallermaggiore</td><td>2004</td><td>52.4</td><td>01/01/2004</td><td>249.9</td><td>01/01/2004</td><td></td><td></td></tr><tr><td>661300312</td><td>Cavallermaggiore</td><td>2005</td><td></td><td></td><td></td><td></td><td>12.981609000000001</td><td>01/05/2005</td></tr><tr><td>661300371</td><td>Cavallermaggiore</td><td>2004</td><td>45.74</td><td>01/01/2004</td><td>230.2</td><td>01/01/2004</td><td></td><td></td></tr><tr><td>661300371</td><td>Cavallermaggiore</td><td>2005</td><td></td><td></td><td></td><td></td><td>10.3041</td><td>01/05/2005</td></tr><tr><td>661300487</td><td>Cavallermaggiore</td><td>2004</td><td>72.52</td><td>01/01/2004</td><td>309.8</td><td>01/01/2004</td><td></td><td></td></tr><tr><td>661300487</td><td>Cavallermaggiore</td><td>2005</td><td></td><td></td><td></td><td></td><td>10.679823999999998</td><td>01/05/2005</td></tr><tr><td>661300585</td><td>Cavallermaggiore</td><td>2004</td><td>71.739999999999995</td><td>01/01/2004</td><td>305.7</td><td>01/01/2004</td><td></td><td></td></tr><tr><td>661300585</td><td>Cavallermaggiore</td><td>2005</td><td></td><td></td><td></td><td></td><td>10.9561000000000001</td><td>01/05/2005</td></tr><tr><td>661300468</td><td>Headley</td><td>2004</td><td>45.27</td><td>01/01/2004</td><td></td><td>247</td><td>01/01/2004</td><td></td></tr><tr><td>661300468</td><td>Headley</td><td>2005</td><td></td><td></td><td></td><td></td><td>15.888196000000002</td><td>01/05/2005</td></tr><tr><td>661300469</td><td>Headley</td><td>2004</td><td>70.9300000000000007</td><td>01/01/2004</td><td></td><td>313</td><td>01/01/2004</td><td></td></tr><tr><td>661300469</td><td>Headley</td><td>2005</td><td></td><td></td><td></td><td></td><td>13.271448999999999</td><td>01/05/2005</td></tr><tr><td>661300533</td><td>Headley</td><td>2004</td><td>57.67</td><td>01/01/2004</td><td>258.8</td><td>01/01/2004</td><td></td><td></td></tr></tbody></table>
> **MIAPPE main sections – Investigation**

* **Investigations are research programmes with defined aims.** They can exist at various scales: e.g. grant-funded programme of work with various published components; a single experiment.

* One investigation holds one to many studies.

* **Metadata**

* Similar to Archive deposition

* DOI, title, description, associated publications/people, ...

* **Examples Investigation :**

* MAIZE [1]: the whole set of multilocal and pluriannual phenotyping experiments

* POPLAR [2]: the whole set of multilocal measurements over three years
<figure><img src="image_129.png" /></figure>

# MIAPPE main sections – Study

## A "study" (or experiment)

* One study = one location

* comprises a series of assays (or measurements) undertaken to answer a particular biological question.

## Metadata

* experiment as a whole

* timing, location, statistical design, cultural practices (but not event lists), etc...

## Examples of Studies:

* MAIZE [1] : 37 studies: year x location x treatment (Gaillac 2012 rain, Gaillac 2013 watered, ...)

* POPLAR [2] : 3 locations over one to several years (Ardon_2003) (Ardon_2003-2005)

<figure><img src="image_130.png" /></figure>
<figure><img src="image_131.png" /></figure>

# MIAPPE v1.1 Study Metadata

* Poplar Study

* In that example Study = Trial

<figure><img src="image_132.png" /></figure>

<table><thead><tr><th>Accession Number</th><th>Trial Site</th><th>Campaign</th><th>Circum1: Tree circumference at 1 year</th><th>Date [Circum1]</th><th>Height1: Tree total height at 1 year</th><th>Date [Height1]</th><th>Shoots3: Number of resprouts at 3 years</th><th>Date [Shoots3]</th></tr></thead><tbody><tr><td>66130027</td><td>Ardon</td><td>2004</td><td>45.645632645603683</td><td>12/01/2004</td><td>284.3</td><td>12/01/2004</td><td></td><td></td></tr><tr><td>66130027</td><td>Ardon</td><td>2005</td><td></td><td></td><td></td><td>14.630625</td><td></td><td>12/05/2005</td></tr><tr><td>66130044</td><td>Ardon</td><td>2004</td><td>38.96112577281653</td><td>12/01/2004</td><td>228.8</td><td>12/01/2004</td><td></td><td></td></tr><tr><td>66130044</td><td>Ardon</td><td>2005</td><td></td><td></td><td></td><td>8.5030559999999991</td><td></td><td>12/05/2005</td></tr></tbody></table>
> **MIAPPE main sections – Biological material**

* **Biological material being studied**

* Plus its source (stock center, gen bank, etc...).

* **_Crucial_** for integrating phenotyping data with genomic or genetic data.

* **Metadata**

♦ Minimal fields from Multicrop Passport Descriptor (MCPD) standard

♦ GPS location for forest tree / in situ material provenance

**Source of the material used:**
accession, cultivar/variety, region
of provenance, laboratory cross, ...

<figure><img src="image_133.png" /></figure>

**Biological material used in the study:** seed lot, cuttings...

<figure><img src="image_134.png" /></figure>

**Plant Samples used in the study:** detached leaves, ...

**MCPD identification system:**

* **Genebank/Lab + Species + accession number (mandatory)**

* **DOI**

* **Lab + internal accession number (mandatory)**

* **URI**

* **Lab + internal accession number (mandatory)**

* **BioSample ID**
<figure><img src="image_135.png" /></figure>

# MIAPPE main sections – Biological material

## Plant Material

* Identification

* Description

## Multi Crop Passport Descriptor

<figure><img src="image_136.png" /></figure>

<table><thead><tr><th>Accession Number</th><th>Trial Site</th><th>Campaign</th><th>Circum1: Tree circumference at 1 year</th><th>Date [Circum1]</th><th>Height1: Tree total height at 1 year</th><th>Date [Height1]</th><th>Shoots3: Number of resprouts at 3 years</th><th>Date [Shoots3]</th></tr></thead><tbody><tr><td>661300270</td><td>Ardon</td><td>2004</td><td>45.645632645603683</td><td>12/01/2004</td><td>284.3</td><td>12/01/2004</td><td></td><td></td></tr><tr><td>661300270</td><td>Ardon</td><td>2005</td><td></td><td></td><td></td><td>14.630625</td><td></td><td>12/05/2005</td></tr><tr><td>661300444</td><td>Ardon</td><td>2004</td><td>38.96112577281653</td><td>12/01/2004</td><td>228.8</td><td>12/01/2004</td><td></td><td></td></tr><tr><td>661300444</td><td>Ardon</td><td>2005</td><td></td><td></td><td></td><td>8.5030559999999991</td><td></td><td>12/05/2005</td></tr></tbody></table>
# MIAPPE main sections – Observed Variable (Assay)

## An Observed Variable (assay)

* specific measurement.

* Targets a trait

* with a method and a scale.

* Trait: Phenotype or Environment

## Metadata

* Crop Ontology Trait Dictionary:

* trait, method, scale

## Example:

### In each study of Millet *et al.* [1] :

* Plant level: 20 variables

* Genotype/ Study level: 19 variable both phenotype and environment

* Plot level: 9 variables

* E.g. Female flowering days to silking D20deg

* E.g. Plant height (cm)

**FFLW_D20deg:** Female flowering days to silking D20deg **VARIABLE**

**Synonyms** Female flowering days to silking D20deg FFLW

**Growth stage** Flowering

**Crop** Maize

**Silking time** **TRAIT**

**Identifier** CO_322:0000031

**Name** Silking time

**Description** Silking time

**Synonyms** Female flowering time

**Main abbreviation** Silk

**Alternative abbreviations** S

**Entity** FFlw

**Attribute** Flower

**Class** Silking time

**Class** Phenological

**Thermal time between emergence and silking – Computation** **METHOD**

**Identifier** MIPO:0000027

**Name** Thermal time between emergence and silking – Computation

**Description** Calculated as equivalent days at 20 °C unit between emergence and 50% anthesis.

**Reference** B. Parent, O. Turc, Y. Gibon, M. Stitt and F. Tardieu (2010) Modelling temperature-compensated physiological rates, based on the co-ordination of responses to temperature of developmental processes. Journal of Experimental Botany

**Class** Computation

**D20deg:** days equivalent time at 20 °C **SCALE**

**Identifier** MIPO:0000030

**Name** D20deg: days equivalent time at 20 °C

**Data type** Numerical

**PTHT:** Plant height (cm) **VARIABLE**

**Ontology name** Maize Traits

**Identifier** MIPO:0000006

**Name** PTHT

**Synonyms** Plant height (cm)

**Xref** CO_322:00000994

**Crop** Maize

**Plant height** **TRAIT**

**Identifier** CO_322:00000994

**Name** Plant height

**Description** Plant height from the base to the top part (in reproductive stages to the top of the tassel).

**Main abbreviation** PH

**Entity** Plant

**Attribute** height

**Class** Agronomical

**PH - Measurement** **METHOD**

**Identifier** CO_322:00000995

**Name** PH - Measurement

**Description** Recommended to take multiple plants and measure the height from the base of a plant to the top of the tassel, enter the data individually in the FieldBook and calculate the average.

**Reference** DTMA drought phenotyping protocol. 2009. CIMMYT. Magorokosho et al. 2010. Characterization of maize germplasm grown in eastern and southern Africa: Results of the 2009 regional trials coordinated by CIMMYT. Zimbabwe
# MIAPPE v1.1 Metadata

## Observed Variable: Trait + Method + Scale

### Traits, methods and scales

<figure><img src="image_137.png" /></figure>

English

<figure><img src="image_138.png" /></figure>

### HT: Tree total height [VARIABLE]

<figure><img src="image_139.png" /></figure>

<table><tr><td>Ontology name</td><td>Woody Plant Ontology</td></tr><tr><td>Identifier</td><td>CO_357:0000048</td></tr><tr><td>Name</td><td>HT</td></tr><tr><td>Tree height [TRAIT]</td><td></td></tr><tr><td>Identifier</td><td>CO_357:1000037</td></tr><tr><td>Name</td><td>Tree height</td></tr><tr><td>Description</td><td>Total height of the tree, from the ground to the tallest part of the crown</td></tr><tr><td>Main abbreviation</td><td>HT</td></tr><tr><td>Alternative abbreviations</td><td>Height<br>HP<br>HPL<br>h<br>H<br>TH<br>Height.F<br>Height.UK<br>Height.I</td></tr><tr><td>Entity</td><td>tree</td></tr><tr><td>Attribute</td><td>height</td></tr><tr><td>Status</td><td>Standard for INRA & GenTree project</td></tr><tr><td>Class</td><td>Morphological</td></tr><tr><td>Tree height protocol [METHOD]</td><td></td></tr><tr><td>Identifier</td><td>CO_357:2000027</td></tr><tr><td>Name</td><td>Tree height protocol</td></tr><tr><td>Description</td><td>Measured from soil to basis of the apical meristem or bud (depending of time in season) with a pole or a clinometer</td></tr><tr><td>Reference</td><td>GenTree_protocols_0.99.pdf page 16, https://en.wikipedia.org/wiki/Tree#Height</td></tr><tr><td>Class</td><td>Measurement</td></tr><tr><td>cm [SCALE]</td><td></td></tr><tr><td>Identifier</td><td>CO_357:3000107</td></tr><tr><td>Name</td><td>cm</td></tr></table>

<figure><img src="image_140.png" /></figure>

Assay
(Observed variable)

<table><thead><tr><th>A</th><th>B</th><th>C</th><th>D</th><th>E</th><th>F</th><th>G</th><th>H</th><th>I</th></tr></thead><tbody><tr><td>Accession Number</td><td>Trial Site</td><td>Campaign</td><td>Circum1: Tree circumference at 1 year</td><td>Date [Circum1]</td><td>Height1: Tree total height at 1 year</td><td>Date [Height1]</td><td>Shoots3: Number of resprouts at 3 years</td><td>Date [Shoots3]</td></tr><tr><td>661300270</td><td>Ardon</td><td>2004</td><td>45.645632645603683</td><td>12/01/2004</td><td>284.3</td><td>12/01/2004</td><td></td><td></td></tr><tr><td>661300270</td><td>Ardon</td><td>2005</td><td></td><td></td><td></td><td></td><td>14.630625</td><td>12/05/2005</td></tr><tr><td>661300444</td><td>Ardon</td><td>2004</td><td>38.96112577281653</td><td>12/01/2004</td><td>228.8</td><td>12/01/2004</td><td></td><td></td></tr><tr><td>661300444</td><td>Ardon</td><td>2005</td><td></td><td></td><td></td><td></td><td>8.5030559999999991</td><td>12/05/2005</td></tr></tbody></table>
> MIAPPE main sections – Observation Unit, Samples

* **Observation units**

* objects in the study

* Observations/measures are made on observation units

* Treatments values are made on observation units

* Also used for environmental variables.

* **Metadata are specific to MIAPPE: identifiers, location, replication, treatments, ...**

* **A sample**

* portion of plant tissue *extracted from an observation unit*

* sub-plant observations and/or molecular studies.

* **Metadata: identifiers, information about processing, ...**
> **MIAPPE main sections – Observation Unit, Samples**

**Example: Monclus *et al.*, 2012, [2]**

<figure><img src="image_141.png" /></figure>

6 randomized blocks

1 observation unit = one tree

No treatment

6 replicates defined by their position in each block: row and column
> **MIAPPE main sections – Observation Unit, Samples**

**Example: Monclus *et al.*, 2012, [2]**

<figure><img src="image_142.png" /></figure>

Different types of processing of the leaf disks depending on the measurement: can be captured by different sets of samples (e.g. if subsampling generates repetitions) or in the method of the observed variable.
<figure><img src="image_143.png" /></figure>

> MIAPPE V1.1 data model – Other Important sections

<figure><img src="image_144.png" /></figure>

<figure><img src="image_145.png" /></figure>
> **MIAPPE main sections – Experimental Factor**

• Biotic or abiotic experimental factor

◆ Its effects are evaluated in the study.

◆ Takes different values in the observation units

• Metadata: name, description and value

• Example : Maize dataset [2]

◆ Two Experimental factors/treatments: Rainfed, Watered

◆ Identified by dedicated metadata

◆ Block organisation

◆ Study the effect of Environment/Drought on the Biological material

<table><thead><tr><th colspan="2">GENOTYPE ID</th><th rowspan="2">TREATMENT<br>water_regime</th><th rowspan="2">Trial Name</th><th rowspan="2">Trial Site</th><th colspan="3">LEVEL</th></tr><tr><th>Accession Number</th><th>Accession<br>Name</th><th>BLOCK</th><th>PLOT</th><th>REPLICATE</th></tr></thead><tbody><tr><td>FR19_H</td><td>FR19_H</td><td>rainfed</td><td>KWS Karlsruhe 2011</td><td>Karlsruhe</td><td>5</td><td>124</td><td>1</td></tr><tr><td>FR19_H</td><td>FR19_H</td><td>rainfed</td><td>KWS Karlsruhe 2011</td><td>Karlsruhe</td><td>10</td><td>250</td><td>3</td></tr><tr><td>FR19_H</td><td>FR19_H</td><td>rainfed</td><td>KWS Karlsruhe 2013</td><td>Karlsruhe</td><td>11</td><td>401503</td><td>2</td></tr><tr><td>FR19_H</td><td>FR19_H</td><td>watered</td><td>KWS Karlsruhe 2012</td><td>Karlsruhe</td><td>23</td><td>400353</td><td>1</td></tr><tr><td>FR19_H</td><td>FR19_H</td><td>watered</td><td>KWS Karlsruhe 2011</td><td>Karlsruhe</td><td>2</td><td>6</td><td>1</td></tr><tr><td>FR19_H</td><td>FR19_H</td><td>rainfed</td><td>KWS Karlsruhe 2012</td><td>Karlsruhe</td><td>16</td><td>401451</td><td>2</td></tr></tbody></table>
> **MIAPPE main sections – Event**

• **Event**

◆ Discrete occurrence at a particular time

◆ Natural, e.g. rain, unwanted pathogen attack

◆ Cultural practice, e.g. planting, watering, etc.

◆ **Whole study level** or at **the observation unit level**

◆ It is not the studied Factor but an additional information

• **Metadata: name, description, time/date**

• **Examples:** In Poplar, [2]

• the field establishment date, 2003.

• the orchard was subjected to 15mm of rain on March 15, 2012 (fiction).

<figure><img src="image_146.png" /></figure>

<table><thead><tr><th rowspan="2">Study</th><th colspan="3">Event</th></tr><tr><th>Name</th><th>Description</th><th>Date</th></tr></thead><tbody><tr><td>Monclus et al., 2012</td><td>Rain</td><td>15mm of rain on the orchard</td><td>2012-03-15</td></tr></tbody></table>
> **MIAPPE main sections – Environment**

* **Environment parameters**

* Constant throughout the study

* Did not change between observation units or assays.

* Environment characteristics that vary over time, i.e. environmental variables, should be recorded as Observed Variables

* **Metadata :name, value**

* **Example:**

<table><thead><tr><td>Environment parameter</td><td>Environment parameter Value</td></tr></thead><tbody><tr><td>Sowing density</td><td>20 seeds/m2</td></tr><tr><td>Rooting medium composition</td><td>Ca (XEO:00058): 5 mg/L; ...</td></tr></tbody></table>
> Perspectives

* Open community

♦ Github

♦ Mailing list

* Outreach

* Training

* Elixir Service bundles

<figure><img src="image_147.png" /></figure>

<figure><img src="image_148.png" /></figure>
> Questions

Plant Phenotyping standards : why and who
MIAPPE overview through Crop & Forest use case

**Crop ontology**

MIAPPE tools & web services: BrAPI, PPEO Ontology

<figure><img src="image_149.png" /></figure>
<figure><img src="image_150.png" /></figure>

<figure><img src="image_151.png" /></figure>

CROP ONTOLOGY

<figure><img src="image_152.png" /></figure>

<figure><img src="image_153.png" /></figure>

<figure><img src="image_154.png" /></figure>

<figure><img src="image_155.png" /></figure>
## Context

### Need

* Importance of controlled vocabularies and ontologies for data annotation and FAIRness

* Must respect crop specificities

<table><thead><tr><th>Study</th><th>Genotype</th><th>CIR</th><th>HT</th><th>SUR</th></tr></thead><tbody><tr><td>Domaine de Valcros</td><td>6579</td><td>600</td><td>17</td><td>1</td></tr><tr><td>Domaine de Valcros</td><td>6580</td><td>482</td><td>14</td><td>1</td></tr></tbody></table>

<table><thead><tr><th>Study</th><th>Genotype</th><th>H</th><th>CIR</th></tr></thead><tbody><tr><td>La Nerthe</td><td>6579</td><td>23</td><td>650</td></tr><tr><td>La Nerthe</td><td>6580</td><td>28</td><td>514</td></tr></tbody></table>

### Answer

* Development of a standard to build ontologies to annotate phenotypes: "Crop Ontology"

* Crop-specific vocabulary established by its community

* 31 ontologies crop-specific available (including woody plants): www.cropontology.org

Phenotype and Trait Ontology

Bambara groundnut 134 variables LANDRES
version Dec 2019

Banana 370 variables RCRICHTON
Banana Trait Dictionary in template 5 - Biodiversity & IITA - April 2019

Barley 148 variables MALAPORTE
ICARDA - TDv5 - Sept 2018

Barley Trait POLAPGEN Ontology 148 traits HCWI

Barley Trait Ontology template v 4, 6 June 2013 submitted by the Institu-
Plant Genetics Poznan on behalf of Polapgen Consortium Poland

Beet Ontology 369 variables URGI

This ontology was built as part of the AKER project. It describes variable-
used in beet phenotyping (experimental properties and measurement so-
for each institution (INRAE, Geves, ITB) and breeding companies (Florin-
Desprez). Curator: Dorothee Charraud (ADRINORD - URGI) Daphne
Verdelet (Florimond Desprez) - First submission in November 2017.

Brachiaria 82 variables CMEDINA

Brachiaria (forages) ontology TD v5 - Version Oct 2016

Brassica 155 variables WIKTOR

Brassica Trait Ontology (BRaTO) hosts trait information to describe bra-
crop data. Terms are collected from various projects including OREGIN,
RIPR (UK) and Rapsodyn (France). BRATO development is conducted
Earlham Institute (UK), Southern Cross University (Australia) and INRA
(France).

Cassava 245 variables AAFOLABI

Cassava Trait Dictionary in template 5 - IITA - July 2015, updated in
February 2016

Castor bean 75 variables MALAPORTE

March 2017 version

Chickpea 89 variables PRASAD

Chickpea Trait Dictionary in template v5 - ICRISAT - July 2015

Common Bean 184 traits ACUERRERO

CIAT Common bean trait dictionary - version August 2014

Cotton 282 variables JING

Cotton ontology from CottonGen database - June 2019
> Standard model

**Variable** = trait + method + scale

<table><thead><tr><th>Trial</th><th>Genotype</th><th>CIR</th><th>HT</th><th>SUR</th></tr></thead><tbody><tr><td>Domaine de Valcros</td><td>6579</td><td>600</td><td>17</td><td>1</td></tr><tr><td>Domaine de Valcros</td><td>6580</td><td>482</td><td>14</td><td>1</td></tr></tbody></table>

The phenotypic observation

<table><tr><td>Variable ID</td></tr><tr><td>Variable name</td></tr><tr><td>Variable synonyms</td></tr><tr><td>Context of use</td></tr><tr><td>Growth stage</td></tr><tr><td>Variable status</td></tr><tr><td>Variable Xref</td></tr><tr><td>Institution</td></tr><tr><td>Scientist</td></tr><tr><td>Date</td></tr><tr><td>Language</td></tr><tr><td>Crop</td></tr></table>

<figure><img src="image_156.png" /></figure>
> Standard model

$$ \text{Variable} = \text{trait} + \text{method} + \text{scale} $$

<figure><img src="image_157.png" /></figure>

What is the studied character?

<table><tr><td>Trait</td><td>Trait ID</td></tr><tr><td></td><td>Trait</td></tr><tr><td></td><td>Trait class</td></tr><tr><td></td><td>Trait description</td></tr><tr><td></td><td>Trait synonyms</td></tr><tr><td></td><td>Main trait abbreviation</td></tr><tr><td></td><td>Alternative trait abbreviations</td></tr><tr><td></td><td>Entity</td></tr><tr><td></td><td>Attribute</td></tr><tr><td></td><td>Trait status</td></tr><tr><td></td><td>Trait Xref</td></tr></table>
> Standard model

$$ \text{Variable} = \text{trait} + \text{method} + \text{scale} $$

<figure><img src="image_158.png" /></figure>

How is it observed?

<table><thead><tr><th></th><th>Method ID</th></tr></thead><tbody><tr><td rowspan="5">Method</td><td>Method</td></tr><tr><td>Method class</td></tr><tr><td>Method description</td></tr><tr><td>Formula</td></tr><tr><td>Method reference</td></tr></tbody></table>
> Standard model

Variable = trait + method + scale

<table><thead><tr><th>Trial</th><th>Genotype</th><th>CIR</th><th>HT</th><th>SUR</th></tr></thead><tbody><tr><td>Domaine de Valcros</td><td>6579</td><td>600</td><td>17</td><td>1</td></tr><tr><td>Domaine de Valcros</td><td>6580</td><td>482</td><td>14</td><td>1</td></tr></tbody></table>

cm

mm

0 = Alive
1 = Dead
2 = Doubtful

How is it expressed (unit or scale)?

<table><thead><tr><th></th><th>Scale ID</th></tr></thead><tbody><tr><td rowspan="10">Scale</td><td>Scale name</td></tr><tr><td>Scale class</td></tr><tr><td>Decimal places</td></tr><tr><td>Lower limit</td></tr><tr><td>Upper limit</td></tr><tr><td>Scale Xref</td></tr><tr><td>Category 1</td></tr><tr><td>Category 2</td></tr><tr><td>...</td></tr><tr><td>Category n</td></tr></tbody></table>
> Controlled Vocabulary Challenge

Phenotyping/environment variable = *Trait* + *Method* + *Unit/Scale*

Trait

+

Method

+

Unit

M1: Total height

M2: First tassel branch

M3: Last expanded leaf

M4: Youngest growing leaf

U1: cm

We need Species specific CV/Ontologies
BUT
Near infinite number of combination

<figure><img src="image_159.png" /></figure>

<figure><img src="image_160.png" /></figure>

M5: Highest pixel
corresponding to plant

U3: pixel
> Controlled Vocabulary Challenge

Variable annotation

* Using MIAPPE for minimal variable description

• Optional reference to existing crop ontology

- Ad hoc Trait or Method or Scale in MIAPPE dataset

* Focus on Trait for interoperability

<figure><img src="image_161.png" /></figure>
# Controlled Vocabulary Challenge

## Variable annotation Example 1

<table><thead><tr><td>Trait</td><td>Trait description</td></tr></thead><tbody><tr><td>L98AR1</td><td>Latent Period for strain 98AR1</td></tr><tr><td>L93CV1</td><td>Latent Period for strain 93CV1</td></tr><tr><td>Canker LL</td><td>Canker lesion length</td></tr></tbody></table>

<figure><img src="image_162.png" /></figure>
# Controlled Vocabulary Challenge

## Variable annotation Example 2

<table><thead><tr><td>Trait</td><td>Trait description</td></tr></thead><tbody><tr><td>L98AR1</td><td>Latent Period for strain 98AR1</td></tr><tr><td>L93CV1</td><td>Latent Period for strain 93CV1</td></tr><tr><td>Canker_LL</td><td>Canker lesion length</td></tr></tbody></table>

<table><thead><tr><th>VARIABLE</th><th>VARIABLE</th></tr></thead><tbody><tr><td>Canker length.1: Bacterial<br>canker lesion length 1 year<br>after inoculation</td><td>Canker length.2: Bacterial<br>canker lesion length 2 years<br>after inoculation</td></tr></tbody></table>

<table><thead><tr><th colspan="2">Traits, methods and scales</th></tr></thead><tbody><tr><td>canker LL</td><td>English</td></tr><tr><td>Woody Plant Ontology ONTOLOGY</td><td></td></tr><tr><td>Biotic stress TRAIT CLASS</td><td></td></tr><tr><td>Canker lesion length TRAIT</td><td></td></tr><tr><td>CHALCOL: Collar Chalara VARIABLE</td><td></td></tr><tr><td>Canker LL: Canker lesion length VARI</td><td></td></tr><tr><td>Canker_LLC: Canker cumulative length</td><td></td></tr><tr><td colspan="2"><strong>Canker LL: Canker lesion length VARIABLE</strong></td></tr><tr><td>Ontology name</td><td>Woody Plant Ontology</td></tr><tr><td>Identifier</td><td>CO_357:0000088</td></tr><tr><td>Name</td><td>Canker_LL</td></tr><tr><td>Synonyms</td><td>Canker lesion length<br>canker_length<br>Canker.length<br>CLL[Adonis]</td></tr><tr><td>Context of use</td><td>Research-intensive characterization<br>Trial evaluation<br>Breeding criterion</td></tr><tr><td>Status</td><td>Standard for INRA</td></tr><tr><td>Institution</td><td>INRA/IBET</td></tr><tr><td>Scientist</td><td>Célia Michotey</td></tr><tr><td>Date</td><td>13/03/2017</td></tr><tr><td>Crop</td><td>WoodyPlant</td></tr></tbody></table><table><thead><tr><th colspan="2">Canker lesion length TRAIT</th></tr></thead><tbody><tr><td>Identifier</td><td>CO_357:1000076</td></tr><tr><td>Name</td><td>Canker lesion length</td></tr><tr><td>Description</td><td>Lesion length (longitudinal extent) of a canker lesion, an area of dead tissue which grow slowly and is caused by bacteria or fungus</td></tr><tr><td>Main abbreviation</td><td>Canker_LL</td></tr><tr><td>Alternative abbreviations</td><td>canker_length<br>Canker.length<br>LCH<br>LCHA<br>LCHAN</td></tr><tr><td>Entity</td><td>trunk</td></tr><tr><td>Attribute</td><td>lesion</td></tr></tbody></table><table><thead><tr><th colspan="2">Canker lesion length protocol METHOD</th></tr></thead><tbody><tr><td>Identifier</td><td>CO_357:2000037</td></tr><tr><td>Name</td><td>Canker lesion length protocol</td></tr><tr><td>Description</td><td>Measured in millimeters with a measuring tape at the inoculation point. Most protocol are using 2 or 3 inoculation points per tree.</td></tr><tr><td>Reference</td><td>https://forgemia.inra.fr/urgi-is...</td></tr><tr><td>Class</td><td>Measurement</td></tr></tbody></table><table><thead><tr><th colspan="2">mm SCALE</th></tr></thead><tbody><tr><td>Identifier</td><td>UO:0000016</td></tr><tr><td>Name</td><td>mm</td></tr><tr><td>Data type</td><td>Numerical</td></tr><tr><td>Decimal places</td><td>0</td></tr><tr><td>Xref</td><td>http://purl.obolibrary.org/obo/...</td></tr></tbody></table>
> Controlled Vocabulary Challenge

Trait decomposition to Entity/Attribute

* Trait = Leaf Area ➔ Entity = Leaf, Attribute = Area

* Enables ad hoc controlled vocabulary + Trait Interoperability

* Formalized by Mungal et al 2009

* Existing in crop ontology

* EMPHASIS proposition : Use for easy Variable creation and interoperability

<figure><img src="image_163.png" /></figure>
<figure><img src="image_164.png" /></figure>

## Tree Variable example1

https://urgi.versailles.inra.fr/ontologyportal#termIdentifier=CO_357:0000019

1 Trait

3 Variables

3 methods

<figure><img src="image_165.png" /></figure>

<table><tr><td>CIR: Tree circumference</td><td>VARIABLE</td></tr><tr><td>Ontology name</td><td>Woody Plant Ontology</td></tr><tr><td>Identifier</td><td>CO_357:0000019</td></tr><tr><td>Name</td><td>CIR</td></tr><tr><td>Synonyms</td><td>Tree circumference<br/>Tree girth<br/>C130<br/>Ci130<br/>CIR[Adonis]</td></tr><tr><td>Context of use</td><td>Research-intensive characterization<br/>Trial evaluation<br/>Breeding criterion</td></tr><tr><td>Status</td><td>Standard for INRA</td></tr><tr><td>Institution</td><td>INRA</td></tr><tr><td>Scientist</td><td>Célia Michotey</td></tr><tr><td>Date</td><td>13/03/2017</td></tr></table>

<table><tr><td>Tree circumference</td><td>TRAIT</td></tr><tr><td>Identifier</td><td>CO_357:1000012</td></tr><tr><td>Name</td><td>Tree circumference</td></tr><tr><td>Description</td><td>Circumference of the tree main stem</td></tr><tr><td>Synonyms</td><td>Tree girth</td></tr><tr><td>Main abbreviation</td><td>CIR</td></tr><tr><td>Alternative abbreviations</td><td>CIRC<br/>Circ<br/>CI<br/>Girth</td></tr><tr><td>Entity</td><td>trunk</td></tr><tr><td>Attribute</td><td>circumference</td></tr><tr><td>Status</td><td>Standard for INRA</td></tr><tr><td>Class</td><td>Morphological</td></tr><tr><td>Ribbon 130cm protocol</td><td>METHOD</td></tr><tr><td>Identifier</td><td>CO_357:2000017</td></tr><tr><td>Name</td><td>Ribbon 130cm protocol</td></tr><tr><td>Description</td><td>Measured at 130cm from the ground (approximately breast height) with a graduated ribbon</td></tr><tr><td>Class</td><td>Measurement</td></tr><tr><td>mm</td><td>SCALE</td></tr><tr><td>Identifier</td><td>UO:0000016</td></tr><tr><td>Name</td><td>mm</td></tr><tr><td>Data type</td><td>Numerical</td></tr><tr><td>Xref</td><td>http://purl.obolibrary.org/obo/...</td></tr></table>

<figure><img src="image_166.png" /></figure>
## Tree Variable example2

https://urgi.versailles.inra.fr/ontologyportal#termIdentifier=CO_357:0000082

### Traits, methods and scales

<figure><img src="image_167.png" /></figure>

English

<figure><img src="image_168.png" /></figure>

survival

<figure><img src="image_169.png" /></figure>

Woody Plant Ontology ONTOLOGY

<figure><img src="image_170.png" /></figure>

Stress TRAIT CLASS

<figure><img src="image_171.png" /></figure>

Survival TRAIT

ESDET: Detailed health status VARIABLE

SUR: Survival VARIABLE

SUR_Rate: Survival rate VARIABLE

<figure><img src="image_172.png" /></figure>

Terminal bud status TRAIT

<figure><img src="image_173.png" /></figure>

Withered leaves TRAIT

<figure><img src="image_174.png" /></figure>

<figure><img src="image_175.png" /></figure>

### SUR: Survival VARIABLE

Ontology name Woody Plant Ontology

Identifier CO_357:0000082

Name SUR

Synonyms

Survival
Health status
ES
S
MOR
SUR[Adonis]

Survival TRAIT

Identifier CO_357:1000070

Name Survival

Description Assessment of the survival state of the tree

Context of use Trial evaluation

Synonyms

Health status

Status Standard for INRA

Main abbreviation

SUR

Institution INRA/IBET

Alternative abbreviations

S

SR

Scientist Célia Michotey

Survival

Survive

Date 13/03/2017

Etat sanitaire

Crop WoodyPlant

ES

Vivants

MOR

Entity tree

Attribute survival

Status Standard for INRA

Class Stress

Visual scoring METHOD

Identifier CO_357:2000003

Name Visual scoring

Description Visual assessment with a reference scoring scale

Class Estimation

Survival scoring scale SCALE

Identifier CO_357:3000036

Name Survival scoring scale

Data type Nominal

Decimal places 0

Min 0

Max 2

Categories
0 = Alive
1 = Dead
2 = Doubtful
## Environnemental Variable example

<figure><img src="image_176.png" /><figcaption>Variable annotation</figcaption></figure>
> Crop Variable example

<figure><img src="image_177.png" /><figcaption>TKW: Thousand kernel weight (g) VARIABLE</figcaption></figure>

<figure><img src="image_178.png" /></figure>

Ad Hoc Variable

Trait: reference Crop Ontology
Maize
> Crop Ontology repositories and workflow

<figure><img src="image_179.png" /></figure>

GitHub

Version History

<figure><img src="image_180.png" /></figure>

Trait Dictionary Template v5

<figure><img src="image_181.png" /></figure>

Crop Ontology

for agricultural data

Repository, some crop ontologies

<figure><img src="image_182.png" /></figure>

CGIAR

<figure><img src="image_183.png" /></figure>

elixir
INRA
SCIENCE & IMPACT

<figure><img src="image_184.png" /></figure>

AgroPortal

LIRMM

Repository, some other crop ontologies

<figure><img src="image_185.png" /></figure>

O
L
S

EMBL-EBI

<figure><img src="image_186.png" /></figure>

Search over several ontologies

<figure><img src="image_187.png" /></figure>

BrAPI

<figure><img src="image_188.png" /></figure>

Plant scientist friendly browser

https://urgi.versailles.inra.fr/ontologyportal

<figure><img src="image_189.png" /></figure>
> Questions

Plant Phenotyping standards : why and who
MIAPPE overview through Crop & Forest use case
Crop ontology

**MIAPPE tools & web services: BrAPI, PPEO Ontology**

<figure><img src="image_190.png" /></figure>
> MIAPPE TOOLS & WEB SERVICES: BRAPI, PPEO ONTOLOGY

<figure><img src="image_191.png" /></figure>

<figure><img src="image_192.png" /></figure>

<figure><img src="image_193.png" /></figure>

* File Archive: ISA Tab

* Semantic representation in OWL: Phenotyping Experiment Ontology (PPEO) using OWL ([http://agroportal.lirmm.fr/ontologies/PPEO](http://agroportal.lirmm.fr/ontologies/PPEO))

* Web Service: Breeding API

<figure><img src="image_194.png" /></figure>
# MIAPPE File Archive

* ISA Tab for Phenotyping

* Investigation/Study/Assay

* Zip Archive

* MIAPPE Metadata

* Raw data

- CSV

- Images or binary files

- Reference to image archive (URI/URL)

* Elaborated data

- CSV

- Provenance

* Training and improvements

* File and metadata curation

* Elixir

* Célia Miguel & Anne Françoise Adam Blondon

* BrAPI to IsaTab portable tool

* Elixir implementation study for MIAPPE Validation

<figure><img src="image_195.png" /></figure>

Data files

<figure><img src="image_196.png" /></figure>
# MIAPPE File: Poplar Phenology dataset

* Zip archive

* 1 investigation

* i_Investigation.txt : initial file

* 3 Studies

* 3 s_*.txt file

* 1 level per study

* 3 a_*.txt

* 3 trait definition file

* Crop ontology format

* T_*.txt files

* 3 data files

* D_*.txt file

a_dXJuOIVSR0kvc3R1ZHkvUE9QWU9NSUNTLVBPUDItRg==_virtual trial.txt

a_dXJuOIVSR0kvc3R1ZHkvUE9QWU9NSUNTLVBPUDItSQ==_virtual trial.txt

a_dXJuOIVSR0kvc3R1ZHkvUE9QWU9NSUNTLVBPUDItVUs==_virtual trial.txt

d_dXJuOIVSR0kvc3R1ZHkvUE9QWU9NSUNTLVBPUDItRg==_virtual trial.txt

d_dXJuOIVSR0kvc3R1ZHkvUE9QWU9NSUNTLVBPUDItSQ==_virtual trial.txt

d_dXJuOIVSR0kvc3R1ZHkvUE9QWU9NSUNTLVBPUDItVUs==_virtual trial.txt

i_investigation.txt

s_dXJuOIVSR0kvc3R1ZHkvUE9QWU9NSUNTLVBPUDItRg==.txt

s_dXJuOIVSR0kvc3R1ZHkvUE9QWU9NSUNTLVBPUDItSQ==.txt

s_dXJuOIVSR0kvc3R1ZHkvUE9QWU9NSUNTLVBPUDItVUs=.txt

t_dXJuOIVSR0kvc3R1ZHkvUE9QWU9NSUNTLVBPUDItRg==.txt

t_dXJuOIVSR0kvc3R1ZHkvUE9QWU9NSUNTLVBPUDItSQ==.txt

t_dXJuOIVSR0kvc3R1ZHkvUE9QWU9NSUNTLVBPUDItVUs=.txt

<figure><img src="image_197.png" /></figure>
# MIAPPE Semantic: PPEO

* MIAPPE OWL Implementation

* https://github.com/MIAPPE/MIAPPE-ontology

* www.MIAPPE.ORG

* Data model representation

* Formal concepts and constraints

<figure><img src="image_198.png" /></figure>

<figure><img src="image_199.png" /></figure>
> MIAPPE Semantic: PPEO

* Brapi 2 MIAPPE RDF workflow

* http://ist.blogs.inra.fr/wdi/phenotypes-as-rdf/ (www.wheatIS.org data standards)

* JSON LD based

* Plant BrAPI ETL FAIDARE: needs update to BrAPI v1.3

* https://github.com/elixir-europe/plant-brapi-etl-faidare

* Wheat dataset

* http://dx.doi.org/10.15454/1.4489666216568333E12
    Wheat Data Interoperability Guidelines

<figure><img src="image_200.png" /></figure>

<figure><img src="image_201.png" /></figure>

(Security restrictions of this server do not allow you to retrieve remote RDF data, see details.)

HTML

<figure><img src="image_202.png" /></figure>

Results Format:

0

milliseconds (values less than 1

Execution timeout:

<figure><img src="image_203.png" /></figure>

Strict checking of void variables

<figure><img src="image_204.png" /></figure>

Log debug info

Options:

(The result can only be sent back to browser, not saved on the server, see details)

Run Query

Reset
> **Phenotype Technical Standard**

* Breeding API http://brapi.org/

* Connect data repositories and tools:

* Genotype visualization (Flapjack)

* Studies graph preview and filtering

* BrAPPS : Tools integrable in any BrAPI compliant System

* https://www.brapi.org/brapps.php

* R analysis environment

* Field data capture

* FAIR Data discovery → Elixir
FAIDARE

<figure><img src="image_205.png" /></figure>

T3

CIRAD

GOBII

Wageningen

Cornell

iPlant

BILL & MELINDA
GATES foundation

<figure><img src="image_206.png" /></figure>

<figure><img src="image_207.png" /></figure>

<figure><img src="image_208.png" /></figure>

CGIAR

Biovarsity
International

<figure><img src="image_209.png" /></figure>

INRA
SCIENCE & IMPACT

<figure><img src="image_210.png" /></figure>

The James
Hutton
Institute

<figure><img src="image_211.png" /></figure>

<figure><img src="image_212.png" /></figure>

<figure><img src="image_213.png" /></figure>

WAGENINGEN
UNIVERSITY & RESEARCH

<figure><img src="image_214.png" /></figure>

Selby et al. Bioinformatics (2019),
doi.org/10.1093/bioinformatics/btz190

<figure><img src="image_215.png" /></figure>

<figure><img src="image_216.png" /></figure>
<figure><img src="image_217.png" /></figure>

# REST - Representational State Transfer

<figure><img src="image_218.png" /></figure>

* REST is an architecture design for creating Web Services using the well known HTTP standard

* Requests are made with URLs

* Data is represented with JSON
<figure><img src="image_219.png" /></figure>

# Standardized Specification

* BrAPI has defined a **Standardized set of data model structures** to communicate the basic information of plant breeding

* BrAPI is a technical **Specification** which software developers can easily turn into code which communicates using the Standard.
> BrAPI data model

* MIAPPE compliant

* MIAPPE Investigation → BrAPI Trial

* MIAPPE Study → BrAPI Study

* Biological Material Germplasm MCPD dedicated BrAPI calls

* Observation variable BrAPI calls

* Datafile or Observation Unit

<figure><img src="image_220.png" /></figure>

<figure><img src="image_221.png" /></figure>

<figure><img src="image_222.png" /></figure>
<figure><img src="image_223.png" /></figure>

# Data repositories & Data portals

* National data repositories opened for collaborators

* GnpIS/data.inrae.fr

* https://urgi.versailles.inra.fr/Data/Phenotyping/Data-submission

* eDal!-PGP

* https://edal-pgp.ipk-gatersleben.de/

* BrAPI compliant databases

* https://www.brapi.org/servers

* Emphasis databases

* PHIS, PIPPA, ...

* Data portals

* Search over repositories all over the world

* https://urgi.versailles.inra.fr/faidare/

* http://wheatis.org/Search.php

<figure><img src="image_224.png" /></figure>
> Take Home message

Standardizing your data is

- Good for you

- Good for others

- Getting easier

- Contribution Welcomed !

Thank you!

<figure><img src="image_225.png" /></figure>