# Developing infrastructures for FAIR data in plant biology

Cyril Pommier, Guillaume Cornut, Michael Alaux, Windpouire Esther Dzale Yeumo, Raphaël Flores, Thomas Letellier, Célia Michotey, Nacer Mohellibi, Sophie Durand, Erik Kimmel, et al.

► To cite this version:

Cyril Pommier, Guillaume Cornut, Michael Alaux, Windpouire Esther Dzale Yeumo, Raphaël Flores, et al.. Developing infrastructures for FAIR data in plant biology. Gatersleben Research Conference - Applied Bioinformatics for crops (GRC2019), IPK, Mar 2019, Gatersleben, Germany. hal-03316569

HAL Id: hal-03316569

https://hal.inrae.fr/hal-03316569

Submitted on 6 Aug 2021

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# Developing infrastructures for FAIR data in plant biology

A-F Adam-Blondon, URGI, INRA

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Context

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Genetic resources

Test access to genes

Taxa

Genomes

Genetic analyses

Genetic analysis of 1,271,000

Phenotypes

Characterization of 1,271,000

Polymorphisms

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# INRA's objectives necessitate interdisciplinary researches and data re-use

### FOOD

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AGRICULTURE

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ENVIRONMENT

Multi-faceted agriculture: design, evaluation, optimisation, diversification

Climate change: impacts, mitigation and adaptation

Energy and chemical applications of biomass and bio-derived products

Healthy and sustainable food systems

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Predictive biology

Agroecology

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Food security by 2050 in a rapidly changing global context

4 priorities to address major challenges

2 emerging interdisciplinary

1 major goal

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# Example: modelling the impact of climate change using plant phenology

Global plant phenology data portal: [www.plantphenology.org](http://www.plantphenology.org)

Pan European PEP725 Plant phenology database: [http://www.pep725.eu/](http://www.pep725.eu/)

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**Phenology data** : different sources, different accuracy in terms of identification of the species, scoring methods, record formats

- Modelers of the impact of climate change

- Geneticists, Breeders

- Genbank managers

- Experimental station managers

- Civil (economic) society: e.g. vintage dates, cherry blooming dates...
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Data is scattered in an
“ecosystem” of information
systems
# Development of an INRA information system for crops, forest trees and pathogens

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GnplS

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Phenotypes

• GxE

• QTL maps

• GWAS, GS

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Genetics

• Genetic maps

• Genetic markers

• Genetic resources

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Genome

• Annotations

• Transcriptome

• SNP / Structural variant
# Insertion of GnpIS in federations of information systems

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2002: Single database

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2008: DB Interoperability

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ZGnpIS-coreDB

2010: set of interoperable databases

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2015: Distributed
information system

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ZGnpIS Information System
# Featuring federations of information systems

- A network of (stable/sustainable) nodes

- A central portal offering services (e.g. search data)

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Backbone of good practices enabling such infrastructures

F indable

A ccessible

I nteroperable

R eusable

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Wilkinson et al (2016)
SCIENTIFIC DATA,
3:160018, DOI:
10.1038/sdata.2016.18
Europe has also been continuously reinforcing its policy for facilitating open access and interoperability

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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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Recently funded projects were asked to add a WP supporting a FAIR compliant Data Management Plan
This policy is implemented via the European
Open Science Cloud (EOSC)

* The European infrastructures in Life Science should contribute to EOSC

* E.g. European Infrastructure of Bioinformatics for Life-sciences: ELIXIR (23 countries)

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FAIR data management in the
life sciences
10.7490/f1000research.1114985.1

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Gateway for User access and
mechanism for exposing
life-science services (via ELIXIR
Registries)

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Compatible Cloud /
Workflows / Reference Data
Set Distribution Service
# ELIXIR's objectives

In 2023: Continent-scale, standards-based infrastructure for accessing and analysing life-science data

Marine metagenomics

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Human data

Plants

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Metabolomics

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Proteomics

Galaxy Rare diseases ...delivered in partnership with research communities

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a

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Training and Skills

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Too

Is

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Standards

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elixir

Contacts Plants: Celia Miguel (IBET) & Cyril Pommier (INRA)
Examples of federations

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Genetic resources

Genetic resources data

Taxa

Taxa data

Genetic analyses

Genetic analyses of 5,731, 850

Phenotypes

Phenotypes of 5,731, 850

Pollenormaions

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Two main examples or “use cases”

- The Wheat Initiative (G20 Initiative) and its Wheat Information System Expert Working group (www.wheatis.org). Also supported by the Research Data Alliance.

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WHEAT
INITIATIVE

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INRA
SCIENCE & IMPACT

- The European Infrastructure for Multi-scale Plant Phenomics and Simulation (EMPHASIS) and its information system (https://emphasis.plant-phenotyping.eu/Infrastructure). In the frame of a strong collaboration with ELIXIR

PHENOME
Résultats du FPPN

ANR

elixir

EMPHASIS

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Services

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BrAPI

Emphasis Layer

ontologies, references, data access, web services, registry, etc.

Emphasis modules -
registry

BrAPI - MIAPPE

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Semantic Web

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Knowledge

Smart LAYER

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Emphasis
LAYER

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Data Layer

Local DB1

Local DB2

Local DB3

Local DB-n

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miappe
# Developing a federation of FAIR plant phenotyping data repositories

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About

Collaborators

Search

Data Standards

Submit Data

Tools

Links

WheatIS Nodes

WheatIS

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# Registries of standards and guidelines

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https://www.ebi.ac.uk/ols/ontologies

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http://agroportal.lirmm.fr/

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# Development of a metadata standard for phenotyping experiments

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* MIAPPE: Minimum Information About Phenotyping Experiment

* www.miappe.org

* Steering committee Emphasis, Elixir CGIARs

Last release MIAPPE v1.1 (Jan. 2019). Major improvements:

* Extension to accommodate woody plants as an additional use-case.

* Specification of a data model for easier implementation in various formats and automatic validation.

* Improved compatibility with ISA-Tools and Breeding API (BrAPI).

* Provision of clear definitions and examples for all fields.

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# Adoption of the Crop Ontology format for the description of the phenotyping variables
(www.cropontology.org)

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

## Examples

* Woody Plant Ontology

* Wheat INRA Phenotyping Ontology v1.3 being merged with the Wheat Ontology developed by the CYMMIT

* Grape Ontology (v2)

* Also used to describe environment variables

* Strong curation efforts still needed: documentation of the methods, standardize the vocabulary (traits), ...

* Environments facilitating the development and curation of ontologies are needed
Registries of identifiers for key objects

* DOI for plant accessions (following the FAO recommandations)

* Biosample : identifiers for samples derived from accessions

* Crop Ontology : identifiers for phenotyping variables

* DOI associated to phenotyping trial sets (and data papers)
# Developing search and data access
across federations

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Generic Data Discovery Tool

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transPLANT data model

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# WheatIS data discovery:
https://urgi.versailles.inra.fr/wheatis/

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# WheatIS data discovery tool: evolution

## WheatIS nodes

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* ✓ Lighter implementation for new nodes

* ✓ Nodes cannot be « down »

* ✓ Easy addition of new functionalities
# WheatIS data discovery tool: evolution

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Wheat@URGI WheatIS Wheat Initiative

URGI

IWGSC@GnpIS [18 566 139]

GnpIS [92 214]

OpenMinTeD [3 398]

WheatIS File Repository [6]

EBI

Ensembl Plants [2 122 980]

IPK

CR-EST [199 220]

GEBIS [50 875]

MetaCrop [177]

Gramene

Gramene [229 789]

UWA

Wheat Pangenome [167 167]

T3

The Triticace Toolbox [138 441]

South Green

AgroLD [137 060]

Rothamsted Research

KNetMiner [110 775]

GrainGenes

GrainGenes [15 827]

Wheat Gene Catalog at

Komugi [3 043]

PGSB

CrowsNest [13 324]

CIMMYT

CIMMYT Dspace [981]

CIMMYT dataverse [1]

IPGPAS

PlantPhenoDB [3]

WheatIS

Wheat Information
System

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Examples: yield, fhb

Search

**!beta! https://urgi.versailles.inra.fr/data-discovery-staging/**

Based on user's remarks:

* New nodes

* New data

* New filters/data types

**Open software, very generic**, that can (and is) adapted to any type of federation: e.g. the federation of information systems for the french infrastructure of genetic resources for research in agriculture, AgroBRC-RARe (!beta! https://urgi.versailles.inra.fr/rare-beta/ )
# Deeper interoperability in federations

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The Breeding API (BrAPI) Project is an international effort to create a RESTful specification that enables interoperability among plant breeding databases.

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BIOversity
International

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NEXTGEN
CASSAVA

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Diversity
Arrays
Technology

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CIMMYT

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PHENOME
PROJECT

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Earlham
Institute

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SouthGreen
bioinformatics platform

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WAGENINGEN
UNIVERSITY & RESEARCH

Bill & Melinda Gates Foundation

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* Development of a **standard API**:

* Calls for plant material aligned with MCPD

* Calls for phenotyping experiments aligned with MIAPPE and of a supporting data model

* Next steps: develop the same type work on the calls for genotyping data (coordination with GA4GH)
Enabling improvements of data services

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accelerate

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EMPHASIS

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BrAPI

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PHENOME
Research Platform
www.phenome.eu

Search
interface

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Web Service

Data data
analysis

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Batch
Download

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Conclusions

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Genetic resources

Test access to genes

Taxa

Genetic analyses

Genetic analysis of 1,200,000

Phenotypes

Phenotypes of 1,200,000

Pollenorms

Pollenorms

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Conclusions and perspectives

* Generic search tool used for the WheatIS: light tool that allows community building -> Monitoring of the interest of end users to be done in more detail

* BrAPI : same good properties + the possibility to diversify services

* One big challenge still ahead: get the data and get it FAIR during all its life cycle.
There is still a challenge in providing operational environments facilitating data management all along its life cycle

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Researcher's view

From Rowan University, NJ, USA
There is still a challenge in providing operational environments facilitating data management all along its life cycle

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Infrastructures
Data's view (EUDAT)
There is still a challenge in providing operational environments facilitating data management all along its life cycle

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Infrastructures
Identity and access view
# Acknowledgements

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URGI team

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H. Quesneville

C. Pommier

M. Alaux

M. Buy

D. Charruaud

G. Cornut

J. Destin

S. Diagne

S. Durand

B. El-Houdaigui

R. Flores

C. Guerche

E. Kimmel

T. Letellier

C. Michotey

N. Mohellibi

## National and International infrastructures/initiatives

PHENOME
Névisac Frangais
Phénomique végétal

transPLANT

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openMIN7ED
Open Mining Infrastructure for Text & Data

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WHEAT
INITIATIVE

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International
Wheat Genome
Sequencing
Consortium

BrAPI

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Biodiversity
International

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Planteome

## National and international Wheat projects

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WheatBI

## Financial supports

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INRA
SCIENCE & IMPACT

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ANR

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Thank you!
# WheatIS search usage metrics

2016

<table><thead><tr><th colspan="5">Summary</th></tr><tr><th>Reported period</th><td colspan="3">Year 2016</td><td rowspan="3">Pages</td></tr><tr><th>First visit</th><td colspan="3">01 Jul 2016 - 03:57</td></tr><tr><th>Last visit</th><td colspan="3">31 Dec 2016 - 00:42</td></tr><tr><th></th><th>Unique visitors</th><th>Number of visits</th><th></th><th></th></tr></thead><tbody><tr><th>Viewed traffic *</th><td><strong>&lt;= 460</strong><br>Exact value not available in 'Year' view</td><td><strong>689</strong><br>(1.49 visits/visitor)</td><td><strong>3,609</strong><br>(5.23 Pages/Visit)</td><td></td></tr><tr><th>Not viewed traffic *</th><td></td><td></td><td><strong>110</strong></td><td></td></tr></tbody></table>

2017

<table><thead><tr><th colspan="5">Summary</th></tr><tr><th>Reported period</th><td colspan="3">Year 2017</td><td rowspan="3">Pages</td></tr><tr><th>First visit</th><td colspan="3">01 Jan 2017 - 15:36</td></tr><tr><th>Last visit</th><td colspan="3">31 Dec 2017 - 08:09</td></tr><tr><th></th><th>Unique visitors</th><th>Number of visits</th><th></th><th></th></tr></thead><tbody><tr><th>Viewed traffic *</th><td><strong>&lt;= 1,556</strong><br>Exact value not available in 'Year' view</td><td><strong>2,236</strong><br>(1.43 visits/visitor)</td><td><strong>17,568</strong><br>(7.85 Pages/Visit)</td><td></td></tr><tr><th>Not viewed traffic *</th><td></td><td></td><td><strong>996</strong></td><td></td></tr></tbody></table>

2018

<table><thead><tr><th colspan="5">Summary</th></tr><tr><th>Reported period</th><td colspan="3">Year 2018</td><td rowspan="3">Pages</td></tr><tr><th>First visit</th><td colspan="3">07 Jan 2018 - 07:20</td></tr><tr><th>Last visit</th><td colspan="3">27 Nov 2018 - 07:33</td></tr><tr><th></th><th>Unique visitors</th><th>Number of visits</th><th></th><th></th></tr></thead><tbody><tr><th>Viewed traffic *</th><td><strong>&lt;= 1,931</strong><br>Exact value not available in 'Year' view</td><td><strong>3,307</strong><br>(1.71 visits/visitor)</td><td><strong>27,371</strong><br>(8.27 Pages/Visit)</td><td></td></tr><tr><th>Not viewed traffic *</th><td></td><td></td><td><strong>604</strong></td><td></td></tr></tbody></table>

<footnote>* Not viewed traffic includes traffic generated by robots, worms, or replies with special HTTP status codes.</footnote>

<footnote>* Not viewed traffic includes traffic generated by robots, worms, or replies with special HTTP status codes.</footnote>

<footnote>* Not viewed traffic includes traffic generated by robots, worms, or replies with special HTTP status codes.</footnote>