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# The ELIXIR infrastructure for plant phenotyping-genotyping data management

Anne-Françoise Adam-Blondon

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## To cite this version:

Anne-Françoise Adam-Blondon. The ELIXIR infrastructure for plant phenotyping-genotyping data management. Workshop “Plant Phenotyping-Genotyping Data Management”, ITQB; ELIXIR-PT, Nov 2019, Oiras, Portugal. [hal-03316380](https://hal.inrae.fr/hal-03316380)

HAL Id: [hal-03316380](https://hal.inrae.fr/hal-03316380)

https://hal.inrae.fr/hal-03316380

Submitted on 6 Aug 2021

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Distributed under a Creative Commons Attribution 4.0 International License
# The ELIXIR infrastructure for plant phenotyping-genotyping data management

A-F Adam-Blondon, INRA, ELIXIR-FR

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Context

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Genetic resources

Classify, map, and analyze data

Taxons

Taxonomic levels

Genetic analyses

Classify, map, and analyze data

Phenotypes

Characterize and compare

Polymorphisms

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Predictive biology: Genomic x Environment x Phenomic

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# Reusing and integrating the data : e.g. 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 date...
Europe has been continuously reinforcing its policy for facilitating open access to data

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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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Data management plans are evaluated in EU projects proposals
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

Galaxy

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Proteomics

Rare diseases
...delivered in
partnership with
research communities

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a

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Compute

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Training and Skills

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Too

Is

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Standards

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Contacts Plants: Cyril Pommier (FR), Astrid Junker (DE),Kristina Gruden (SL)
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Data is scattered in an “ecosystem”
of information systems
# Insertion of data repositories in federations of information systems

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Internationally
agreed standards
and identifiers
# 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

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Wilkinson et al (2016)
SCIENTIFIC DATA, 3:160018,
DOI: 10.1038/sdata.2016.18
Examples of federations

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Genetic resources

Find genes, resources, data

Taxons

Taxa, resources

Genetic analyses

Genome, map & 3D

Phenotypes

Genotypes and phenotypes

Polymorphisms

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Three “use cases” of federations

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

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

- The data federation of the ELIXIR Plant Science community (https://elixir-europe.org/communities/plant-sciences)
Services

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« Consumable » data

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

F

A

accessible

I

Interoperable

R

eusable

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BrAPI
# Developing a federation of FAIR plant data repositories

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Registries of standards and guidelines

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

International registries

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http://agroportal.lirmm.fr/

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Community registries
# Example of needed resources for a federation of phenotyping data

## Semantic

* Description of the data

* Controlled vocabularies: term name and definitions

* Ontologies: semantic links between terms

* **Biologist driven**

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Crop Ontology
for agricultural data

## Structure

* Formatting and Organizing the data

* Data Models

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

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miappe

* **Biologist & Computer scientist driven**

## Technical

* Data integration and sharing

* Interoperability: tools and systems

* GA4GH

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* Breeding API www.brapi.org

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BrAPI

* **Computer scientist driven**

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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.
# 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: entity and quality), ...

* 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 services across federations:
e.g. search and access data

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# WheatIS Generic Data Discovery Tool

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Apache Solr

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Apache Solr

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Apache Solr

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Apache Solr

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Apache Solr

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User web

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http://www.wheatis.org/

Google like list of results

Proof of concept and
success story

Common
Data
Model

WheatIS

Filters

Clear

Database

☐ TRITICEAE TOOLBOX (437)

☐ OPENMINTED (126)

☐ WHEAT GENE CATALOG AT KONUGU (10)

☐ GRAINGENES (7)

☐ GNPIIS (2)

☐ PLANTPHENODB (1)

Type

☐ ACCESSION (361)

☐ BIBLIOGRAPHY (126)

☐ EXPERIMENT (60)

☐ PHENOTYPE (17)

☐ GENE (10)

☐ GENETIC MAP (6)

☐ PHENOTYPE (1)

☐ PHYSICAL MAP (1)

☐ QTL (1)

Species

☐ TRITICUM AESTIVUM (454)

☐ TRITICUM (126)

☐ LEYMUS RACEMOSUS (1)

☐ TRITICUM DURUM (1)

☐ TRITICUM TURGIDUM SSP

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transPLANT data model

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# WheatIS data discovery tool: evolution

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* ✓ Lighter implementation for new nodes

* ✓ Nodes cannot be « down »

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

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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 Triticaceae 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

**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 (https://urgi.versailles.inra.fr/rare/)

**Light metadata based on the data model and no constraint on vocabularies**

**No data integration**
Towards data integration in federations

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The Breeding API (BrAPI) Project is an **international effort** to create a RESTful specifications for a web service that enables interoperability among plant breeding databases.

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Bill & Melinda Gates Foundation
* 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)

Selby *et al*. *Bioinformatics* (2019), doi.org/10.1093/bioinformatics/btz190
Enabling improvements of data services by an international community

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Anyone can develop a service that will « consume » the data of the federation exposed with the BrAPI standard: « machines understand your data »

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accelerate

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BrAPI
# ELIXIR Plant Data Search Service: FAIDARE

* Open source software

* Links

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* Genomic Centralised repository

* Phenotype Distributed repositories

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## FAIR Data-finder for Agronomic REsearch

### Sources

- [ ] URGI GnpIS (81,335)

- [ ] EBI European Nucleotide Archive (44,975)

- [ ] CIRAD TropGENE (722)

- [ ] VIB PIPPA (692)

- [ ] JBE BioData (67)

### Germplasm

### Trait

### Crops

(common name, species, genus, subtaxa & synonyms)

Search crops

### Germplasm list

(panel, collection & population)

Search germplasm lists

### Types

- [ ] Germplasm (94,589)

- [ ] Genotyping Study (32,210)

- [ ] Phenotyping Study (992)

### Accession

(accession name, number & synonyms)

Search germplasm accession

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How do we (easily) get the data FAIR into the data
federation?

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# Example of five large crop centered French national projects on sugar beet, maize, wheat, pea and rapeseed (2012-2020)

AKER, AMAIZING, BREEDWHEAT, PEAMUST, RAPSODYN Transverse actions in relation with data management

* GnpIS for the integration of heterogeneous public and private data according to the data management plans described in the consortium agreements

* Optimisation of the developments of GnpIS between projects

* Public private partnership to develop a suite of tools aiming at facilitating the insertion and integration of partner's data in GnpIS

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Nb: these projects started before we knew about the FAIR principles and at the very start of the Wheat Initiative
# Publishing FAIR data in GnpIS

## Enforcement of good practices for data traceability in the consortia

* Plant material identified under the responsibility of the genebanks

* Development of crop ontologies for phenotypic variables (e.g. https://www.cropontology.org/ontology/CO_333/Beet%20Ontology)

<table><thead><tr><th rowspan="2"></th><th colspan="2">Genetic Resources</th><th colspan="2">Phenotyping</th><th colspan="2">Genotyping</th><th colspan="2">Association</th></tr><tr><th>Public</th><th>Private</th><th>Public</th><th>Private</th><th>Public</th><th>Private</th><th>Public</th><th>Private</th></tr></thead><tbody><tr><th>Brassica</th><td>981</td><td>18</td><td>5</td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Beta</th><td>10783</td><td></td><td>5</td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Zea</th><td>1861</td><td>869</td><td>20</td><td>3</td><td>1</td><td>4</td><td>3</td><td></td></tr><tr><th>Pisum</th><td>1015</td><td>872</td><td>0</td><td>86</td><td></td><td>3</td><td></td><td></td></tr><tr><th>Wheat</th><td>10448</td><td>2811</td><td>821</td><td>57</td><td>1</td><td>22</td><td>43</td><td>1970</td></tr></tbody></table>

<table><thead><tr><th rowspan="2"></th><th colspan="2">Sequence</th><th colspan="2">Genetic Map</th><th colspan="2">Genome browser</th></tr><tr><th>Public</th><th>Private</th><th>Public</th><th>Private</th><th>Public</th><th>Private</th></tr></thead><tbody><tr><th>Brassica</th><td>4</td><td>2</td><td>2</td><td></td><td>1</td><td></td></tr><tr><th>Beta</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Zea</th><td>8</td><td>8</td><td>18</td><td>21</td><td>4</td><td>3</td></tr><tr><th>Pisum</th><td>5</td><td>2</td><td></td><td>12</td><td>1</td><td></td></tr><tr><th>Wheat</th><td>7</td><td>27</td><td>29</td><td>1</td><td>6</td><td></td></tr></tbody></table>
Publishing FAIR data in GnpIS

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Active data
stewardship helps
a lot

PHENOTYPING DATASETS
# Publishing FAIR data in GnpIS

## Enforcement of good practices for data traceability in the consortia

* Association of DOI to datasets using data.inra before integration in GnpIS (e.g. DROPS/AMAIZING dataset)

Portail Data Inra > Omics Dataverse > URGI Plant and Fungi Dataverse >

A multi-site experiment in a network of European fields for assessing the maize yield response to environmental scenarios

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### A multi-site experiment in a network of European fields for assessing the environmental scenarios (Version 1.1)

Millet, Emilie J.; Pommier, Cyril; Buy, Mélanie; Nagel, Axel; Kruljer, Willem; Welz-Bolduan, Therese; Lopez, Jeremy; Richard, Cécile; Racz, Ferenc; Tanzi, Franco; Spitkot, Tamas; Cané, Maria-Angela; Negro, Sandra S.; Coupel-Ledru, Aude; Nicolas, Stéphane D.; Palaffre, Carine; Bauland, Cyril; Praud, Sébastien; Ranc, Nicolas; Presterl, Thomas; Bedk Zoltan; Tuberosa, Roberto; Usadel, Björn; Charcosset, Alain; van Eeuwijk, Fred A.; Draye, Xavier; Tardieu, François; Welcker, Claude, 2019, "A multi-site experiment in a network of European fields for assessing the maize yield response to environmental scenarios", https://doi.org/10.15454/IASSTN, Portail Data Inra, V1, UNF:6:XjvH4MFxATmPCRAIN8j0aw==

Description

This dataset comes from the European Union project DROPS (DROoug grown with two water regimes (irrigated or rainfed), in seven fields in 2X from western to eastern Europe, plus one site in Chile in 2013. This rea year, one site and one water regime, with two and three repetitions for i environmental characterisation was carried out, with hourly records of r associated with precise measurement of phenology. Grain yield and its The main purpose of this dataset consists in using the environmental ci yield in response to the environmental drivers for genotype-by-environment over the field network are consistent within a scenario but largely differ

Subject

Climate; Plant Breeding and Plant Products

Keyword

Phenotyping, Envirotyping, Genotype-by-environment Interaction, Multi

Files

Metadata

Terms

Versions

Search this dataset...

Find

### Phenotypes

Back to Form

In Search parameters

Genus: 268

Geolocation

+

-

Data

* DATA SUBMISSION
* Phenotyping File

* GNPIS PORTAL

* GENOMES

* SEQUENCES

* GENETIC MAPS

* POLYMORPHISMS

* ASSOCIATION

* GENETIC RE SOURCES

* SRC COLLECTIONS

* PLANT SYNTeny

* TRANSCRIPTOMIC

Origin site ☐ Collecting site ☐ Evaluation site

Phenotyping campaign

<table><tr><td>2011. x</td><td>2012. x</td><td>2013. x</td></tr></table>

rmova all add all

Trial site ☐ Phenotypic data

Data table view Default

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LEVEL: REPLICATE > BLOCK > PLOT

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results per page

<table><thead><tr><th rowspan="2">Lot Number</th><th colspan="2">GENOTYPE ID</th><th colspan="2">TREATMENT</th><th rowspan="2">Total Name</th><th rowspan="2">Total Site</th><th rowspan="2">BLOCK</th></tr><tr><th>Accession number</th><th>Name</th><th>water</th><th>regime</th></tr></thead><tbody><tr><td>3016</td><td>E1892_H</td><td>F1890_H</td><td>rainfed</td><td></td><td>Somnata Dacidea 2011</td><td>Debrecon</td><td>3</td></tr><tr><td>3011</td><td>E912_H</td><td>F912_H</td><td>rainfed</td><td></td><td>GWG Karisathwa 2011</td><td>Kartosha</td><td>4</td></tr><tr><td>3024</td><td>LHC3_H</td><td>LHC3_H</td><td>rainfed</td><td></td><td>GWG Karisathwa 2011</td><td>Kartosha</td><td>3</td></tr><tr><td>3977</td><td>Phen33_H</td><td>PHH93_H</td><td>rainfed</td><td></td><td>GWG Karisathwa 2011</td><td>Kartosha</td><td>6</td></tr><tr><td>3012</td><td>6013_H</td><td>B105_H</td><td>rainfed</td><td></td><td>GWG Karisathwa 2011</td><td>Kartosha</td><td>4</td></tr></tbody></table>

Improve/have a control on the FAIRness of the data sets that are deposited
# Enabling GnpIS insertion in data federation

## Web Interface: get MIAPPE compliant ISATab data

## BrAPI: MIAPPE compliant programatic access

## Data submission to GnpIS compliant with MIAPPE requirements

## Collaboration with the Crop Ontology

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Conclusions

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Genetic resources

Taxa

Genetic analyses

Phenotypes

Polymorphisms

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5 * Open Data

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Courtesy of Daniel Jacob

Progressing towards FAIR and Open Data requires a **multidisciplinary cooperation** :

* Biologists

* Bioinformaticians

* specialists of ontologies/semantics
Data life cycle in (plant) biology research activities

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# Challenge: provide operational environments facilitating data management all along its life cycle

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From Rowan University, NJ, USA

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Priority 2020-2024 : ELIXIR-Converge H2020 project.

Use case Plant coordinated by ELIXIR-PT
ELIXIR Plant community

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NIB

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cirad

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WAGENINGEN
UNIVERSITY & RESEARCH

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JÜLICH
Forschungszentrum

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INRA
SCIENCE & IMPACT

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INSTITUTO
GULBENKIAN
DE CIÊNCIA

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iBET

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VIB

EMBL-EBI

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Biodiversity
International

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IPK
GATERSLEBEN

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Earham
Institute

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FONDAZIONE
EDMUND
MACH

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Thank you!

elixir

www.elixir-europe.org

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@ELIXIREurope

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/company/elixir-europe
# Acknowledgements

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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
Nébuau Français
Phénomique végétal
FPPN

transPLANT

elixir

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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

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Biodiversity
International

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Planteome

## Other national and international projects

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Betterave2020

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amaizing

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Breed

wheat

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PeaMUST

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Raps

dyN

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GENTREE

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Wheatbi

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GenOak

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## Financial supports

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INRA
SCIENCE & IMPACT

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ANR

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RESEARCH DATA ALLIANCE