# Find Genomic, Genetic and Phenomic Data for Wheat Using the WheatIS Data Discovery Tool

Michael Alaux, Mario Caccamo, Sarah Dyer, David Edwards, Raphaël Flores, Najwa Lakmouri, Cyril Pommier, Taner Z Sen

► To cite this version:

Michael Alaux, Mario Caccamo, Sarah Dyer, David Edwards, Raphaël Flores, et al. Find Genomic, Genetic and Phenomic Data for Wheat Using the WheatIS Data Discovery Tool. PAG 32, Jan 2025, San Diego (CA), United States. `hal-05528830`

HAL Id: `hal-05528830`

https://hal.inrae.fr/hal-05528830v1

Submitted on 26 Feb 2026

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
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# Find Genomic, Genetic and Phenomic Data for Wheat Using the WheatIS Data Discovery Tool

Michael Alaux et al.

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RÉPUBLIQUE
FRANÇAISE

Liberté
Égalité
Fraternité

INRAO

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Session

"Databases and online tools for
displaying the anchored physical maps"

Wheat mapping and annotation data at URGI

Michaël Alaux

PAG 2010 San Diego
IWGSC Physical Mapping Standard Protocols Workshop
organized by Catherine Feuillet

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* Genomics data well managed thanks to the coordination of the IWGSC

* Need to find the other types of data (germplasm, phenotyping, genotyping, etc.)
hosted in databases around the world

→ Wheat Information System

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# Outline

* What is WheatIS and how to use it to discover data ?

* The Wheat Initiative

* The WheatIS Expert Working Group

* Data standards

* Data discovery

* Sharing experiences

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COORDINATING GLOBAL WHEAT
RESEARCH

THE WHEAT INITIATIVE

TH OF JANUARY 2025 - APPLY NOW TO BECOME A MEMBER - NEW MEMBERS CAN ONLY BE RECIEVED UNTIL THE 6TH OF DECEMBER AND.
> History

Coordinated by JKI (Germany)
scientific leader: Peter Langridge

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Coordinated by INRA (France)
scientific leader: Hélène Lucas

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chair: Michael Alaux (INRAE-URGI)

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chair: Taner Sen (USDA-ARS)

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Launch of the Wheat Information
System Expert Working Group
chair: Hadi Quesneville (INRA-URGI)

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## Organisation

### NUMBERS

11 Expert Working Groups (EWG)

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600+ EWG members

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14 member countries (public)

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5 industry members (private)

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2 research organisations

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5 observer countries

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5 Associated Programmes

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WHEAT INITIATIVE SECRETARIAT

WHEAT INITIATIVE MEMBERS

MEMBER STATES

INTERNATIONAL ORGANISATIONS

INDUSTRY

INSTITUTION'S
COORDINATION
COMMITTEE

RESEARCH
COMMITTEE

SCIENTIFIC
BOARD

EXPERT WORKING GROUPS

ABIOTIC STRESS

BREEDING

GERMPLASM

PESTS

DURUM

QUALITY

NUTRIENT

AGRONOMY

PHENOTYPING

WHEAT IS

FUNDING

PATHOGENS

WHEAT INITIATIVE PROGRAMMES

AHEAD

WheatVIVO

WheatIS

Reference
Collection

ASSOCIATED PROGRAMMES

IWGSC

IWYP

AgMIP

10+ GENOMES

WheatBi

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STRATEGIC RESEARCH
AGENDA

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WHEAT
INITIATIVE

July 2022

Open Access

Feature Paper

Review

Meeting the Challenges Facing Wheat Production: The Strategic Research Agenda of the Global Wheat Initiative

by ⬒ Peter Langridge 1,2,* ⬒, ⬒ Michael Alaux 3, ⬒ Nuno Felipe Almeida 4 ⬒, ⬒ Karim Ammar 5,

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Michael Baum 6, ⬒ Faouzi Bekkaoui 7, ⬒ Alison R. Bentley 5 ⬒, ⬒ Brian L. Beres 8 ⬒,

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Bettina Berger 9 ⬒, ⬒ Hans-Joachim Braun 5, ⬒ Gina Brown-Guedira 10 ⬒,

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Christopher James Burt 11, ⬒ Mario Jose Caccamo 12 ⬒, ⬒ Luigi Cattivelli 13 ⬒, ⬒ Gilles Charmet 14,

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Peter Civán 14, ⬒ Sylvie Cloutier 15 ⬒, ⬒ Jean-Pierre Cohan 16, ⬒ Pierre J. Devaux 17 ⬒,

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Fiona M. Doohan 18, + Show full author list

Agronomy **2022**, *12*(11), 2767; https://doi.org/10.3390/agronomy12112767

Received: 26 September 2022 / Revised: 28 October 2022 / Accepted: 29 October 2022 /

Published: 7 November 2022

<footnote n="1">¹ School of Agriculture, Food and Wine, Waite Campus, University of Adelaide, PMB1, Glen Osmond, SA 5064, Australia</footnote>

<footnote n="2">² Wheat Initiative, JKl (Julius Kühn Institute), Federal Research Centre for Cultivated Plants, 14195 Berlin, Germany</footnote>

<footnote n="3">³ INRAE, URGI, Université Paris-Saclay, 78026 Versailles, France</footnote>

<footnote n="4">⁴ ASUR Plant Breeding, 60190 Estrées-Saint-Denis, France</footnote>

<footnote n="5">⁵ CIMMYT (International Maize and Wheat Improvement Center), Texcoco 56237, Mexico</footnote>

<footnote n="6">⁶ ICARDA (International Center for Agricultural Research in the Dry Areas), Rabat 10106, Morocco</footnote>

<footnote n="7">⁷ INRA (National Institute for Agricultural Research), Rabat 10090, Morocco</footnote>

<footnote n="8">⁸ AAFC (Agriculture & Agri-Food Canada), Lethbridge Research and Development Centre, Lethbridge, AB T1J 4B1, Canada</footnote>

<footnote n="9">⁹ Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA 5064, Australia</footnote>

<footnote n="10">¹⁰USDA-ARS (United States Department of Agriculture—Agricultural Research Service), Plant Science Research, Raleigh, NC 27695, USA</footnote>

<footnote>+ Show full affiliation list</footnote>

<footnote>* Author to whom correspondence should be addressed.</footnote>
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## Build the community

* **Building an expert working group**

* focus on recruiting diverse profiles covering important countries or geographical areas, institutions, interest groups and scientific fields for wheat research

* **Seeking help from other communities**

* Wheat Data Interoperability Working Group was created as one of the Research Data Alliance working groups

* **Surveys**

* usage of data standards in the wheat research community

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In this paper, we present our experience, the challenges we faced, and the answer we brought for establishing an international research community to build an informational infrastructure. Our hope is that our experience with building wheatis.org will guide current and future research communities to facilitate institutional and international challenges to create global tools and resources to help their respective scientific communities.

https://doi.org/10.12688/f1000research.23525.1
## Build the community

* Fundings

* Meetings (e.g. annual meeting at PAG)

* Workshops

* A successful result

* WheatIS data discovery tool

* Outreach

* Trainings

* Website and social media

* Joint actions with other EWGs

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In this paper, we present our experience, the challenges we faced, and the answer we brought for establishing an international research community to build an informational infrastructure. Our hope is that our experience with building wheatis.org will guide current and future research communities to facilitate institutional and international challenges to create global tools and resources to help their respective scientific communities.

https://doi.org/10.12688/f1000research.23525.1

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### MAIN RESEARCH AREA

* Information systems are recognized today as essential tools for federating scientific communities. They facilitate the sharing of data

### PURPOSE OF THE EWG

Creation and animation of a network of experts, collaborating to provide the scientific community with wheat genetic and genomic data.

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### EWG CHAIRS AND CO-CHAIRS

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<figure><img src="image_76.png" /><figcaption>Luis CACCAMO
(NIAB)</figcaption></figure>

<caption>Taner SEN
(USDA-ARS)</caption>

<caption>Sarah DYER
(EMBL-EBI)</caption>

<caption>Dave EDWARDS
(UWA)</caption>

* The Wheat Information System EWG federates the diverse information sources (e.g. genetic, genomic, phenotypic, environmental), allowing their findability and integration.

COME A
MBER
HEATTHEWORLD

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WHEAT
INITIATIVE

WheatIS website
http://www.wheatis.org/

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WheatIS Data Discovery
https://www.versailles.inrae.fr/wheatis/

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Webinar
https://www.elixir-europe.org/materials/wheatis-datadiscovery-webinar

A single entry point for the wheat community to find available data through a full text search engine (WheatIS Data Discovery), allowing searching data in all the wheat databases around the world.

### MAIN CHALLENGES IN THE FIELD

* To maintain effective communication with stakeholders and users to ensure proper tool development and data integration.

* To ensure up-to-date data in the WheatIS Data Discovery.

* To index new databases (especially from Asia) in the WheatIS Data Discovery.

* To improve the FAIRness of the data and metadata.

The EWG is open to Early Career Researchers

21 experts from Europe, America, Asia, Oceania including 6 early career researchers

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About

Collaborators

Search

Data Standards

Submit Data

Tools

Links

WheatIS Nodes

WheatIS

## About

**Help desk:** If you have questions regarding this Wheat Information System project, please contact wheatis-contact @ wheatis.org

This project aims at building an International Wheat Information System, called hereafter WheatIS, to support the wheat research community. The main objective is to provide a single-access web base system to access to the available data resources and bioinformatics tools.

This project is based on the principles listed below:

* Collective building of the WheatIS to better respond to the needs of the international wheat community;

* Incremental implementation to offer rapidly an operational information system;

* Emphasis on Quality Assurance to serve as a framework for an approach with incremental implementation;

* Promotion of an open-access model for data exchange;

* Reliance on a distributed system;

* Use of Virtual Machine and Cloud Computing technologies to facilitate sharing data and tools;

* Promotion of the visibility of each participating platform to contribute to their sustainability.

If you have questions regarding this Wheat Information System project, please contact: wheatis-contact @ wheatis.org

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wheatIS

WHEAT INFORMATION SYSTEM

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DATA STANDARDIZATION

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RESEARCH DATA ALLIANCE

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## Community driven methodology

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### OPINION ARTICLE

**REVISED** Developing data interoperability using standards: A wheat community use case [version 2; peer review: 2 approved]

Esther Dzale Yeumo¹, Michael Alaux ¹D,², Elizabeth Arnaud³, Sophie Aubin¹, Ute Baumann⁴, Patrice Buche⁵, Laurel Cooper ¹D,⁶, Hanna Ćwiek-Kupczyńska⁷, Robert P. Davey ¹D⁶, Richard Allan Fuls⁹, Clement Jonquet ¹D¹⁰,¹¹, Marie-Angelique Laporte³, Pierre Larmande ¹D¹²,¹³, Cyril Pommier ¹D², Vassilis Protonotarios ¹D¹⁴, Carmen Reverte ¹D¹⁵, Rosemary Shrestha⁹, Imma Subirats¹⁶, Aravind Venkatesan ¹D¹², Alex Whan¹⁷,✉ Hadi Quesneville ¹D²

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Author details

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This article is included in the Agriculture, Food and Nutrition gateway.

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### Abstract

In this article, we present a joint effort of the wheat research community, along with data and ontology experts, to develop wheat data interoperability guidelines. Interoperability is the ability of two or more systems and devices to cooperate and exchange data, and interpret that shared information. Interoperability is a growing concern to the wheat scientific community, and agriculture in general, as the need to interpret the deluge of data obtained through high-throughput technologies grows. Agreeing on common data formats, metadata, and vocabulary standards is an important step to obtain the required data interoperability level in order to add value by encouraging data sharing, and subsequently facilitate the extraction of new information from existing and new datasets.

During a period of more than 18 months, the RDA Wheat Data Interoperability Working Group (WDI-WG) surveyed the wheat research community about the use of data standards, then discussed and selected a set of recommendations based on consensual criteria. The recommendations promote standards for data types identified by the wheat research community as the most important for the coming years: nucleotide sequence variants, genome annotations, phenotypes, germplasm data, gene expression experiments, and physical maps. For each of these data types, the guidelines recommend best practices in terms of use of data formats, metadata standards and ontologies. In addition to the best practices, the guidelines provide examples of tools and implementations that are likely to facilitate the adoption of the recommendations.

To maximize the adoption of the recommendations, the WDI-WG used a community-driven approach that involved the wheat research community from the start, took into account their needs and practices, and provided them with a framework to keep the recommendations up to date. We also report this approach's potential to be generalizable to other (agricultural) domains.
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# RDA Wheat Data Interoperability Working Group

* Dissemination

* Website

* Agroportal for wheat ontologies

* Adoption

OPINION ARTICLE

**REVISED** Developing data interoperability using standards: A wheat community use case [version 2; peer review: 2 approved]

Esther Dzale Yeumo¹, Michael Alaux ¹,², Elizabeth Arnaud³, Sophie Aubin¹, Ute Baumann⁴, Patrice Buche⁵, Laurel Cooper ¹,⁶, Hanna Ćwiek-Kupczyńska⁷, Robert P. Davey ¹,⁶, Richard Allan Fulss⁹, Clement Jonquet ¹,¹⁰,¹¹, Marie-Angélique Laporte³, Pierre Larmande ¹,¹²,¹³, Cyril Pommier ¹,², Vassilis Protonotarios ¹,¹⁴, Carmen Reverte ¹,¹⁵, Rosemary Shrestha⁹, Imma Subirats¹⁶, Aravind Venkatesan ¹,¹², Alex Whan¹⁷, ✉ Hadi Quesneville ¹,²

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Author details

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This article is included in the Agriculture, Food and Nutrition gateway.

**Abstract**

In this article, we present a joint effort of the wheat research community, along with data and ontology experts, to develop wheat data interoperability guidelines. Interoperability is the ability of two or more systems and devices to cooperate and exchange data, and interpret that shared information. Interoperability is a growing concern to the wheat scientific community, and agriculture in general, as the need to interpret the deluge of data obtained through high-throughput technologies grows. Agreeing on common data formats, metadata, and vocabulary standards is an important step to obtain the required data interoperability level in order to add value by encouraging data sharing, and subsequently facilitate the extraction of new information from existing and new datasets.

During a period of more than 18 months, the RDA Wheat Data Interoperability Working Group (WDI-WG) surveyed the wheat research community about the use of data standards, then discussed and selected a set of recommendations based on consensual criteria. The recommendations promote standards for data types identified by the wheat research community as the most important for the coming years: nucleotide sequence variants, genome annotations, phenotypes, germplasm data, gene expression experiments, and physical maps. For each of these data types, the guidelines recommend best practices in terms of use of data formats, metadata standards and ontologies. In addition to the best practices, the guidelines provide examples of tools and implementations that are likely to facilitate the adoption of the recommendations.

To maximize the adoption of the recommendations, the WDI-WG used a community-driven approach that involved the wheat research community from the start, took into account their needs and practices, and provided them with a framework to keep the recommendations up to date. We also report this approach's potential to be generalizable to other (agricultural) domains.

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Wheat Data Interoperability Guidelines

Home

Guidelines

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Ontologies & Vocabularies

Use cases

<figure><img src="image_101.png" /></figure>

Getting involved

About

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Welcome

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 initiative that aims to reinforce synergies between bread and durum wheat national and international research programmes to increase food security, nutritional value and safety while taking into account societal demands for sustainable and resilient agricultural production systems. All the standards and databases presented in these recommendations are referenced into the FAIRsharing website.

More specifically, the WG aims to:

<figure><img src="image_104.png" /></figure>

**Promote** the adoption of common standards, vocabularies and best practices for Wheat data management

<figure><img src="image_105.png" /></figure>

**Facilitate** access, discovery and reuse of wheat data

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**Facilitate** wheat data integration

Guidelines

Under the submenus of this section you will find information on the best practices, tools, recommendations and examples to create, manage and share data related to Wheat. It includes subsections for each of the following data types: What are currently the most used and relevant vocabularies in the context of Wheat Initiative?

Sequ

Gene

Phen

Germ

Gene

Physi

Exempl

From December to 2014 to January 2015 the editorial team conducted a survey "Towards a Comprehensive Overview of Ontologies and Vocabularies for Research on Wheat". The objective was to collect information about the visibility, interoperability, domain, content and other technical aspects of relevant ontologies and vocabularies. As a result, in February 2015 a report (link) was published, and also a list of vocabularies listed as follows:

1. AGROVOC

2. Biorefinery

3. CAB Thesaurus (CABT)

4. Cell Ontology (CL)

5. Chemical Entities of Biological Interest (ChEBI)

6. Crop Ontology (CO)

7. Crop Research Ontology - part of Crop Ontology (CO_715)

8. Environment Ontology (ENVO)

9. Experimental Factor Ontology (EFO)

10. Feature Annotation Location Description Ontology (FALDO)

11. NAL Thesaurus (NALT)

12. Phenotype And Trait Ontology (PATO)

13. Plant Experimental Conditions Ontology (Plant Environment Ontology, EO, may be changing to PECO)

Links

FAIRshare

Use cases

<figure><img src="image_107.png" /></figure>

Clic on the titles for details

1. QUERY ON TRIAL DATA ASSOCIATED WITH VARIETIES

2. DECIPHER THE EFFECT OF SNPS

3. IDENTIFICATION OF WHEAT GENES THAT CONTROL ROOT GROWTH

4. QUERY DATA BY GERMPLASM/GENOTYPE

5. QUERY PHENOTYPE TRIALS TO BUILD INTEGRATIVE DATASET FOR PHENOTYPE OR GENETIC ANALYSIS.

6. QUERY ON GERMPLASM WITH SPECIFIC TRAITS

<figure><img src="image_108.png" /></figure>
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https://urgi.versailles.inrae.fr/wheatis
▶ Federated data portal

<figure><img src="image_111.png" /></figure>
## Repositories

### INRAE-URGI

IWGSC@GnplS [19,195,264]

GnplS [624,827]

brapi@INRAE-URGI [15,949]

OpenMinTeD@GnplS [1,692]

WheatIS File Repository [6]

Research Data Gouv [4]

### CAU

TGT [5,041,169]

wGRN [270,235]

WheatUnion [105,200]

pNOGmap [118]

### EBI

Ensembl Plants [1,954,199]

### Gramene

Gramene Plants [1,954,199]

### IPK

CR-EST [199,220]

GEBIS [51,820]

MetaCrop [355]

e!DAL - PGP Repository [144]

### Data type

- ☐ Genome

annotation [14,176,618]

- ☐ Gene annotation [2,861,241]

- ☐ Physical map

feature [2,157,405]

<figure><img src="image_112.png" /></figure>

<figure><img src="image_113.png" /></figure>

<figure><img src="image_114.png" /></figure>

InterMine

<figure><img src="image_115.png" /></figure>

### CIMMYT

Publications Repository [1,741]

Data Repository [374]

### EVA

brapi@EVA [710]

### TERRA-REF

brapi@TERRA-REF [284]

### IPGPAS

PlantPhenoDB [6]
> Behind the scene

<figure><img src="image_116.png" /></figure>
> Behind the scene

<figure><img src="image_117.png" /></figure>
> Behind the scene

<figure><img src="image_118.png" /></figure>
> Behind the scene

<figure><img src="image_119.png" /></figure>
> Behind the scene

<figure><img src="image_120.png" /></figure>
## Data exchange format

<figure><img src="image_121.png" /></figure>

BrAPI

* /brapi/v1/germplasm GET

* /brapi/v1/germplasm/{germplasmDbId} GET

* /brapi/v1/variables GET

* /brapi/v1/locations GET

* /brapi/v1/locations/{locationDbId} GET

* /brapi/v1/studies/{studyDbId}/germplasm GET

* /brapi/v1/studies/{studyDbId}/observationvariables GET

* /brapi/v1/trials GET

* /brapi/v1/trials/{trialDbId} GET

or

<figure><img src="image_122.png" /></figure>

Field

<figure><img src="image_123.png" /></figure>

* name

* url

* description

* entryType

* species

* node

* databaseName

<figure><img src="image_124.png" /></figure>

https://github.com/elixir-europe/plant-faidare

* WheatIS species list: Aegilops*, Hordeum*, Triticum*, Wheat*

* Format: JSON or TSV, as you wish!

* Web server: HTTP or FTP, as you wish!

https://urgi.versailles.inrae.fr/wheatis/join
## Data providers

### INRAE-URGI

IWGSC@GnpIS [19,195,264]

GnpIS [642,142]

OpenMinTeD@GnpIS [1,589]

WheatIS File Repository [6]

### EBI

Ensembl Plants [1,168,762]

### IPK

CR-EST [199,220]

GEBIS [51,302]

MetaCrop [355]

### Gramene

Gramene [229,851]

### T3

The Triticeae Toolbox [206,406]

UniProt by T3 [16,607]

### UWA

Wheat Pangenome [167,167]

### South Green

AgroLD [137,060]

### Rothamsted Research

KnetMiner [108,474]

### GrainGenes

GrainGenes [20,190]

Komugi wheat gene catalog by

GrainGenes [3,119]

### PGSB

CrowsNest [13,324]

### CIMMYT

CIMMYT Publications [1,605]

CIMMYT Datasets [183]

### IPGPAS

PlantPhenoDB [6]

## WheatIS

Wheat Information
System

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Triticum [1]

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10.1007/s00122-012-1838-2 - OpenMinTeD@GnpIS

Bibliography

Triticum

Bibliography OpenMinTeD 10.1007/s00122-012-1838-2 Mapping QTL for <span class='openminded_phenotype'>resistance to eyespot of <span class='openminded_taxon'>[...]</span> 415</span>, and <span class='openminded marker'>cfd2</span> are tightly linked to Q.<span class='open marker'>span class='ope ... (Expand)</span>

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Bibliography [1]

Uniprot (AAN0000000000)

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IWGSC@GnpIS [4]

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OpenMinTeD@GnpIS [1]

Data provider

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INRAE-URGI [1]

browse chr4A_7333030_7333940_CFD_SSR_TRIMMED - IWGSC@GnpIS

Gene annotation

Triticum aestivum

SEQUENCE FEATURE IWGSC@GnpISchr4A_7333656_7333940_CFD_SSR_TRIMMED Start = 7333656, End = 7333940, Strand = 0, Source = ePCR, Seq_id = chr4A, Size = 283, Marker = CFD2, Id = CFD2, Type = similarity, Motif = ca(11) Triticum aestivum similarity chr4 ... (Expand)

10.1007/s00122-012-1838-2 - OpenMinTeD@GnpIS

Bibliography

Triticum

Bibliography OpenMinTeD 10.1007/s00122-012-1838-2 Mapping QTL for <span class='openminded_phenotype'>resistance to eyespot of <span class='openminded_taxon'>[...]</span> 415</span>, and <span class='openminded marker'>cfd2</span> are tightly linked to Q.<span class='open marker'>span class='ope ... (Expand)</span>
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Triticum aestivum (IWGSC)

Location: 2A:100.113.981-100.117.432

Gene: TraesCS2A02G151900

Trans: TraesCS2A02G151900.1

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Ensembl Plants is produced in collaboration with Gramene

Gene: TraesCS2A02G151900

Description
Uncharacterized protein At3g57150 (Fragment) [Source:Projected from Arabidopsis thaliana (AT3G57150) UniProtKB/TrfEMBL;Acc:C0SVF3]

Location
Chromosome 2A: 100.113.981-100.117.432 forward strand.

About this gene
This gene has 1 transcript (splice variant) and 144_orthologues.

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Genes annotated with high confidence by IWGSC

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# Mapping QTL for resistance to eyespot of wheat in *Aegilops longissima*

Hongyan Sheng, Deven R. See & Timothy D. Murray

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Theoretical and Applied Genetics **125**, 355–366(2012) | Cite this article

402 |

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Citations

## Abstract

Eyespot is an economically important disease of wheat caused by the soilborne fungi *Oculimacula yallundae* and *O. acuformis*. These pathogens infect and colonize the stem base, which results in lodging of diseased plants and reduced grain yield. Disease resistant cultivars are the most desirable control method, but resistance genes are limited in the wheat gene pool. Some accessions of the wheat wild relative *Aegilops longissima* are resistant to eyespot, but nothing is known about the genetic control of resistance. A recombinant inbred line population was developed from the cross PI 542196 (R) × PI 330486 (S) to map the resistance genes and better understand resistance in *Ae. longissima*. A genetic linkage map of the S¹ genome was constructed with 169 wheat microsatellite markers covering 1261.3 cM in 7 groups. F₅ lines (189) were tested for reaction to *O. yallundae* and four QTL were detected in chromosomes 1S¹, 3S¹, 5S¹, and 7S¹. These QTL explained 44 % of the total phenotypic variation in reaction to eyespot based on GUS scores and 63 % for visual disease ratings. These results demonstrate that genetic control of *O. yallundae* resistance in *Ae. longissima* is polygenic. This is the first report of multiple QTL conferring resistance to eyespot in *Ae. longissima*. Markers cfd6, umc67, umc68, and cfd8 are tightly linked to O.Pchwst1.S1

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> New features: ontology annotation

* Link literature with experimental data (genotyping, phenotyping...) using ontology annotation

* Corpus of ~1600 wheat open access publications

* Text mining ontology: Wheat Trait Ontology

* Experimental data ontology: Crop Ontology

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> New features: synonym enrichment

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### Species (290)

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Marquis\*4/2/Stewart\*3/Triticum monococcum - GrainGenes

Germplasm

Triticum aestivum

Germplasm GrainGenes Marquis\*4/2/Stewart\*3/Triticum monococcum Unknown Germplasm Type. Marquis\*4/2/Stewart\*3/Triticum monococcum Triticum aestivum ... (Expand)

### Data type (20)

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2006380 - GEBIS

Triticum monococcum L.

2006380 genbank_passport_data GEBIS Triticum monococcum L. ACCESSION NUMBER: TRI 29537; BOTANICAL NAME: Triticum monococcum L.; OTHER NAMES: Albini; ANNUALITY: spring type; SAMPLE STATUS: Advanced/improved cultivar; COUNTRY OF ORIGIN: Germany; ... (Expand)

### Database (17)

Filter on Database...

617 - GEBIS

Triticum monococcum L. var. vulgare körn

617 genbank_passport_data GEBIS Triticum monococcum L. var. vulgare Körn ACCESSION NUMBER: TRI 617; BOTANICAL NAME: Triticum monococcum L. var. vulgare Körn.; ANNUALITY: spring type; SAMPLE STATUS: Traditional cultivar/landrace; COUNTRY OF ORIGIN: Albania; ... (Expand)

### Data provider

☐ UWA [167 167]

☐ INRAE-URGI [12 224]

☐ GrainGenes [8 008]

☐ IPK [6 706]

☐ CIMMYT [1 696]

☐ T3 [1 353]

☐ Rothamsted Research [63]

☐ Gramene [4]

☐ IPGPAS [1]

2009 - GEBIS

Triticum monococcum L. var. macedonicum papag

2009 genbank_passport_data GEBIS Triticum monococcum L. var. macedonicum Papag ACCESSION NUMBER: TRI 2009; BOTANICAL NAME: Triticum monococcum L. var. macedonicum Papag.; ANNUALITY: spring type; SAMPLE STATUS: Traditional cultivar/landrace; COUNTRY OF ORIGIN: Albania; ... (Expand)

644 - GEBIS

Triticum monococcum L. var. vulgare körn

644 genbank_passport_data GEBIS Triticum monococcum L. var. vulgare Körn ACCESSION NUMBER: TRI 644; BOTANICAL NAME: Triticum monococcum L. var. vulgare Körn.; ANNUALITY: spring type; SAMPLE STATUS: Traditional
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New features: BrAPI sources

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# How to join Plant data discovery Federations (FAIDARE, WheatIS)?

## Overview

The plant data discovery Federations (FAIDARE, wheatIS) provides search data portal that index the metadata from your data resources and then link back to an access page in your system. This indexation can be done using the following approaches:

* Datadiscovery files in a webfolder

* Breeding API (BrAPI) web service endpoint. Provides both datadiscovery and [summary cards](#)

* Breeding API (BrAPI) files in a webfolder. Provides both datadiscovery and [summary cards](#)

Each of those approaches are described below and all assume a minimum information set comprising an URL for link back plus description.

The metadata format must follow the indications below and we invite you to [contact us](#) as soon as possible so that we can provide help and discuss the best way to go ahead.

## Breeding API (BrAPI)

This is the richer approach and will bring you all FAIDARE functionalities. The web services building will enable you to plug any [BrAPI](#) client on your database. The BrAPI file generation is simpler and easier to deploy. Only Germplasms and study are indexed from a BreedingAPI endpoint, with their full description. Those metadata will be used to create summary cards such as The datadiscovery metadata files, following the [specifications](#) below are generated from those summaries. Currently (FEB 2023), FAIDARE indexes BrAPI v1.1+ sources (V1.3 recommended).

## Web services

The breedingAPI full specifications are available on [www.brapi.org](#). The resources indexed are germplasms and study only. Information cards are created using the following calls :

* germplasm (mandatory)

* location (recommended)

* ontology (recommended)

* program

* study (mandatory)

* study/(studyDbId)/observationVariable (recommended)

* study/(studyDbId)/germplasm (mandatory)

* study/(studyDbId)/observationUnit (can be resource intensive and therefore not implemented)

* trial (recommended)

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Experiences about AI in wheat bioinformatics

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› Acknowledgements

wheatIS
WHEAT INFORMATION SYSTEM

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EWG CHAIR AND CO-CHAIRS

<figure><img src="image_177.png" /><figcaption>Michael ALAUX
INRAE, France</figcaption></figure>

<figure><img src="image_178.png" /><figcaption>Mario CACCAMO
NIAB, UK</figcaption></figure>

<figure><img src="image_179.png" /><figcaption>Taner SEN
USDA-ARS, USA</figcaption></figure>

<figure><img src="image_180.png" /><figcaption>Sarah DYER
EMBL-EBI, UK</figcaption></figure>

<figure><img src="image_181.png" /><figcaption>Dave EDWARDS
University of WA
Australia</figcaption></figure>

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Former chair:
Hadi Quesneville

Expert Working Group

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Michael Alaux

Françoise Alfama

Raphael Flores

Nicolas Francillonne

Najwa Lakmouri

Célia Michotey

Cyril Pommier

Anne-Françoise Adam-Blondon

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International
Wheat Genome
Sequencing
Consortium

Isabelle Caugant

Josh Stein

Kellye Eversole

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Teresa Saavedra

Sylvie Cloutier

Peter Langridge

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All data and metadata providers !

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Funded by
the European Union

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PHENOME
French Plant
Phenomic Network

INRAO

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INRAO

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## WheatIS data discovery

https://urgi.versailles.inrae.fr/wheatis

## Related publications:

Alaux et al., *Genome Biology* 2018

https://doi.org/10.1186/s13059-018-1491-4

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Linking the International Wheat Genome Sequencin...

The Wheat@URGI portal has been developed to provide the international community of researchers and breeders with access to the bread wheat reference genome se...

genomebiology.biomedcentral.com

## Alaux et al., The Wheat Genome book 2023

https://doi.org/10.1007/978-3-031-38294-9_2

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