This document discusses making wheat data FAIR (Findable, Accessible, Interoperable, and Reusable). It describes achievements in developing a centralized Wheat Information System (WheatIS) portal for data discovery, a Breeding API for programmatic access, and repositories for sequences and files. Challenges include synchronizing technical updates, improving search capabilities, and getting the repositories widely adopted. Standards are being developed and implemented for phenotyping data, traits, and identifiers to improve interoperability. Making data FAIR requires community management and identification of central resources for standards and ontologies.

1. Making wheat data FAIR
eROSA workshop, Montpellier, 6-7/07/2017
Unit of Research in Genomic-Info (URGI), INRA
Anne-Françoise Adam-Blondon
Michael Alaux

2. Aknowledgements
URGI team
Hélène Lucas
WheatIS Expert Working
Group
Mario Caccamo
Dave Edwards
Gerard Lazo…
Financial supports
Esther Dzalé Kaboré
Odile Hologne
Coord P. Kersey
EMBL-EBI (GB)
IPK (D)
WUR-DLO (NL)
H. Quesneville
C. Pommier
M. Alaux
R. Flores
E. Kimmel
G. Cornut
M. Loaec
C. Guerche
F. Philippe
T. Letelllier
M. Lainé
S. Durand
C. Michotey
MIPS (D)
GMI (Aus)
Biogemma (Fr)
IPG-PAS (PL)
Elisabeth Arnaud
eROSA workshop, Montpellier, 6-7/07/2017
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Coord P. Kersey
EMBL-EBI (GB)
Claire Nédellec
Sophie Aubin

3. Wheat Initiative -
Objectives of the Wheat Information
System expert working group
eROSA workshop, Montpellier, 6-7/07/2017
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4. Wheat distributed information system
WheatIS core
File
repository
Web
portal
Search
Integrated
DB
DB
Indexes
WheatIS node
Storage
Distributed Storage
DB
Indexes
WheatIS node
Storage
DB
Indexes
WheatIS node
Storage
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Necessity to implement FAIR principles: Findability,
Accessibility, Interoperability and Reusability

5. Achievements and current challenges
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6. Findability: Data discovery through a common portal
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User web
interface
http://www.wheatis.org/
Lucene
indexes
Common
Data
Model
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Spannagl et al 2016, doi: 10.3835/plantgenome2015.06.0038

7. WheatIS nodes #12
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Challenges:
• Synchronize technical updates of the infrastructure
• Synchronize improvements of the data model
• Searching with increasingly natural language (e.g. for traits)

8. http://www.brapi.org/
• Development of a standard API for data used in breeding:
• Genetic material, phenotyping experiments,
genotyping experiments, ….
• Aligned on/implementation of data standards: MCPD,
MIAPPE, …
• BrAPI end points accross EU in the frame of a project of
the ELIXIR bioinformatic infrastructure:
• programatic access to plant material and phenotyping
experiments (underway)
• improved web portal for data findability using BrAPI
and cross mapping of ontologies (CO, TO and
ontology derived from text mining)
Accessibility: Breeding API initiative
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9. Accessibility: IWGSC central sequence repository
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http://wheat-urgi.versailles.inra.fr/Seq-Repository

10. Accessibility: central file repository
http://wheatis.org/
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User friendly to biologists and data managers, any type of data

11. wheatis.org
Raw data +
metadata
Accessibility: central file repository
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Challenge: get it widely known to and used by the wheat community
along with good practices in term of data standardization
Metadata
indexed and
searchable
under the
central portal

12. Interoperabity, Reusability: standards,
identifiers
wheatis.org
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Globally agreed good practices for data standardization and
identifiers choices

13. Contribution to the development and implementation
of standards for phenotyping
Minimal Information About Plant
Phenotyping Experiments (MIAPPE;
www.miappe.org ):
• Improvement of the standard
(alignment to MCPD,
CropOntology, …)
• Specifications for different type of
plants: annuals, perenials, forest
trees
Description of the phenotyping variables
using the CropOntology standard (TD v5)
in major wheat french and EU projects
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Database
submission format
Metadata
submission
interface
Development of trait
dictionaries for
wheat (CYMMIT,
INRA)

14. Data interoperability/integration:
identification of key objects
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DOI
BioSample ID
Challenge:
• demonstrations of knowledge gain through data integration
• Key object for connecting with other disciplinary data (GPS, variety,
…)
Central role of
BRC managers at
INRA
CO_id
Ideally to be organized
by crop communities

15. Conclusion
Making data FAIR is a lot about community
management (within and between):
• Developpers
• Specialists of ontologies and standards
• Data managers
• Biologists (data producers)
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Crop
projects
(Global)
Infrastructure
projects
Need for identification and long term maintainance of:
• Searchable central repositories of standards and ontologies
for agriculture (e.g. agroportal.lirmm.fr, biosharing.org)
• FAIR tools for data managers/developers for automatic
formatting or format validation (BioSchemas, …)

16. Thank you!
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17. URGI is a node of the french network of bio-
informatics facilities (IFB-ReNaBi)
EPSO-FEBS 2014, le 27/06/14 .017
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18. INRA IS for crops, forest trees and pathogens
Genome •Annotation
•Transcriptome
•SNP / Structural variant
Genetics •Genetic maps
•Genetic markers
•Genetic
resources
Phenotypes
•GxE
•QTL
maps
•GWAS,
GS
URGI DBs
« GnpIS »
EPSO 2016, Prague, 29/06/16
Analysis
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