Work package 2: Informatics

Lead partner: BIOVER. Involved partners: all partners

Work package leader: Dr Ehsan Dulloo, Bioversity International, Email:


The main objectives of this work package are to:

  1. To produce a web-based crop wild relative (CWR) and landrace (LR) Trait Information Portal (TIP) building on existing databases that will: (a) provide useful trait information (phenomics, genomics and transcriptomics data) on European CWR and LR diversity, particularly for the case study genera, Avena, Beta, Brassica and Medicago; (b) provide baseline biodiversity information on CWR and LR diversity and its conservation; (c) establish links with related existing information systems regarding genomic characterization (e.g., EMBL Nucleotide Sequence Database) and ensure integration with other relevant plant genetic resources for food and agriculture (PGRFA) information systems (e.g., the Crop Wild Relative Information System (CWRIS), European Internet Search Catalogue of Ex Situ PGR Accessions across Europe (EURISCO), European Central Crop Databases (ECCDBs) and European Native Seed Conservation Network (ENSCONET)).
  2. To research predictive characterization as a means of identifying CWR and LR in situ populations/ex situ accessions of diverse crop types (Avena for cereals, Beta for root/tubers, Brassica for leafy vegetables, and Medicago for legumes) which are likely to contain desirable traits through the innovative approach of Focused Identification of Germplasm Strategy (FIGS), as well as to explore the broad utilization of FIGS methodology to aid breeders’ selection of CWR and LR accessions.

Description of work

The work package consists of two main tasks which correspond to each of the stated objectives above:

Task 2.1: Trait Information Portal. Task leader: BIOVER. Involved partners: all partners

There are several databases and information systems in Europe capable of providing useful trait-based information on plant genetic resources of the four crops under study. Databases already exist for:

These databases are all web-based systems composed of passport data, and some also include trait description, method description and characterization and evaluation data. They run parallel and validated taxonomic systems, map characterization and evaluation traits of georeferenced accessions and allow the visualization of the geographic pattern of specific diseases. Each of them is a core element of the European Cooperative Programme for Plant Genetic Resources (ECPGR) Working Groups for their crops; the development of an international information system has been recommended by the user community. Furthermore, these databases are complemented by the web-based CWRIS–AEGRO–Population Level Information System (PLIS), with in situ population level data for three of the four target crops, namely Avena, Beta and Brassica.

For the purpose of this project, the work package consortium will carry out research using novel information technology tools to develop a CWR and LR TIP which will draw upon various existing documentation systems, as well as new sources of information (phenomic, genomic, and transcriptomic traits of CWR and LR of the four target crops) that will be generated by other work packages (especially work package 1). In addition, European CWR and LR inventory data, as well as existing molecular data (e.g. EMBL Nucleotide Sequence Database), will be considered. The purpose of TIP is to provide breeders with information about where individual traits, such as pest and diseases resistance yield, abiotic stresses including drought, frost susceptibility and other traits of importance to breeders,  can be located. Specifically within the project, information on resistance to sap feeding insects will be evaluated in WP1 , but this WP will research any available information of the above-mentioned traits from existing information sources about  gene bank accessions or populations  (e.g. European Central Crop database, collection holders etc.). The portal would link individual traits of interest, for which information is available, as well as data generated in this project, to individual populations/ex situ accessions of CWR and LR. The advantage of TIP is that it will provide a unique entry point for the breeder community to access trait-specific information to help direct their research and allow them to obtain germplasm on CWR and LR for their breeding work. Such a system aims at presenting and managing this type of information to better meet breeders’ needs.

This task is divided into five subordinate activities:

  1. Conceptualization of a CWR and LR TIP. This activity will involve all the partners, including breeders, to develop TIP infrastructure framework, including its ontology and technical specifications. TIP will be able to link to data generated in other work packages, namely the CWR and LR inventories (WP3 and 4, respectively) and characterization data (phenomic, genomic and transcriptomic) (WP1).
  2. Definition of the key sources of information that will permit the development of TIP and its core infrastructure. In particular, CWRIS/PLIS, EURISCO, relevant crop specific ECCDB (i.e., Avena, Beta, Brassica and Medicago databases maintained by members of the work package consortium) and EMBL will constitute some of the main sources of information.
  3. Development of a TIP preliminary version for testing and adjustments.
  4. Testing and validation of TIP preliminary version with the other WP leaders and collaborating breeders and revising of TIP to ensure it meets the breeders’ demands (in close collaboration with WP5).
  5. Development of the final version of TIP, after necessary adjustments.

Task 2.2: Predictive characterization. Task Leader: BIOVER. Involved partners: UoB, DLO, BIOVER, UNIPG, JKI, MTT, URJC, SXS, UNOTT

One of the first steps of this work package is to identify a set of populations of CWR and LR most likely to contain the traits of interest. This will be done using a technique of predictive characterization called Focused Identification of Germplasm Strategy (FIGS), which aims to predict which in situ populations or ex situ germplasm accessions possess the desired adapted traits (e.g. insect pest resistance). FIGS is an innovative approach that brings together information available on plant genetic resources and the environments in which they evolved through geographic information system (GIS) technology. It uses climatic and ecogeographic information, combined with distribution data of the target species (in this case CWR and LR of target crops), as well as distribution of pest and diseases for which resistance is being sought, in order to create environmental profiles of the habitats in which a given population (genotype) evolved. From this information, FIGS finally identifies the populations or accessions most likely to contain the desirable adaptive traits. For example, FIGS has successfully identified seven new resistance alleles to powdery mildew (genePm3) from an initial number of 16,089 wheat accessions (see Bhullar et al., 2009). In this work package, FIGS will use existing characterization and evaluation data in ECCDB to identify a ‘core’ set of locations with potential resistance for each trait of interest to breeders. The set of populations and accessions identified for Brassica, and Medicago CWR and LR using FIGS will then be used by other work packages in order to conduct more detailed phenomic, genomic and transcriptomic characterization and will also be evaluated for the desirable pest resistance traits.

This task includes the following six steps:

  1. Compile and gather ecogeographic distribution information on the primary insect diseases of the four target crop genera (Avena, Beta, Brassica, and Medicago) as well as the CWR and LR themselves in Europe (from WP3 on CWR and WP4 on LR). Sources of information include the ECCDBs and project partners;
  2. Use existing characterization and evaluation data to identify sites where required variations exists;
  3. Profile the sites identified in step 2 in terms of environmental, ecological and any other relevant data;
  4. Look for similar environmental profiles amongst other sites and develop a sampling strategy using clustering, principal component analysis etc.;
  5. Identify whether ex situ accessions are available or if it is necessary to collect de novo from the identified sites in order to proceed with the characterization and screening activities;
  6. Identify potential information sources on spatial distribution of pests and diseases for the four selected crop genera in order to facilitate the wide application of the FIGS approach to generate focused subsets of candidate locations with potential resistance to pests and diseases.