Published August 2022 | Version preprint
Conference proceeding Open

The BreedingValue integrated data platform

Authors/Creators

  • 1. ROR icon Forschungszentrum Jülich
  • 2. ROR icon James Hutton Institute

Description

Abstract
In the EU-funded project BreedingValue, we are following a holistic approach to generate the necessary data and know-how required to conduct comprehensive breeding programs aimed at developing modern berry cultivars that are both globally competitive and satisfy the demands of a growing market. Not only are we characterizing and storing variation held in plant genetic resources, but also information from pre-breeding material, selection populations, traditional and modern cultivars. The germplasm used in this work is phenotypically characterized in the field and greenhouse and genotypic analyses are performed using molecular markers. A metabolomic study will develop a better understanding of the metabolites underlying environmental stresses and help identify crucial compounds that constitute aroma, flavor and taste characteristics. This will be combined with a sensory study conducted with a trained panel of sensory experts. Finally, consumer preferences across Europe are assessed via an acceptance study and an online survey. 
As part of the Open Data initiative of the EU and following FAIR principles (making data Findable, Accessible, Interoperable and Reusable), data, metadata and results of BreedingValue will be made publicly available using the online platform Germinate (https://germinateplatform.github.io/get-germinate) that serves as data repository for a diverse range of species covering cereals, legumes and solanaceous crops. Not only growers and breeders but also researchers from other disciplines can benefit from the data stored on the platform, share, and re-use it for further purposes. Additionally, data from previous projects as well as published data from other sources will be integrated. Visualization tools will be developed so that users’ can interact and explore their data. Machine learning approaches will reveal trait associations that link phenotype, genetical background and metabolomics. Raw data can be made available for download in standard formats. 
Here we will present the data management platform.

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Additional details

Funding

European Union
Pre-breeding strategies for obtaining new resilient and added value berries 101000747

Dates

Accepted
2022-08

References

  • Alercia, A., Diulgheroff, S., and Mackay, M. (2015). FAO/Biodiversity Multi-Crop Passport Descriptors V.2.1. (MCPD V.2.1), FAO Rome, Italy; Biodiversity International, Rome, Italy, pp. 11.
  • Bolger, A.M., Poorter, H., Dumschott, K., Bolger, M.E., Arend, D., Osorio, S., Gundlach, H., Mayer, K.F., Lange, M., Scholz, U., and Usadel, B. (2019). Computational aspects underlying genome to phenome analysis in plants. Plant J. 97 (1), 182–198.
  • Ćwiek-Kupczyńska, H., Altmann, T., Arend, D., Arnaud, E., Chen, D., Cornut, G., Fiorani, F., Frohmberg, W., Junker, A., Klukas, C., et al. (2016). Measures for interoperability of phenotypic data: minimum information requirements and formatting. Plant Methods 12, 44.
  • Gomez-Cabrero, D., Abugessaisa, I., Maier, D., Teschendorff, A., Merkenschlager, M., Gisel, A., Ballestar, E., Bongcam-Rudloff, E., Conesa, A., and Tegnér, J. (2014). Data integration in the era of omics: current and future challenges. BMC Syst Biol. 8 Suppl 2, I1. doi: 10.1186/1752-0509-8-S2-I1. Epub 2014 Mar 13.
  • Lee, I. (2017). Big data: dimensions, evolution, impacts, and challenges. Bus. Horiz. 60 (3), 293-303.
  • Papoutsoglou, E.A., Faria, D., Arend, D., Arnaud, E., Athanasiadis, I.N., Chaves, I., Coppens, F., Cornut, G., Costa, B.V., Ćwiek-Kupczyńska, H., et al. (2020). Enabling reusability of plant phenomic datasets with MIAPPE 1.1. New Phytol. 227, 260–273.
  • Raubach, S., Kilian, B., Dreher, K., Amri, A., Bassi, F.M., Boukar, O., Cook, D., Cruickshank, A., Fatokun, C., Haddad, N.E., et al. (2021). From bits to bites: advancement of the Germinate platform to support prebreeding informatics for crop wild relatives. Crop Sci. 61, 1538–1566.
  • Schneider, M.V., and Orchard, S. (2011). Omics Technologies, Data and Bioinformatics Principles. In Bioinformatics for Omics Data, Mayer, B., ed. (Methods in Molecular Biology vol. 719, Humana Press), p. 3–30.
  • Senger, E., Osorio, S., Olbricht, K., Shaw, P., Denoyes, B., Davik, J., Predieri, S., Karhu, S., Raubach, S., Lippi, N., et al. (2022). Towards smart and sustainable development of modern berry cultivars in Europe. Plant J. 2022 Jun 24. doi: 10.1111/tpj.15876. Epub ahead of print.
  • Shaw, P., Raubach, S., Hearne, S.J., Dreher, K., Bryan, G., McKenzie, G., Milne, I., Stephen, G., and Marshall, D.F. (2017). Germinate 3: Development of a common platform to support the distribution of experimental data on crop wild relatives. Crop Sci. 57 (3), 1259–1273.
  • Varshney, R.K., Bohra, A., Yu, J., Graner, A., Zhang, Q., and Sorrells, M.E. (2021). Designing future crops: genomics-assisted breeding comes of age. Trends Plant Sci. 26 (6), 631–649.
  • Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.W., da Silva Santos, L.B., Bourne, P.E., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018.