Vine Bioinformatics - grape genomics for Innovative viticulture

The cultivated grape (Vitis vinifera) has become the world’s leading fruit crop. Grape is unique not only because it is a major global perennial crop but also for its historical and cultural connections with the development of humans. One of the main challenges for viticulture is to sustainably maintain the production of high-quality grape varieties in the face of climate change. Current models predict an increasing disease pressure for grapes, mainly because of warmer and partly drier conditions. Several knowledge gaps exist regarding these challenges.1) Understanding the whole genome diversity of vine: With the advent of next generation sequencing, whole genome data become increasingly available. This data is permanently growing: genomic datasets of hundreds of vine accessions are produced and added each year. The genetic information hidden in this data which in size is roughly comparable with that of the human genome is by far not extracted or even understood.2) Functional Genomics: The genomic information becomes functional after transcription and translation. Omics-data (e.g., transcriptomics, epigenetics, proteomics) are increasingly generated for vine and other cultivated plants to study functional genomics with impact for improving yield, quality, and resistance. However, many issues, related to, e.g., development, stress-response, and resistance are still understudied. This opens novel opportunities to close this gap by analyzing such datasets.3) Machine learning of multidimensional plant omics data. The flood of omics data generated require appropriate processing and analysis for their transformation into useful knowledge. Bioinformatics methods including algorithm development and data science techniques are needed to accomplish these tasks. Machine learning and artificial intelligence in combination with “domain knowledge” are the most promising approaches for extracting the information hidden in these highly complex and multidimensional data.4) Translation into FAIR viticulture. The results of basic research must be translated into practice. As a first step, the generated information must be made available to the scientific community under the FAIR (Findable, Accessible, Interoperable, Re-usable) standards for scientific data. This requirement applies to primary sequencing data, secondary genomic, as well as phenotypic characteristics of the accessions, and also the results of the downstream analyses together with the methods and the models used. Presently, this information is either not available or it is spread over many disjunct data repositories hampering their effective exploitation.