Using OneGenE and Grapedia resources to support research in grapevine physiology

Causal relation discovery from observational data is a recent research topic that is being actively studied both theoretically and technically. The OneGenE project applied a causal inference method, the PC algorithm, to big homogeneous datasets of transcriptomic data, such as the Vespucci v1 (2016) and the more recent Vespucci v2 (2021) datasets for Vitis vinifera. To deal with the high computational complexity and large amount of input data, OneGenE method was applied in the framework of distributed computing, within the BOINC project based on volunteers’ availability to run OneGenE on their desktops. The output is represented by a list of causally associated genes for every input gene of the Vespucci matrix, based on the 12X.V1 release of grapevine genome. The next step has been the delivery of this information to the biologists through a website and the development of tools which take it as input for further exploration and analysis. The final aim would be to reconstruct gene regulatory networks, where genes are connected by means of oriented edges representing causal relationships. This model would support gene functional studies and system biology approaches to interpret relevant aspects of plant physiology. The information has been published according to the FAIR principles of data management, and the opportunity to integrate it inside Grapedia is unvaluable to improve its accessibility and usability, as already occurring for the gene annotation integrated from the Gene Ref Catalogue resource. Regulatory networks related to floral initiation and organ specification will be presented as examples.