Could Causal Discovery in Proteogenomics Assist in Understanding Gene-Protein Relations? A Perennial Fruit Tree Case Study Using Sweet Cherry as a Model.

Genome-wide transcriptome analysis is a method that produces important data on plant biology at a systemic level. The lack of understanding of the relationships between proteins and genes in plants necessitates a further thorough analysis at the proteogenomic level. Recently, our group generated a quantitative proteogenomic atlas of 15 sweet cherry ( L.) cv. 'Tragana Edessis' tissues represented by 29,247 genes and 7584 proteins. The aim of the current study was to perform a targeted analysis at the gene/protein level to assess the structure of their relation, and the biological implications. Weighted correlation network analysis and causal modeling were employed to, respectively, cluster the gene/protein pairs, and reveal their cause-effect relations, aiming to assess the associated biological functions. To the best of our knowledge, this is the first time that causal modeling has been employed within the proteogenomics concept in plants. The analysis revealed the complex nature of causal relations among genes/proteins that are important for traits of interest in perennial fruit trees, particularly regarding the fruit softening and ripening process in sweet cherry. Causal discovery could be used to highlight persistent relations at the gene/protein level, stimulating biological interpretation and facilitating further study of the proteogenomic atlas in plants.