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

Abstract

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 (Prunus avium 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.

A wide foodomics approach coupled with metagenomics elucidates the environmental signature of potatoes

“…A similar approach, but based on the assessment of two omics databases, was used in. 50 , 51 The PC algorithm evaluates the independence for each pair of variables (X, Y) in a dataset, conditioning on all subsets of all the remaining variables. If their association is persistent, it is considered to be causal.…”

Delving into Causal Discovery in Health-Related Quality of Life Questionnaires

“…Causal discovery goes beyond correlation analysis, aiming to infer causal relationships between variables [38]. It is a tool with wide potential that may aid to validate and/or reveal new knowledge in diverse fields (see, e.g., [39][40][41][42][43][44][45][46]). Common methods in causal discovery include causal Bayesian networks (BNs), which are graphical models representing and describing the causal relations between random variables through a directed acyclic graph (DAG), and structural equation modeling (SEM), which investigates the relationships between constructs relative to a certain phenomenon [47].…”

Single Nucleotide Polymorphisms’ Causal Structure Robustness within Coronary Artery Disease Patients

“…Causal structure investigation, and DAGs, have been employed by authors of this work in several studies. For example, in [9], causal discovery was employed in sweet cherry multi-omics data, leading to understanding of cause-effect relationships that are important in the fruit softening and ripening process. In addition, a proteogenomic-based causal model analysis unveiled key interaction networks that are involved in salt priming in olive trees [10].…”