Predicting genetic interactions with random walks on biological networks

Abstract

Background: Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed nonviable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse biological networks and show that our method outperforms established methods.

Brain Structure and Response to Emotional Stimuli as Related to Gut Microbial Profiles in Healthy Women

“…sPLS-DA simultaneously performs feature selection and modelling and achieves sparsity using lasso penalization (43). sPLS-DA operates using a supervised framework to find orthogonal components, linear combinations of a limited set of variables (brain features) that predict class membership.…”

Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality

“…Previous computational approaches developed to systematically study genetic interactions have mainly focused on yeast, where there are genome-wide maps of experimentally determined SL interactions (Chipman and Singh, 2009;Kelley and Ideker, 2005;Szappanos et al, 2011;Wong et al, 2004). In cancer, synthetic lethality has been computationally inferred by mapping SL interactions in yeast to their human orthologs (Conde-Pueyo et al, 2009) and by utilizing metabolic models and evolutionary characteristics of metabolic genes Frezza et al, 2011;Lu et al, 2013).…”

Predicing Yeast Synthetic Lethal Genetic Interactions Using Short Polypeptide Clusters

“…So, it is difficult to select features and understand how features are related to SLGIs. Several computational approaches have been proposed for the prediction of SLGIs; various features, such as protein interactions, gene expression, functional annotation, gene location, protein network characteristics, and genetic phenotype, have been utilized by these methods [10,[17][18][19][20]. However, those methods depend on other genome-wide experimental results.…”