Systematic identification of gene annotation errors in the widely used yeast mutation collections

Abstract

The baker's yeast mutation collections are extensively used genetic resources that are the basis for many genome-wide screens and new technologies. Anecdotal evidence has previously pointed to the putative existence of a neighboring gene effect (NGE) in these collections. NGE occurs when the phenotype of a strain carrying a particular perturbed gene is due to the lack of proper function of its adjacent gene. Here we performed a large-scale study of NGEs, presenting a network-based algorithm for detecting NGEs and validating software predictions using complementation experiments. We applied our approach to four datasets uncovering a similar magnitude of NGE in each (7-15%). These results have important consequences for systems biology, as the mutation collections are extensively used in almost every aspect of the field, from genetic network analysis to functional gene annotation.