TY - JOUR T1 - Quantitative Epistasis Analysis and Pathway Inference from Genetic Interaction Data A1 - Phenix, Hilary A1 - Morin, Katy A1 - Batenchuk, Cory A1 - Parker, Jacob A1 - Abedi, Vida A1 - Yang, Liu A1 - Tepliakova, Lioudmila A1 - Perkins, Theodore J. A1 - Kærn, Mads Y1 - 2011/05/12 N2 - Author Summary Cells have evolved elaborate pathways that allow them to optimally use available nutrients, for example, and alter gene expression in response to external challenges. The mapping of these pathways provides an understanding of cell function critical for advancements in a number of fields, from biofuel production to drug discovery. In this study, we developed a novel method to map pathways of genes that function in the cellular response to a given signal or stress. The method represents a significant advancement since it takes full advantage of modern genomics techniques to provide novel, detailed information about gene function, including the contribution from different genes individually, and in combination with other genes or pathways. We tested the method on a pathway in yeast whose human equivalent is associated with a serious and potentially fatal hereditary disease called galactosemia. We demonstrate that the method allows a highly accurate reconstruction of this pathway, correctly segregating genes with major and minor functions, and recapitulating the known mechanisms associated with the disease. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 7 IS - 5 UR - https://doi.org/10.1371/journal.pcbi.1002048 SP - e1002048 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002048 ER -