Genecentric: a package to uncover graph-theoretic structure in high-throughput epistasis data. - Gallant, Andrew.; Leiserson, Mark DM.; Kachalov, Maxim.; Cowen, Lenore J.; Hescott, Benjamin J. (2016)
Background: New technology has resulted in high-throughput screens for pairwise genetic interactions in yeast and other model organisms. For each pair in a collection of non-essential genes, an epistasis score is obtained, representing how much sicker (or healthier) the double-knockout organism will be compared to what would be expected from the sickness of the component single knockouts. Recent algorithmic work has identified graph-theoretic patterns in this data that can indicate functional modules, and even sets of genes that may occur in compensatory pathways, such as a BPM-type schema first introduced by Kelley and Ideker. However, to date, any algorithms for finding such patterns in the data were implemented internally, with no software being made publically available.; Springer Open.