In Bioinformatics (Oxford, England)
MOTIVATION : A digenic genetic interaction (GI) is observed when mutations in two genes within the same organism yield a phenotype that is different from the expected, given each mutation's individual effects. While multiplicative scoring is widely applied to define GIs, revealing underlying gene functions, it remains unclear if it is the most suitable choice for scoring GIs in Escherichia coli. Here, we assess many different definitions, including the multiplicative model, for mapping functional links between genes and pathways in E. coli.
RESULTS : Using our published E. coli GI datasets, we show computa-tionally that a machine learning Gaussian Process (GP)-based definition better identifies functional associations among genes than a multiplicative model, which we have experimentally confirmed on a set of gene pairs. Overall, the GP definition improves the detection of GIs, biological reasoning of epistatic connectivity, as well as the quality of GI maps in E. coli, and, potentially, other microbes.
SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.
Kumar Ashwani, Hosseinnia Ali, Gagarinova Alla, Phanse Sadhna, Kim Sunyoung, Aly Khaled A, Zilles Sandra, Babu Mohan