A quantitative measure of monochromaticity reveals alleviating or aggravating interactions between protein complexes

A genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions could allow us to clarify relationships between genes, such as compensation and masking, and to identify functional modules of genes which refer to a group of genes functioning in a consistent manner. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes of the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. In this study, we propose the monochromatic index (MCI) which can be used to quantitatively evaluate the monochromaticity of potential functional modules of genes; and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the shortage of MP-score but also properly incorporate the background effect. The results showed that both within-complex and between-complex connections present significant monochromatic tendency. Furthermore, we also find that negative genetic interactions connect significantly higher proportion of protein complexes in metabolic network, while relatively even number of positive and negative genetic interactions link protein complexes in transcription and translation system. In summary, MCI resolved the bias caused by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. MCI also unveiled features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA) and epistatic miniarray profile (E-MAP)