title: A quantitative analysis of monochromaticity in genetic interaction networks.
abstract: BACKGROUND
  A genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in 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. However, no appropriate quantitative measurement for this phenomenon has been proposed.
  
  
  RESULTS
  In this study, we propose the monochromatic index (MCI), which is able 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 deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes.
  
  
  CONCLUSIONS
  In summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil 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).
authors.affiliation: Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
authors.forename: Chien-Hsiang
authors.initials: CH
authors.lastname: Hsu
authors.forename: Tse-Yi
authors.initials: TY
authors.lastname: Wang
authors.forename: Hsueh-Ting
authors.initials: HT
authors.lastname: Chu
authors.forename: Cheng-Yan
authors.initials: CY
authors.lastname: Kao
authors.forename: Kuang-Chi
authors.initials: KC
authors.lastname: Chen
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004843:Epistasis, Genetic; D053858:Metabolic Networks and Pathways; D046912:Multiprotein Complexes / Q000378:metabolism; D010641:Phenotype; D014176:Protein Biosynthesis; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism; D014158:Transcription, Genetic
chemical_list: D046912:Multiprotein Complexes