Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
BMC Bioinformatics. 2011;12 Suppl 13(Suppl 13):S16. doi: 10.1186/1471-2105-12-S13-S16. Epub 2011 Nov 30.
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.
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.
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).
遗传相互作用是指当同时扰动两个基因时，表型与预期的偏离。研究遗传相互作用有助于澄清基因之间的关系，如补偿和掩蔽，并识别功能模块的基因群。最近，进行了几项用于测量定量（正和负）遗传相互作用的全基因组实验。结果表明，同一模块中的基因通常以一致的方式相互作用（纯正或负）；这种现象被称为单色调。单色调可能是揭示细胞网络模块化的潜在原则。然而，尚未提出用于此现象的适当定量测量方法。
在本研究中，我们提出了单色调指数（MCI），它能够定量评估基因潜在功能模块的单色调，并使用 MCI 研究不同细胞子系统中的遗传景观。我们证明了 MCI 不仅弥补了 MP-score 的缺陷，而且还适当纳入了背景效应。结果表明，不仅在复合物内，而且在复合物之间都存在显著的单色调趋势。此外，我们还发现代谢网络中连接蛋白质复合物的负遗传相互作用的比例显著更高，而转录和翻译系统则采用相对均匀的正和负遗传相互作用来连接蛋白质复合物。
总之，我们证明 MCI 改善了 MP-score 所遭受的缺陷，并可以用于定量评估单色调。此外，它还有助于揭示不同细胞子系统中遗传景观的特征。此外，MCI 可以轻松应用于不同类型的遗传相互作用方法（如合成遗传阵列（SGA）和上位性微阵列谱（E-MAP））产生的数据。
