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This article is part of the supplement: Tenth International Conference on Bioinformatics. First ISCB Asia Joint Conference 2011 (InCoB/ISCB-Asia 2011): Bioinformatics

Open Access Proceedings

A quantitative analysis of monochromaticity in genetic interaction networks

Chien-Hsiang Hsu1†, Tse-Yi Wang2†, Hsueh-Ting Chu3, Cheng-Yan Kao1,4* and Kuang-Chi Chen5*

Author Affiliations

1 Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan

2 Institute of Information Science, Academia Sinica, Taipei, Taiwan

3 Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

4 Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

5 Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan

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BMC Bioinformatics 2011, 12(Suppl 13):S16 doi:10.1186/1471-2105-12-S13-S16

Published: 30 November 2011

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.

Conclusion

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).