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Uncover disease genes by maximizing information flow in the phenome-interactome network.

Chen Y, Jiang T, Jiang R - Bioinformatics (2011)

Bottom Line: We demonstrate the effectiveness of this method in prioritizing candidate genes through a series of cross-validation experiments, and we show the possibility of using this method to identify diseases with which a query gene may be associated.As an example application, we apply our method to predict driver genes in 50 copy number aberration regions of melanoma.Our method is not only able to identify several driver genes that have been reported in the literature, it also shed some new biological insights on the understanding of the modular property and transcriptional regulation scheme of these driver genes. ruijiang@tsinghua.edu.cn.

View Article: PubMed Central - PubMed

Affiliation: MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 1000084, China.

ABSTRACT

Motivation: Pinpointing genes that underlie human inherited diseases among candidate genes in susceptibility genetic regions is the primary step towards the understanding of pathogenesis of diseases. Although several probabilistic models have been proposed to prioritize candidate genes using phenotype similarities and protein-protein interactions, no combinatorial approaches have been proposed in the literature.

Results: We propose the first combinatorial approach for prioritizing candidate genes. We first construct a phenome-interactome network by integrating the given phenotype similarity profile, protein-protein interaction network and associations between diseases and genes. Then, we introduce a computational method called MAXIF to maximize the information flow in this network for uncovering genes that underlie diseases. We demonstrate the effectiveness of this method in prioritizing candidate genes through a series of cross-validation experiments, and we show the possibility of using this method to identify diseases with which a query gene may be associated. We demonstrate the competitive performance of our method through a comparison with two existing state-of-the-art methods, and we analyze the robustness of our method with respect to the parameters involved. As an example application, we apply our method to predict driver genes in 50 copy number aberration regions of melanoma. Our method is not only able to identify several driver genes that have been reported in the literature, it also shed some new biological insights on the understanding of the modular property and transcriptional regulation scheme of these driver genes.

Contact: ruijiang@tsinghua.edu.cn.

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Transcriptional network of the 47 predicted driver genes. The predicted genes are marked green and their transcriptional factors are marked blue. The most enriched transcriptional factors, p53, NF-kappaB1, NF-kappaB, PPAR-gamma1and PPAR-gamma2, are marked red.
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Figure 6: Transcriptional network of the 47 predicted driver genes. The predicted genes are marked green and their transcriptional factors are marked blue. The most enriched transcriptional factors, p53, NF-kappaB1, NF-kappaB, PPAR-gamma1and PPAR-gamma2, are marked red.

Mentions: Next, we extract transcription factors of the 47 predicted driver genes from DAVID and examine whether these genes are co-regulated. We find that these genes and their transcription factors form a dense transcriptional regulatory network (Fig. 6). The most enriched transcription factors are P53, NF-kappaB1, NF-kappaB, PPAR-gamma1 and PPAR-gamma2, each of which regulates nine or more genes, suggesting that they are critical in melanoma.Fig. 6.


Uncover disease genes by maximizing information flow in the phenome-interactome network.

Chen Y, Jiang T, Jiang R - Bioinformatics (2011)

Transcriptional network of the 47 predicted driver genes. The predicted genes are marked green and their transcriptional factors are marked blue. The most enriched transcriptional factors, p53, NF-kappaB1, NF-kappaB, PPAR-gamma1and PPAR-gamma2, are marked red.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117332&req=5

Figure 6: Transcriptional network of the 47 predicted driver genes. The predicted genes are marked green and their transcriptional factors are marked blue. The most enriched transcriptional factors, p53, NF-kappaB1, NF-kappaB, PPAR-gamma1and PPAR-gamma2, are marked red.
Mentions: Next, we extract transcription factors of the 47 predicted driver genes from DAVID and examine whether these genes are co-regulated. We find that these genes and their transcription factors form a dense transcriptional regulatory network (Fig. 6). The most enriched transcription factors are P53, NF-kappaB1, NF-kappaB, PPAR-gamma1 and PPAR-gamma2, each of which regulates nine or more genes, suggesting that they are critical in melanoma.Fig. 6.

Bottom Line: We demonstrate the effectiveness of this method in prioritizing candidate genes through a series of cross-validation experiments, and we show the possibility of using this method to identify diseases with which a query gene may be associated.As an example application, we apply our method to predict driver genes in 50 copy number aberration regions of melanoma.Our method is not only able to identify several driver genes that have been reported in the literature, it also shed some new biological insights on the understanding of the modular property and transcriptional regulation scheme of these driver genes. ruijiang@tsinghua.edu.cn.

View Article: PubMed Central - PubMed

Affiliation: MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 1000084, China.

ABSTRACT

Motivation: Pinpointing genes that underlie human inherited diseases among candidate genes in susceptibility genetic regions is the primary step towards the understanding of pathogenesis of diseases. Although several probabilistic models have been proposed to prioritize candidate genes using phenotype similarities and protein-protein interactions, no combinatorial approaches have been proposed in the literature.

Results: We propose the first combinatorial approach for prioritizing candidate genes. We first construct a phenome-interactome network by integrating the given phenotype similarity profile, protein-protein interaction network and associations between diseases and genes. Then, we introduce a computational method called MAXIF to maximize the information flow in this network for uncovering genes that underlie diseases. We demonstrate the effectiveness of this method in prioritizing candidate genes through a series of cross-validation experiments, and we show the possibility of using this method to identify diseases with which a query gene may be associated. We demonstrate the competitive performance of our method through a comparison with two existing state-of-the-art methods, and we analyze the robustness of our method with respect to the parameters involved. As an example application, we apply our method to predict driver genes in 50 copy number aberration regions of melanoma. Our method is not only able to identify several driver genes that have been reported in the literature, it also shed some new biological insights on the understanding of the modular property and transcriptional regulation scheme of these driver genes.

Contact: ruijiang@tsinghua.edu.cn.

Show MeSH
Related in: MedlinePlus