Gene co-opening network deciphers gene functional relationships†

IF 3.743 Q2 Biochemistry, Genetics and Molecular Biology Molecular BioSystems Pub Date : 2017-09-12 DOI:10.1039/C7MB00430C
Wenran Li, Meng Wang, Jinghao Sun, Yong Wang and Rui Jiang
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引用次数: 11

Abstract

Genome sequencing technology has generated a vast amount of genomic and epigenomic data, and has provided us a great opportunity to study gene functions on a global scale from an epigenomic view. In the last decade, network-based studies, such as those based on PPI networks and co-expression networks, have shown good performance in capturing functional relationships between genes. However, the functions of a gene and the mechanism of interaction of genes with each other to elucidate their functions are still not entirely clear. Here, we construct a gene co-opening network based on chromatin accessibility of genes. We show that genes related to a specific biological process or the same disease tend to be clustered in the co-opening network. This understanding allows us to detect functional clusters from the network and to predict new functions for genes. We further apply the network to prioritize disease genes for Psoriasis, and demonstrate the power of the joint analysis of the co-opening network and GWAS data in identifying disease genes. Taken together, the co-opening network provides a new viewpoint for the elucidation of gene associations and the interpretation of disease mechanisms.

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基因共开网络解读基因功能关系†
基因组测序技术产生了大量的基因组和表观基因组数据,为我们从表观基因组的角度研究全球范围内的基因功能提供了很好的机会。在过去十年中,基于网络的研究,如基于PPI网络和共表达网络的研究,在捕获基因之间的功能关系方面表现良好。然而,基因的功能和基因之间相互作用的机制,以阐明其功能仍不完全清楚。在此,我们构建了一个基于基因染色质可及性的基因共开放网络。我们表明,与特定生物过程或相同疾病相关的基因倾向于聚集在共同开放网络中。这种理解使我们能够从网络中检测功能簇,并预测基因的新功能。我们进一步将该网络应用于银屑病疾病基因的优先排序,并展示了联合开放网络和GWAS数据在识别疾病基因方面的联合分析能力。综上所述,共开放网络为阐明基因关联和解释疾病机制提供了新的视角。
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来源期刊
Molecular BioSystems
Molecular BioSystems 生物-生化与分子生物学
CiteScore
2.94
自引率
0.00%
发文量
0
审稿时长
2.6 months
期刊介绍: Molecular Omics publishes molecular level experimental and bioinformatics research in the -omics sciences, including genomics, proteomics, transcriptomics and metabolomics. We will also welcome multidisciplinary papers presenting studies combining different types of omics, or the interface of omics and other fields such as systems biology or chemical biology.
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