Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation.
Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation.
复制标题
利用 DNA 甲基化基于加权组织特异性网络预测疾病相关基因
DOI：
10.1186/1755-8794-7-s2-s4
复制
发表时间：
2014
影响因子：
2.7
通讯作者：
Wu FX
中科院分区：
文献类型：
--
作者：
Li M;Zhang J;Liu Q;Wang J;Wu FX
研究背景预测疾病相关基因是生物信息学和系统生物学的重要任务之一。随着高通量技术的发展，大量的蛋白质相互作用被发现，这使得在网络水平上识别疾病相关基因成为可能。然而，基于网络的疾病相关基因的识别仍然是一个挑战，因为相当大的假阳性仍然存在于现有的蛋白质相互作用网络（PIN）。结果考虑到大多数遗传性疾病往往只表现在一个或几个组织的事实，我们构建了组织特异性网络（TSN）整合PIN和组织特异性数据。由于DNA甲基化在复杂疾病的发展中起着不可替代的作用，我们进一步通过使用DNA甲基化来权衡构建的组织特异性网络（WTSN）。一个基于PageRank的方法被开发用于从构建的网络中识别疾病相关基因。为了验证所提出的方法的有效性，我们构造PIN，加权PIN（WPIN），TSN，WTSN分别为结肠癌和白血病。对结肠癌和白血病的实验结果表明，组织特异性数据和DNA甲基化的结合有助于更准确地识别疾病相关基因。结论组织特异性数据和DNA甲基化是研究人类疾病的两个重要因素。与在原始PIN、WPIN或TSN上实现的方法相比，在WTSN上实现的相同方法可以实现更好的结果。基于PageRank的方法在识别WTSN中的疾病相关基因方面优于基于度中心度的方法。
BackgroundPredicting disease-related genes is one of the most important tasks in bioinformatics and systems biology. With the advances in high-throughput techniques, a large number of protein-protein interactions are available, which make it possible to identify disease-related genes at the network level. However, network-based identification of disease-related genes is still a challenge as the considerable false-positives are still existed in the current available protein interaction networks (PIN).ResultsConsidering the fact that the majority of genetic disorders tend to manifest only in a single or a few tissues, we constructed tissue-specific networks (TSN) by integrating PIN and tissue-specific data. We further weighed the constructed tissue-specific network (WTSN) by using DNA methylation as it plays an irreplaceable role in the development of complex diseases. A PageRank-based method was developed to identify disease-related genes from the constructed networks. To validate the effectiveness of the proposed method, we constructed PIN, weighted PIN (WPIN), TSN, WTSN for colon cancer and leukemia, respectively. The experimental results on colon cancer and leukemia show that the combination of tissue-specific data and DNA methylation can help to identify disease-related genes more accurately. Moreover, the PageRank-based method was effective to predict disease-related genes on the case studies of colon cancer and leukemia.ConclusionsTissue-specific data and DNA methylation are two important factors to the study of human diseases. The same method implemented on the WTSN can achieve better results compared to those being implemented on original PIN, WPIN, or TSN. The PageRank-based method outperforms degree centrality-based method for identifying disease-related genes from WTSN.