m(7)GDisAI: N7-methylguanosine (m(7)G) sites and diseases associations inference based on heterogeneous network.
m(7)GDisAI: N7-methylguanosine (m(7)G) sites and diseases associations inference based on heterogeneous network.
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m7GDisAI：基于异构网络的N7-甲基鸟苷（m7G）位点与疾病关联推断
DOI：
10.1186/s12859-021-04007-9
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发表时间：
2021-03-24
影响因子：
3
通讯作者：
Liu H
中科院分区：
文献类型：
--
作者：
Ma J;Zhang L;Chen J;Song B;Zang C;Liu H
近年来的研究证实，N7-甲基鸟苷（m7 G）修饰在调节多种生物学过程中起着重要作用，并与多种疾病有关。湿实验室实验对于鉴定疾病相关的m7 G位点是成本和时间无效的。迄今为止，已经通过高通量测序方法鉴定了数万个m7 G位点，并且这些信息在生物信息学数据库中是公开可用的，可以利用这些信息来使用计算视角预测潜在的疾病相关m7 G位点。因此，迫切需要用于m7 G-疾病关联预测的计算方法，但目前没有一种方法是可用的。为了填补这一空白，我们收集了m7 G位点与疾病之间的关联信息，m7 G位点的基因组信息以及来自不同数据库的疾病表型信息，以构建m7 G-疾病关联数据集。为了推断潜在的疾病相关m7 G位点，我们提出了一个基于异构网络的模型，m7 G位点和疾病关联推断（m7 GDisAI）模型。m7 GDisAI通过在异构网络上应用矩阵分解方法，综合m7 G位点和疾病的全面相似性信息，预测潜在的疾病相关m7 G位点。为了评估预测性能，首先进行了10次十倍交叉验证，m7 GDisAI获得了最高的AUC，为0.740（± 0.0024）。通过全局和局部留一法交叉验证（LOOCV）实验分别对模型在全局和局部情况下的准确性进行了评估。总体LOOCV中的AUC为0.769，而局部LOOCV中的AUC为0.635。最后进行了一个案例研究，以确定最有希望的卵巢癌相关的m7 G位点，以进一步的功能分析。通过GO富集分析，探讨了m7 G位点的宿主基因与GO术语之间的复杂关联。结果表明，m7 GDisAI鉴定的疾病相关m7 G位点及其宿主基因与卵巢癌的发病机制一致，可能为疾病的发病机制提供一定的线索。m7 GDisAI网络服务器可以在http：//180.208.58.66/m7GDisAI/访问，其提供了用户友好的界面来查询疾病相关的m7 G。可以实现预测与177种疾病相关的前20个m7 G位点的列表。此外，还显示了关于特定m7 G位点和疾病的详细信息。在线版本包含补充材料，可通过10.1186/s12859-021-04007-9获得。
Recent studies have confirmed that N7-methylguanosine (m7G) modification plays an important role in regulating various biological processes and has associations with multiple diseases. Wet-lab experiments are cost and time ineffective for the identification of disease-associated m7G sites. To date, tens of thousands of m7G sites have been identified by high-throughput sequencing approaches and the information is publicly available in bioinformatics databases, which can be leveraged to predict potential disease-associated m7G sites using a computational perspective. Thus, computational methods for m7G-disease association prediction are urgently needed, but none are currently available at present. To fill this gap, we collected association information between m7G sites and diseases, genomic information of m7G sites, and phenotypic information of diseases from different databases to build an m7G-disease association dataset. To infer potential disease-associated m7G sites, we then proposed a heterogeneous network-based model, m7G Sites and Diseases Associations Inference (m7GDisAI) model. m7GDisAI predicts the potential disease-associated m7G sites by applying a matrix decomposition method on heterogeneous networks which integrate comprehensive similarity information of m7G sites and diseases. To evaluate the prediction performance, 10 runs of tenfold cross validation were first conducted, and m7GDisAI got the highest AUC of 0.740(± 0.0024). Then global and local leave-one-out cross validation (LOOCV) experiments were implemented to evaluate the model’s accuracy in global and local situations respectively. AUC of 0.769 was achieved in global LOOCV, while 0.635 in local LOOCV. A case study was finally conducted to identify the most promising ovarian cancer-related m7G sites for further functional analysis. Gene Ontology (GO) enrichment analysis was performed to explore the complex associations between host gene of m7G sites and GO terms. The results showed that m7GDisAI identified disease-associated m7G sites and their host genes are consistently related to the pathogenesis of ovarian cancer, which may provide some clues for pathogenesis of diseases. The m7GDisAI web server can be accessed at http://180.208.58.66/m7GDisAI/, which provides a user-friendly interface to query disease associated m7G. The list of top 20 m7G sites predicted to be associted with 177 diseases can be achieved. Furthermore, detailed information about specific m7G sites and diseases are also shown. The online version contains supplementary material available at 10.1186/s12859-021-04007-9.
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影响因子：
14.9
作者：
Oliva R;Cavallo L;Tramontano A
通讯作者：
Tramontano A
DOI：
10.2183/pjab.91.394
发表时间：
2015
期刊：
Proceedings of the Japan Academy. Series B, Physical and biological sciences
影响因子：
--
作者：
Furuichi Y
通讯作者：
Furuichi Y
影响因子：
6
作者：
Chan, WY;Cheung, KK;Mok, SC
通讯作者：
Mok, SC
影响因子：
64.8
作者：
BRONNER, CE;BAKER, SM;LISKAY, RM
通讯作者：
LISKAY, RM
影响因子：
--
作者：
Ichikawa, Y;Lemon, SJ;Lynch, HT
通讯作者：
Lynch, HT