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VIRsiRNApred:一个用于预测靶向人类病毒的小干扰RNA(siRNA)抑制效果的网络服务器。

VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses.

作者信息

Qureshi Abid, Thakur Nishant, Kumar Manoj

机构信息

Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research, Sector 39-A, Chandigarh 160036, India.

出版信息

J Transl Med. 2013 Dec 11;11:305. doi: 10.1186/1479-5876-11-305.

Abstract

BACKGROUND

Selection of effective viral siRNA is an indispensable step in the development of siRNA based antiviral therapeutics. Despite immense potential, a viral siRNA efficacy prediction algorithm is still not available. Moreover, performances of the existing general mammalian siRNA efficacy predictors are not satisfactory for viral siRNAs. Therefore, we have developed "VIRsiRNApred" a support vector machine (SVM) based method for predicting the efficacy of viral siRNA.

METHODS

In the present study, we have employed a new dataset of 1725 viral siRNAs with experimentally verified quantitative efficacies tested under heterogeneous experimental conditions and targeting as many as 37 important human viruses including HIV, Influenza, HCV, HBV, SARS etc. These siRNAs were divided into training (T1380) and validation (V345) datasets. Important siRNA sequence features including mono to penta nucleotide frequencies, binary pattern, thermodynamic properties and secondary structure were employed for model development.

RESULTS

During 10-fold cross validation on T1380 using hybrid approach, we achieved a maximum Pearson Correlation Coefficient (PCC) of 0.55 between predicted and actual efficacy of viral siRNAs. On V345 independent dataset, our best model achieved a maximum correlation of 0.50 while existing general siRNA prediction methods showed PCC from 0.05 to 0.18. However, using leave one out cross validation PCC was improved to 0.58 and 0.55 on training and validation datasets respectively. SVM performed better than other machine learning techniques used like ANN, KNN and REP Tree.

CONCLUSION

VIRsiRNApred is the first algorithm for predicting inhibition efficacy of viral siRNAs which is developed using experimentally verified viral siRNAs. We hope this algorithm would be useful in predicting highly potent viral siRNA to aid siRNA based antiviral therapeutics development. The web server is freely available at http://crdd.osdd.net/servers/virsirnapred/.

摘要

背景

在基于小干扰RNA(siRNA)的抗病毒治疗药物研发过程中,选择有效的病毒siRNA是必不可少的一步。尽管具有巨大潜力,但目前仍没有病毒siRNA疗效预测算法。此外,现有的通用哺乳动物siRNA疗效预测器对病毒siRNA的性能并不令人满意。因此,我们开发了“VIRsiRNApred”,这是一种基于支持向量机(SVM)的方法,用于预测病毒siRNA的疗效。

方法

在本研究中,我们使用了一个包含1725个病毒siRNA的新数据集,这些siRNA在异质实验条件下经过实验验证的定量疗效,并且靶向多达37种重要的人类病毒,包括HIV、流感病毒、丙型肝炎病毒、乙型肝炎病毒、SARS等。这些siRNA被分为训练(T1380)和验证(V345)数据集。重要的siRNA序列特征,包括单核苷酸到五核苷酸频率、二元模式、热力学性质和二级结构,被用于模型开发。

结果

在使用混合方法对T1380进行10倍交叉验证期间,我们在病毒siRNA的预测疗效和实际疗效之间实现了最大皮尔逊相关系数(PCC)为0.55。在V345独立数据集上,我们最好的模型实现了最大相关性为0.50,而现有的通用siRNA预测方法显示的PCC为0.05至0.18。然而,使用留一法交叉验证时,训练和验证数据集上的PCC分别提高到了0.58和0.55。支持向量机的表现优于其他使用的机器学习技术,如人工神经网络、K近邻和REP树。

结论

VIRsiRNApred是第一个使用经过实验验证的病毒siRNA开发的预测病毒siRNA抑制疗效的算法。我们希望该算法将有助于预测高效的病毒siRNA,以辅助基于siRNA的抗病毒治疗药物的开发。该网络服务器可在http://crdd.osdd.net/servers/virsirnapred/免费获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cd/3878835/c2a49b8d1ba2/1479-5876-11-305-1.jpg

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