Predicting RNA-protein interactions using only sequence information.
Predicting RNA-protein interactions using only sequence information.
复制标题
仅使用序列信息预测RNA - 蛋白质相互作用。
DOI：
10.1186/1471-2105-12-489
复制
发表时间：
2011-12-22
影响因子：
3
通讯作者：
Dobbs D
中科院分区：
文献类型：
--
作者：
Muppirala UK;Honavar VG;Dobbs D
RNA-蛋白质相互作用（RPIs）在从基因表达的转录和转录后调节到宿主对病原体的防御等多种细胞过程中发挥重要作用。鉴定RNA-蛋白质相互作用的高通量实验开始提供关于RNA-蛋白质相互作用网络复杂性的有价值的信息，但是昂贵且耗时。因此，需要可靠的计算方法来预测RNA-蛋白质相互作用。我们提出了RPISeq，一个家庭的分类预测RNA-蛋白质相互作用，只使用序列信息。给定RNA和蛋白质的序列作为输入，RPIseq预测RNA-蛋白质对是否相互作用。基于7个字母的简化字母表表示，RNA序列被编码为其核糖核苷酸4聚体组成的标准化载体，蛋白质序列被编码为其3聚体组成的标准化载体。RPISeq的两种变体：RPISeq-SVM，它使用支持向量机（SVM）分类器和RPISeq-RF，它使用随机森林分类器。在从蛋白质-RNA界面数据库（PRIDB）提取的两个非冗余基准数据集上，RPISeq实现了0.96和0.92的AUC（受试者工作特征（ROC）曲线下面积）。在仅包含mRNA-蛋白质相互作用的第三个数据集上，RPISeq的性能与需要关于许多不同特征（例如，mRNA半衰期，GO注释）。此外，使用PRIDB数据训练的RPISeq分类器正确预测了来自E. coli、S. cerevisiae，D. melanogaster，M. musculus和H.智人我们使用RPISeq的实验表明，仅使用序列衍生的信息就可以可靠地预测RNA-蛋白质相互作用。RPISeq为RNA-蛋白质相互作用网络的计算构建提供了一种廉价的方法，并且应该为非编码RNA的功能提供有用的见解。RPISeq作为基于Web的服务器在http://pridb.gdcb.iastate.edu/RPISeq/上免费提供。
RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions. We propose RPISeq, a family of classifiers for predicting RNA-protein interactions using only sequence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens. Our experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. RPISeq is freely available as a web-based server at http://pridb.gdcb.iastate.edu/RPISeq/.
登录
查看更多内容
影响因子：
64.5
作者：
Hafner M;Landthaler M;Burger L;Khorshid M;Hausser J;Berninger P;Rothballer A;Ascano M Jr;Jungkamp AC;Munschauer M;Ulrich A;Wardle GS;Dewell S;Zavolan M;Tuschl T
通讯作者：
Tuschl T
影响因子：
14.9
作者：
Cook KB;Kazan H;Zuberi K;Morris Q;Hughes TR
通讯作者：
Hughes TR
DOI：
10.1007/978-1-60327-563-7_2
发表时间：
2009-01-01
期刊：
PLANT SYSTEMS BIOLOGY
影响因子：
--
作者：
Barkan, Alice
通讯作者：
Barkan, Alice
影响因子：
--
作者：
Cherkassky, V
通讯作者：
Cherkassky, V
DOI：
10.3791/2034
发表时间：
2010-07-02
期刊：
Journal of visualized experiments : JoVE
影响因子：
--
作者：
Hafner, Markus;Landthaler, Markus;Tuschl, Thomas
通讯作者：
Tuschl, Thomas