Livi Carmen Maria, Klus Petr, Delli Ponti Riccardo, Tartaglia Gian Gaetano
Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain and.
Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain and Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain.
Bioinformatics. 2016 Mar 1;32(5):773-5. doi: 10.1093/bioinformatics/btv629. Epub 2015 Oct 31.
Recent technological advances revealed that an unexpected large number of proteins interact with transcripts even if the RNA-binding domains are not annotated. We introduce catRAPID signature to identify ribonucleoproteins based on physico-chemical features instead of sequence similarity searches. The algorithm, trained on human proteins and tested on model organisms, calculates the overall RNA-binding propensity followed by the prediction of RNA-binding regions. catRAPID signature outperforms other algorithms in the identification of RNA-binding proteins and detection of non-classical RNA-binding regions. Results are visualized on a webpage and can be downloaded or forwarded to catRAPID omics for predictions of RNA targets.
catRAPID signature can be accessed at http://s.tartaglialab.com/new_submission/signature
gian.tartaglia@crg.es or gian@tartaglialab.com
Supplementary data are available at Bioinformatics online.
最近的技术进展表明,即使RNA结合结构域未被注释,也有大量意想不到的蛋白质与转录本相互作用。我们引入了catRAPID特征,基于物理化学特征而非序列相似性搜索来识别核糖核蛋白。该算法以人类蛋白质为训练对象,并在模式生物上进行测试,计算总体RNA结合倾向,随后预测RNA结合区域。在识别RNA结合蛋白和检测非经典RNA结合区域方面,catRAPID特征优于其他算法。结果在网页上可视化,可下载或转发至catRAPID组学以预测RNA靶标。
可通过http://s.tartaglialab.com/new_submission/signature访问catRAPID特征。
gian.tartaglia@crg.es或gian@tartaglialab.com
补充数据可在《生物信息学》在线获取。