Hu Xihao, Wong Thomas K F, Lu Zhi John, Chan Ting Fung, Lau Terrence Chi Kong, Yiu Siu Ming, Yip Kevin Y
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China 100084, School of Life Sciences, Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong and Department of Biology and Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China 100084, School of Life Sciences, Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong and Department of Biology and Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, CSIRO Ecosystem Sciences, Canberra, ACT 2601, Australia, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China 100084, School of Life Sciences, Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong and Department of Biology and Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.
Bioinformatics. 2014 Apr 15;30(8):1049-1055. doi: 10.1093/bioinformatics/btt757. Epub 2013 Dec 27.
High-throughput sequencing has been used to probe RNA structures, by treating RNAs with reagents that preferentially cleave or mark certain nucleotides according to their local structures, followed by sequencing of the resulting fragments. The data produced contain valuable information for studying various RNA properties.
We developed methods for statistically modeling these structure-probing data and extracting structural features from them. We show that the extracted features can be used to predict RNA 'zipcodes' in yeast, regions bound by the She complex in asymmetric localization. The prediction accuracy was better than using raw RNA probing data or sequence features. We further demonstrate the use of the extracted features in identifying binding sites of RNA binding proteins from whole-transcriptome global photoactivatable-ribonucleoside-enhanced cross-linking and immunopurification (gPAR-CLIP) data.
The source code of our implemented methods is available at http://yiplab.cse.cuhk.edu.hk/probrna/ CONTACT: kevinyip@cse.cuhk.edu.hk Supplementary information: Supplementary data are available at Bioinformatics online.
高通量测序已被用于探测RNA结构，方法是用根据RNA局部结构优先切割或标记特定核苷酸的试剂处理RNA，然后对产生的片段进行测序。所产生的数据包含用于研究各种RNA特性的有价值信息。
我们开发了对这些结构探测数据进行统计建模并从中提取结构特征的方法。我们表明，提取的特征可用于预测酵母中的RNA“邮政编码”，即不对称定位中由She复合体结合的区域。预测准确性优于使用原始RNA探测数据或序列特征。我们进一步证明了提取的特征在从全转录组全局光活化核糖核苷增强交联和免疫纯化（gPAR-CLIP）数据中识别RNA结合蛋白结合位点方面的应用。
我们所实现方法的源代码可在http://yiplab.cse.cuhk.edu.hk/probrna/获取。联系方式：kevinyip@cse.cuhk.edu.hk。补充信息：补充数据可在《生物信息学》在线获取。
