A Novel Method for Assessing the Statistical Significance of RNA-RNA Interactions Between Two Long RNAs

Journal of computational biology: A journal of computational molecular cell biology

TsukasaFukunaga^1,2,3¹  MichiakiHamada^3,4,5,6,7⁴ 

[1] Address correspondence to:Dr. Tsukasa FukunagaDepartment of Computer ScienceGraduate School of Information Science and TechnologyUniversity of Tokyo7-3-1, HongoBunkyo-KuTokyo 113-8656Japan^1;Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan^4;Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan^5;Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan^2;Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, Tokyo, Japan^3;Graduate School of Medicine, Nippon Medical School, Tokyo, Japan^7;Institute for Medical-Oriented Structural Biology, Waseda University, Tokyo, Japan^6

关键词: RNA bioinformatics;    RNA-RNA interaction;    statistical test;   

DOI  :  10.1089/cmb.2017.0260

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

RNA-RNA interactions are key mechanisms through which noncoding RNA (ncRNA) regions exert biological functions. Computational prediction of RNA-RNA interactions is an essential method for detecting novel RNA-RNA interactions because their comprehensive detection by biological experimentation is still quite difficult. Many RNA-RNA interaction prediction tools have been developed, but they tend to produce many false positives. Accordingly, assessment of the statistical significance of computationally predicted interactions is an important task. However, there is no method to evaluate the statistical significance of RNA-RNA interactions that is applicable to interactions between two long RNA sequences. We developed a method to calculate the p-value for the minimal interaction energy between two long RNA sequences. The developed method depends on the fact that minimum interaction energies of RNA-RNA interactions between long RNAs follow a Gumbel distribution when repeat sequences in RNAs are masked. To show the usefulness of the developed method, we applied it to whole human 5′-untranslated region (UTR) and 3′-UTR sequences to detect novel 5′-UTR-3′-UTR interactions. We thus identified two significant 5′-UTR-3′-UTR interactions. Specifically, the human small proline-rich repeat protein 3 shows conserved 5′-UTR-3′-UTR interactions with some nucleotide variations preserving base pairings among primates. Our developed method enables us to detect statistically significant RNA-RNA interactions between long RNAs such as long ncRNAs. Statistical significance estimates help in identification of interactions for experimental validation and provide novel insights into the function of ncRNA regions.

【 授权许可】

Unknown