2024-03-28T23:03:43Zhttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_oaipmhoai:ipsj.ixsq.nii.ac.jp:000696682023-04-27T10:00:04Z01164:05352:06035:06114
RNA-RNA Interaction Prediction Using Integer Programming with Threshold CutRNA-RNA Interaction Prediction Using Integer Programming with Threshold Cutenghttp://id.nii.ac.jp/1001/00069667/Technical Reporthttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=69668&item_no=1&attribute_id=1&file_no=1Copyright (c) 2010 by the Information Processing Society of JapanBioinformatics Center, Institute for Chemical Research, Kyoto UniversityGraduate School of Frontier Sciences, University of TokyoMizuho Information & Research Institute, Inc/Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST)Department of Mathematical Sciences, Faculty of Science and Engineering, Doshisha UniversityGraduate School of Frontier Sciences, University of Tokyo/Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST)Bioinformatics Center, Institute for Chemical Research, Kyoto UniversityYuki, KatoKengo, SatoMichiaki, HamadaYoshihide, WatanabeKiyoshi, AsaiTatsuya, AkutsuMuch attention has been focused on predicting RNA-RNA interaction since it is a key to identifying possible targets of noncoding small RNAs that regulate gene expression post-transcriptionally. A number of computational studies have so far been devoted to predicting joint secondary structures or binding sites under a specific class of interactions. In this technical report, we propose RactIP, a fast and accurate prediction method for RNA-RNA interaction of general type based on integer programming. RactIP can integrate approximate information on an ensemble of equilibrium joint structures into the objective function using posterior internal and external base paring probabilities. Experimental results show that prediction accuracy of RactIP is at least comparable to that of several state-of-the-art methods for RNA-RNA interaction prediction. Moreover, we demonstrate that RactIP can run incomparably faster than competitive methods for predicting joint secondary structures.Much attention has been focused on predicting RNA-RNA interaction since it is a key to identifying possible targets of noncoding small RNAs that regulate gene expression post-transcriptionally. A number of computational studies have so far been devoted to predicting joint secondary structures or binding sites under a specific class of interactions. In this technical report, we propose RactIP, a fast and accurate prediction method for RNA-RNA interaction of general type based on integer programming. RactIP can integrate approximate information on an ensemble of equilibrium joint structures into the objective function using posterior internal and external base paring probabilities. Experimental results show that prediction accuracy of RactIP is at least comparable to that of several state-of-the-art methods for RNA-RNA interaction prediction. Moreover, we demonstrate that RactIP can run incomparably faster than competitive methods for predicting joint secondary structures.AA12055912研究報告バイオ情報学(BIO)2010-BIO-2132182010-06-112010-06-03