RNA Secondary Structure Prediction Via Energy Density Minimization

Abstract. There is a resurgence of interest in RNA secondary structure prediction problem (a.k.a. the RNA folding problem) due to the discovery of many new families of non-coding RNAs with a variety of functions. The vast majority of the computational tools for RNA secondary structure prediction are based on free energy minimization. Here the goal is to compute a non-conflicting collection of structural elements such as hairpins, bulges and loops, whose total free energy is as small as possible. Perhaps the most commonly used tool for structure prediction, mfold/RNAfold, is designed to fold a single RNA sequence. More recent methods, such as RNAscf and alifold are developed to improve the prediction quality of this tool by aiming to minimize the free energy of a number of functionally similar RNA sequences simultaneously. Typically, the (stack) prediction quality of the latter approach improves as the number of sequences to be folded and/or the similarity between the sequences increase...

• Type: Conference
• Year: 2006
• Where: RECOMB
• Authors: Can Alkan, Emre Karakoç, Süleyman Cenk Sahinalp, Peter J. Unrau, H. Alexander Ebhardt, Kaizhong Zhang, Jeremy Buhler
• Keywords: Computational Biology, Free Energy, RECOMB 2006, Secondary Structure Prediction, Sequence Folding Methods