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The RNAKinetics server () is a web interface for the newly developed RNAKinetics software. The software models the dynamics of RNA secondary structure by the means of kinetic analysis of folding transitions of a growing RNA molecule. The result of the modeling is a kinetic ensemble, i.e. a collection of RNA structures that are endowed with probabilities, which depend on time. This approach gives comprehensive probabilistic description of RNA folding pathways, revealing important kinetic details that are not captured by the traditional structure prediction methods. The access to the RNAKinetics server is free.

The structures attained by RNA molecules depend not only on their sequence but also on environmental parameters such as their temperature. So far, this effect has been largely neglected in bioinformatics studies. Here, we show that structural comparisons can be facilitated and more coherent structural models can be obtained when differences in environmental parameters are taken into account. We re-evaluate the secondary structures of the spliced leader (SL) RNAs from the seven eukaryotic phyla in which SL RNA trans-splicing has been described. Adjusting structure prediction to the natural growth temperatures and considering energetically similar secondary structures, we observe striking similarities among Euglenida, Kinetoplastida, Dinophyceae, Cnidaria, Rotifera, Nematoda, Platyhelminthes, and Tunicata that cannot be explained easily by the independent innovation of SL RNAs in each of these phyla. Supplementary Table is available at .

Commonly used RNA folding programs compute the minimum free energy structure of a sequence under the pseudoknot exclusion constraint. They are based on Zuker's algorithm which runs in time O(n³). Recently, it has been claimed that RNA folding can be achieved in average time O(n²) using a sparsification technique. A proof of quadratic time complexity was based on the assumption that computational RNA folding obeys the "polymer-zeta property". Several variants of sparse RNA folding algorithms were later developed. Here, we present our own version, which is readily applicable to existing RNA folding programs, as it is extremely simple and does not require any new data structure. We applied it to the widely used Vienna RNAfold program, to create sibRNAfold, the first public sparsified version of a standard RNA folding program. To gain a better understanding of the time complexity of sparsified RNA folding in general, we carried out a thorough run time analysis with synthetic random sequences, both in the context of energy minimization and base pairing maximization. Contrary to previous claims, the asymptotic time complexity of a sparsified RNA folding algorithm using standard energy parameters remains O(n³) under a wide variety of conditions. Consistent with our run-time analysis, we found that RNA folding does not obey the "polymer-zeta property" as claimed previously. Yet, a basic version of a sparsified RNA folding algorithm provides 15- to 50-fold speed gain. Surprisingly, the same sparsification technique has a different effect when applied to base pairing optimization. There, its asymptotic running time complexity appears to be either quadratic or cubic depending on the base composition. The code used in this work is available at: http://sibRNAfold.sourceforge.net/.

Most of the functional RNA elements located within large transcripts are local. Local folding therefore serves a practically useful approximation to global structure prediction. Due to the sensitivity of RNA secondary structure prediction to the exact definition of sequence ends, accuracy can be increased by averaging local structure predictions over multiple, overlapping sequence windows. These averages can be computed efficiently by dynamic programming. Here we revisit the local folding problem, present a concise mathematical formalization that generalizes previous approaches and show that correct Boltzmann samples can be obtained by local stochastic backtracing in McCaskill’s algorithms but not from local folding recursions. Corresponding new features are implemented in the ViennaRNA package to improve the support of local folding. Applications include the computation of maximum expected accuracy structures from RNAplfold data and a mutual information measure to quantify the sensitivity of individual sequence positions.

Proteins and RNA are biological macromolecules built from linear polymers. The process by which they fold into compact, well-defined, three-dimensional architectures to perform their functional tasks is still not well understood. It can be visualized by Brownian motion of an ensemble of molecules through a rugged energy landscape in search of an energy minimum corresponding to the native state. To explore the conformational energy landscape of small RNAs, single pair Förster resonance energy transfer (spFRET) experiments on solutions as well as on surface-immobilized samples have provided new insights. In this review, we focus on our recent work on two FRET-labeled small RNAs, the Diels-Alderase (DAse) ribozyme and the human mitochondrial tRNA^Lys. For both RNAs, three different conformational states can be distinguished, and the associated mean FRET efficiencies provide clues about their structural properties. The systematic variation of their free energies with the concentration of Mg²⁺ counterions was analyzed quantitatively by using a thermodynamic model that separates conformational changes from Mg²⁺ binding. Furthermore, time-resolved spFRET studies on immobilized DAse reveal slow interconversions between intermediate and folded states on the time scale of ~ 100 ms. The quantitative data obtained from spFRET experiments may likely assist in the further development of theories and models addressing the folding dynamics and (counterion-dependent) energetics of RNA molecules.