The RNAfold web server will predict secondary structures of single stranded RNA or DNA sequences. Current limits are 7, nt for partition function. Thermodynamic Structure Prediction. RNAfold Server predicts minimum free energy structures and base pair probabilities from single RNA or DNA sequences . Publication Date (Web): June 16, Structure of a TrmA-RNA complex: A consensus RNA fold contributes to substrate Published online 16 June
In case you are using our software for your publications you may want to cite: Lorenz, Ronny and Bernhart, Stephan H. See the Changelog for version 2. This extension adds RNA-ligand interactions, e. The feature is easy to use through the command file interface in RNAfold. After almost a year without a new release, we are happy to announce many new features.
Freiburg RNA Tools
This version officially introduces generic hard- and soft-constraints for many of the folding algorithms. Furthermore, RNAfold and the RNAlib interface allow for a simple way to incorporate ligand binding to specific hairpin- or interior-loop motifs.
This version also introduces the new v3. We strongly recommend upgrading your installation to this or a newer version to obtain predictions that are better comparable to RNAstructure or UNAFold.
The secondary structure model of RNA perfectly fits together with modern genomics and transcriptomics since it works at the same level of abstraction, treating nucleotides as basic entities. With the increasing availability of RNA sequence data, and the realization that many of the functional RNAs have evolutionary well-conserved secondary structures, many research groups developed a plethora of specialized tools for various aspects of RNA bioinformatics.
As an alternative to the direct measurement of thermodynamic parameters, for instance, machine learning approaches employing stochastic context free grammars SCFG were introduced e.
ViennaRNA Package 2.0
They are, in fact, very close cousins of the minimum free energy and partition function folding algorithms. The contrafold tools in fact recently bridged the apparent gap between the thermodynamic and the machine learning approach to RNA bioinformatics proposing to learn a parameter set for a SCFG that structurally matches the standard energy model [ 18 ].
Several other tools implement dynamic programming based RNA secondary structures prediction: RNAstructure [ 20 ] started as a reimplementation of mfold with a graphical user interface in Windows, but is now available for other platforms and has added several additional algorithms such as partition function folding and suboptimal structures. The group around Kiyoshi Asai developed several tools focusing the usage of centroid and maximum expected accuracy MEA estimators, see e.RNA interference (RNAi): by Nature Video
Ye Ding's Sfold program [ 23 ] was the first to introduce stochastic structure sampling. The group around Robert Giegerich provides several RNA related tools, notably the RNAshapes The Vienna RNA Package [ 25 ] has its roots in a series of large-scale simulation studies aiming at an understanding of adaptive evolution on rugged fitness landscapes [ 26 - 28 ] and the statistical properties of the sequence-structure relationships of RNA [ 29 - 31 ] rather than the detailed analysis of individual RNA molecules of biological interest.
The primary design goals for its implementation in the early s, therefore, were twofold. First and foremost, the basic folding algorithms were to be implemented so as to be as efficient as possible in their usage of both CPU and memory resources.
The core algorithms are accessible as a C library, which later on was also equipped with Perl bindings to facilitate interoperability with this commonly used scripting language. Secondly, the interactive programs were to be used mostly in shell-script pipelines, hence they use a simple command-line interface and, where possible, they read from and write to a stream.
This feature made it easy to construct a suite of web services [ 32 ] providing easy access to most functionalities of the Vienna RNA Package.
With the rising tide of first genomics and then transcriptomics data, the need for both efficient implementation and easy incorporation into pipelines remained, even though the focus gradually shifted from large-scale simulation to large-scale data analysis. Little has changed in the core folding algorithms in the 17 years since the first publication [ 25 ] of the package.
On the other hand, a variety of variants have been included such as consensus structure prediction from alignments or scanning versions capable of dealing with local structures in genome-scale data sets.
TBI - ViennaRNA Package 2
The systematic overhaul of the Vienna RNA Package documented here was largely triggered by the publication of improved parametrizations of the energy model, which affected nearly every component in the library, and by the progress in computer technology, which led to the widespread deployment of shared-memory multi-core processors. In order to exploit these hardware features a restructuring of the RNA library to make it thread-safe and hence fit for use in concurrent computations was required.
Interactive tools Since its first release, the ViennaRNA Package included interactive command-line tools which enable users to access the high performance implementations of the algorithms via a command-line interface.
To ensure scalability of the use-cases all programs were developed with the objective of handling input- and output-streams, facilitating their integration into UNIX pipes.