SUMMARY: We present a web service for improving characterization of non-coding RNAs (ncRNAs) from NCBI BLAST outputs, based on a command-line application rboAnalyzer. Briefly, the application extends subject sequences of selected high scoring pairs (HSPs) in BLAST output to their plausible full length, and predicts their homology and secondary structures. The aim of the application is to aid to characterize subject RNAs in HSPs that come uncharacterized in BLAST output. The main advantages of the web-server are easy use and interactive analysis with search, filtering and data export options. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at rboanalyzer.elixir-czech.cz. The website frontend is implemented in Elm, while backend is implemented in Python and served by Apache.

SUMMARY: We present the cpPredictor webserver that implements a novel template-based method for prediction of secondary structure of RNA. The method outperforms available prediction methods as it uses RNA structures of related molecules, either predicted or experimentally identified, as structural templates. The server aims at three major tasks: i) prediction of RNA secondary structures that are difficult to predict by available methods, ii) characterization of uncharacterized RNAs as compatible or incompatible with a chosen template structure and iii) an identification of the most relevant structure among different candidate structures of a single RNA ambiguously predicted by available methods. The web server is accompanied with a comprehensive documentation. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at http://cppredictor.elixir-czech.cz/. The source code of the cpPredictor algorithm is freely available from the webserver under the Apache License, Version 2.0.

RNA tertiary structure prediction approaches can be divided into two groups: de novo methods and template-based modeling. De novo are applicable only for small molecules while in case of medium and large size RNA molecules, template-based modeling needs to be employed. While this type of modeling is quite common in protein structure prediction field, there exist only very few tools for template-based RNA structure prediction. Therefore, we present a methodology for prediction of RNA three dimensional structure (target) utilizing a known structure of a related RNA molecule (template). First, the target and template sequences are aligned. Next, sequentially similar regions in the alignment are identified and corresponding substructures are transferred from template to target. The remaining parts of the target structures are predicted using an external tool. This phase includes treatment of indels and valid linking of the transferred and predicted portions of the target structure. Our proposed method is able to predict even large ribosomal RNA structures when sufficiently similar template is available. The experiments have shown that the main impact on the quality of prediction has the sequence similarity of the template and target and number of indels. For structures with size of hundreds of nucleotides with sequence similarity with template over 50% and ratio of indels up to 50% the method is able to generate target structures up to ten RMSD with respect to the reference structure.

Determining the structure of ribosomal RNAs (rRNAs) is one of the crucial steps in understanding the process of protein synthesis, for which rRNAs are one of the basic components. Nevertheless, due to extreme technical difficulties, spatial (3D) structures have been resolved experimentally for only 14 organisms. Also, computational prediction of 3D rRNA structure is almost impossible, and prediction of secondary structure (the list of base pairs in the folded RNA), an important intermediate step between sequence and 3D structure that is used broadly in modeling of RNA structures, is in the case of rRNAs hindered by both extreme sequence length and high structure complexity. Here we present a proof-of-concept for an rRNA secondary structure prediction method that utilizes known structures as structural templates. Our template-based prediction algorithm determines those regions of the sequence for which structure is being predicted that are conserved well enough so that their secondary structure can be copied over from the template. The structure of the remaining, unconserved regions is predicted using a thermodynamic folding model. Applying a baseline implementation of our algorithm to the E. coli 16S rRNA, we have achieved state-of-the-art recall and precision using the structure of T. thermophilus 16S rRNA as a template.

MOTIVATION: The principal motivation was to design an environment for the development of image-analysis applications which would allow the integration of independent modules into one frame and make available tools for their build-up, running, management and mutual communication. RESULTS: The system was designed as modular, consisting of the core and work modules. The system core focuses on overall management and provides a library of classes for build-up of the work modules, their user interface and data communication. The work modules carry practical implementation of algorithms and data structures for the solution of a particular problem, and were implemented as dynamic-link libraries. They are mutually independent and run as individual threads, communicating with each other via a unified mechanism. The environment was designed to simplify the development and testing of new algorithms or applications. An example of implementation for the particular problem of the analysis of two-dimensional (2D) gel electrophoretograms is presented. The environment was designed for the Windows NT operating system with the use of Microsoft Foundation Class Library employing the possibilities of C++ programming language. AVAILABILITY: Available on request from the authors.