Biomedical subjects

Björn Voss

Publications and source records attributed to Björn Voss.

7 recordsLinked to original sources

PubMed2006

Structural analysis of aligned RNAs.

The knowledge about classes of non-coding RNAs (ncRNAs) is growing very fast and it is mainly the structure which is the common characteristic property shared by members of the same class. For correct characterization of such classes it is therefore of great importance to analyse the structural features in great detail. In this manuscript I present RNAlishapes which combines various secondary structure analysis methods, such as suboptimal folding and shape abstraction, with a comparative approach known as RNA alignment folding. RNAlishapes makes use of an extended thermodynamic model and covariance scoring, which allows to reward covariation of paired bases. Applying the algorithm to a set of bacterial trp-operon leaders using shape abstraction it was able to identify the two alternating conformations of this attenuator. Besides providing in-depth analysis methods for aligned RNAs, the tool also shows a fairly well prediction accuracy. Therefore, RNAlishapes provides the community with a powerful tool for structural analysis of classes of RNAs and is also a reasonable method for consensus structure prediction based on sequence alignments. RNAlishapes is available for online use and download at http://rna.cyanolab.de.

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PubMed2006

Beyond Mfold: recent advances in RNA bioinformatics.

Computational analysis of RNA secondary structure is a classical field of biosequence analysis, which has recently gained momentum due to the manyfold regulatory functions of RNA that have become apparent. We present five recent computational approaches that address the problems of synoptic folding space analysis, pseudoknot prediction, structure alignment, comparative structure prediction, and miRNA target prediction. All these programs are in current use and are available via the Bielefeld Bioinformatics Server at .

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PubMed2006

Complete probabilistic analysis of RNA shapes.

BACKGROUND: Soon after the first algorithms for RNA folding became available, it was recognised that the prediction of only one energetically optimal structure is insufficient to achieve reliable results. An in-depth analysis of the folding space as a whole appeared necessary to deduce the structural properties of a given RNA molecule reliably. Folding space analysis comprises various methods such as suboptimal folding, computation of base pair probabilities, sampling procedures and abstract shape analysis. Common to many approaches is the idea of partitioning the folding space into classes of structures, for which certain properties can be derived. RESULTS: In this paper we extend the approach of abstract shape analysis. We show how to compute the accumulated probabilities of all structures that share the same shape. While this implies a complete (non-heuristic) analysis of the folding space, the computational effort depends only on the size of the shape space, which is much smaller. This approach has been integrated into the tool RNA shapes, and we apply it to various RNAs. CONCLUSION: Analyses of conformational switches show the existence of two shapes with probabilities approximately 2/3 vs. 1/3, whereas the analysis of a microRNA precursor reveals one shape with a probability near to 1.0. Furthermore, it is shown that a shape can outperform an energetically more favourable one by achieving a higher probability. From these results, and the fact that we use a complete and exact analysis of the folding space, we conclude that this approach opens up new and promising routes for investigating and understanding RNA secondary structure.

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PubMed2005

RNAshapes: an integrated RNA analysis package based on abstract shapes.

We introduce RNAshapes, a new software package that integrates three RNA analysis tools based on the abstract shapes approach: the analysis of shape representatives, the calculation of shape probabilities and the consensus shapes approach. This new package is completely reimplemented in C and outruns the original implementations significantly in runtime and memory requirements. Additionally, we added a number of useful features like suboptimal folding with correct dangling energies, structure graph output, shape matching and a sliding window approach.

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PubMed2004

Abstract shapes of RNA.

The function of a non-protein-coding RNA is often determined by its structure. Since experimental determination of RNA structure is time-consuming and expensive, its computational prediction is of great interest, and efficient solutions based on thermodynamic parameters are known. Frequently, however, the predicted minimum free energy structures are not the native ones, leading to the necessity of generating suboptimal solutions. While this can be accomplished by a number of programs, the user is often confronted with large outputs of similar structures, although he or she is interested in structures with more fundamental differences, or, in other words, with different abstract shapes. Here, we formalize the concept of abstract shapes and introduce their efficient computation. Each shape of an RNA molecule comprises a class of similar structures and has a representative structure of minimal free energy within the class. Shape analysis is implemented in the program RNAshapes. We applied RNAshapes to the prediction of optimal and suboptimal abstract shapes of several RNAs. For a given energy range, the number of shapes is considerably smaller than the number of structures, and in all cases, the native structures were among the top shape representatives. This demonstrates that the researcher can quickly focus on the structures of interest, without processing up to thousands of near-optimal solutions. We complement this study with a large-scale analysis of the growth behaviour of structure and shape spaces. RNAshapes is available for download and as an online version on the Bielefeld Bioinformatics Server.

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PubMed2004

Evaluating the predictability of conformational switching in RNA.

MOTIVATION: There are various cases where the biological function of an RNA molecule involves a reversible change of conformation. paRNAss is a software approach to the prediction of such structural switching in RNA. It is based on three hypotheses about the secondary structure space of a switching RNA molecule that can be evaluated by RNA folding and structure comparison. In the positive case, the predicted structural switching must be verified experimentally. RESULTS: After reviewing the strategy used in paRNAss, we present recent improvements on the algorithmic level of the approach, and the results of an evaluation procedure, comprising 1500 RNA sequences. It could be shown that the paRNAss approach performs well on known examples for conformational switching in RNA. The overall number of positive predictions was small, whereas for human 3' UTRs, representing regulatory important regions, it was substantially higher than for arbitrary natural and random sequences. AVAILABILITY: paRNAss is available as a Web service at http://bibiserv.techfak.uni-bielefeld.de/parnass SUPPLEMENTARY INFORMATION: Detailed information on the analyses summarized in Table 1 can be found at http://bibiserv.techfak.uni-bielefeld.de/parnass/examples.html

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PubMedDate not supplied

Pure multiple RNA secondary structure alignments: a progressive profile approach.

In functional, noncoding RNA, structure is often essential to function. While the full 3D structure is very difficult to determine, the 2D structure of an RNA molecule gives good clues to its 3D structure, and for molecules of moderate length, it can be predicted with good reliability. Structure comparison is, in analogy to sequence comparison, the essential technique to infer related function. We provide a method for computing multiple alignments of RNA secondary structures under the tree alignment model, which is suitable to cluster RNA molecules purely on the structural level, i.e., sequence similarity is not required. We give a systematic generalization of the profile alignment method from strings to trees and forests. We introduce a tree profile representation of RNA secondary structure alignments which allows reasonable scoring in structure comparison. Besides the technical aspects, an RNA profile is a useful data structure to represent multiple structures of RNA sequences. Moreover, we propose a visualization of RNA consensus structures that is enriched by the full sequence information.

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