71 research outputs found
RNAhybrid: microRNA target prediction easy, fast, and flexible
No full textKrüger J, Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast, and flexible. Nucleic Acids Research. 2006;34(Web Server):W451-W454
Complete probabilistic analysis of RNA shapes
No full textVoß B, Giegerich R, Rehmsmeier M. Complete probabilistic analysis of RNA shapes. BMC Biology. 2006;4(1): 5.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 RNAshapes, 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
Phylogeny meets sequence search
No full textRehmsmeier M, Vingron M. Phylogeny meets sequence search. In: Proceedings of the German Conference on Bioinformatics GCB. 1999: 66-72
Comprehensive prediction of novel microRNA targets in Arabidopsis thaliana
No full textAlves jun. L, Niemeier S, Hauenschild A, Rehmsmeier M, Merkle T. Comprehensive prediction of novel microRNA targets in Arabidopsis thaliana. Nucleic Acids Research. 2009;37(12):4010-4021
Phylogenetic Information Improves Homology Detection
No full textRehmsmeier M, Vingron MV. Phylogenetic Information Improves Homology Detection. Proteins: Structure, Function, and Genetics. 2001;45:360-371
Genome Wide Prediction of Polycomb/Trithorax Response Elements in Drosophila melanogaster
No full textRingrose L, Rehmsmeier M, Dura J-M, Paro R. Genome Wide Prediction of Polycomb/Trithorax Response Elements in Drosophila melanogaster. Developmental Cell. 2003;5:759-771
Sequence Database Search Using Jumping Alignments
No full textSpang R, Rehmsmeier M, Stoye J. Sequence Database Search Using Jumping Alignments. In: Proc. of ISMB 2000. 2000: 367-375
Automatic clustering of large sequence databases
No full textKrause A, Nicodeme P, Rehmsmeier M, Vingron M. Automatic clustering of large sequence databases. In: Proceedings of the German Conference on Bioinformatics GCB. 1998
Database searching with phylogenetic trees
No full textRehmsmeier M. Database searching with phylogenetic trees. Bielefeld (Germany): Bielefeld University; 2001.Database searching and phylogenetic tree reconstruction are two major fields of computational sequence analysis. This thesis introduces a combination of both: a database search method that is based on phylogenetic trees - treesearch. A given protein family is described by its multiple alignment and its phylogenetic tree. A database sequence that is tested for membership in the family is tentatively inserted into that tree. The result of this operation determines how well the sequence fits into the family. The idea is realized in the distance based context of phylogeny. To assess the performance of the method in terms of sensitivity and selectivity, it is compared to profiles (ISREC pfsearch), two implementations of hidden Markov models (HMMER hmmsearch and SAM hmmscore), and to the family pairwise search (FPS) method. The comparison is based on a novel evaluation tool, which was also developed during this work. All methods are presented in a new unified functional framework of database searching. The analysis is complemented by extensive simulations. The treesearch idea is also transferred to the probabilistic context of phylogeny, which results in a generalization of established probabilistic methods such as pairwise sequence alignment, multiple sequence alignment, and profile searches
mkESA: enhanced suffix array construction tool
No full textHomann R, Fleer D, Giegerich R, Rehmsmeier M. mkESA: enhanced suffix array construction tool. Bioinformatics. 2009;25(8):1084-1085.We introduce the tool mkESA, an open source program for constructing enhanced suffix arrays (ESAs), striving for low memory consumption, yet high practical speed. mkESA is a user-friendly program written in portable C99, based on a parallelized version of the Deep-Shallow suffix array construction algorithm, which is known for its high speed and small memory usage. The tool handles large FASTA files with multiple sequences, and computes suffix arrays and various additional tables, such as the LCP table (longest common prefix) or the inverse suffix array, from given sequence data
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