Comparison of <i>P-RnaPredict</i> and <i>mfold</i>—algorithms for RNA secondary structure prediction

Wiese, Kay C.; Hendriks, A.

doi:10.1093/bioinformatics/btl043

Cited by 52 publications

(29 citation statements)

References 30 publications

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“…Even though such experimental structural information currently exists for only a small number of cases, such information may soon be available. Alternatively, using existing thermodynamic-based secondary structure prediction programs (14, 27) (with ϳ50%-90% prediction accuracy [28]) for predicting a small set of thermodynamically stable folds and restricting the algorithm to those, allows both reduction of the search space and integration of thermodynamic considerations into our model, which are not fully embedded into standard covariance model applications. Certainly, other structure prediction tools, such as those based on probabilistic models (29), can also be used to derive a set of highly probable folds as an input to our method.…”

Section: Resultsmentioning

confidence: 99%

Computational prediction of RNA structural motifs involved in posttranscriptional regulatory processes

Rabani

Kertesz

Segal

2008

Proc. Natl. Acad. Sci. U.S.A.

109

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Messenger RNA molecules are tightly regulated, mostly through interactions with proteins and other RNAs, but the mechanisms that confer the specificity of such interactions are poorly understood. It is clear, however, that this specificity is determined by both the nucleotide sequence and secondary structure of the mRNA. Here, we develop RNApromo, an efficient computational tool for identifying structural elements within mRNAs that are involved in specifying posttranscriptional regulations. By analyzing experimental data on mRNA decay rates, we identify common structural elements in fastdecaying and slow-decaying mRNAs and link them with binding preferences of several RNA binding proteins. We also predict structural elements in sets of mRNAs with common subcellular localization in mouse neurons and fly embryos. Finally, by analyzing premicroRNA stem-loops, we identify structural differences between pre-microRNAs of animals and plants, which provide insights into the mechanism of microRNA biogenesis. Together, our results reveal unexplored layers of posttranscriptional regulations in groups of RNAs and are therefore an important step toward a better understanding of the regulatory information conveyed within RNA molecules. Our new RNA motif discovery tool is available online.bioinformatics ͉ motif prediction ͉ posttranscriptional regulation ͉ RNA secondary structure ͉ SCFGs R NA molecules undergo diverse posttranscriptional regulation of gene expression, including regulation of RNA transport and localization, mRNA translation, and RNA decay (1-3). In many cases, such posttranscriptional regulation occurs through elements on the mRNA molecule that interact with the hundreds of RNA binding proteins (RBPs) that exist in the cell (4). A well-known example is the iron-responsive element (IRE), a secondary structure RNA motif located on UTRs of members of the iron metabolism and transport pathway (5). The binding of the RBPs Irp1 and Irp2 to IRE elements affects the translation rate of the mRNA, and by that coordinates the response to changing levels of iron in the environment. Other examples include a 118-nucleotide stem-loop structure through which mRNAs are transported to the yeast bud tip by the RBP She2 (6) and the RBP Sbp2 that is involved in mediating UGA redefinition from a stop codon to selenocysteine by binding specific stem-loop structures, termed selenocystein insertion site (SECIS) elements, in the 3Ј UTR of selenoproteins (7). In other cases, elements on the mRNA molecule interact with other RNAs that direct the regulatory effect. For example, the recognition and binding affinity of a microRNA to its mRNA target is determined by both the sequence and structure of the target mRNA (8-11).The examples above suggest that the posttranscriptional regulation of mRNAs is determined not only by its linear nucleotide sequence but also by its secondary structure. Thus, a key goal is to understand the involvement of mRNA secondary structures in such regulation. One approach is to identify recurring patterns, termed m...

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Section: Resultsmentioning

confidence: 99%

Computational prediction of RNA structural motifs involved in posttranscriptional regulatory processes

Rabani

Kertesz

Segal

2008

Proc. Natl. Acad. Sci. U.S.A.

109

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“…Part of this effort is focused on providing a means whereby the shape of these biomolecules can be predicted through the use of computer algorithms. Dynamic Programming [14] and Genetic Algorithms [11,13], take a sequence of bases and attempt to find a secondary structure using free energy minimization as a guide. Comparative methods, on the other hand, use known sequence and folding patterns to generate a predicted secondary structure [6].…”

Section: Introductionmentioning

confidence: 99%

jViz.Rna - An Interactive Graphical Tool for Visualizing RNA Secondary Structure Including Pseudoknots

Wiese

Glen

2006

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)

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In order for structure prediction researchers to better understand the results of their algorithms and to enable life science researchers to interpret RNA structure easily, it is helpful to provide them with a flexible and powerful tool for RNA secondary structure visualization. jViz.Rna is a multi-platform visualization tool capable of displaying RNA secondary structures encoded in a variety of file formats. A single structure can be shown using the linear Feynman, circular Feynman, dot plot, and classical structure visualization models. The resulting drawings are dynamic and can easily be further modified by the user. Any of the drawings produced can be saved to disk enabling easy dissemination. The unique usage of a spring model for classical structure drawing allows for clear visualization of pseudoknots with minimal overlaps. The addition of a locality tool allows for the isolation of pseudoknotted regions or other regions of interest. Availability: http://jviz.research.iat.sfu.ca

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“…Evolutionary computation was previously used for RNA structure folding [2], [8], [18], [19], [38], [39], [40], [44] and the prediction of common structures [3] using free energy calculations. Furthermore, evolutionary algorithms were applied to the identification of conserved regulatory RNA structures in prokaryotic metabolic pathway genes [29].…”

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confidence: 99%

Finding a Common Motif of RNA Sequences Using Genetic Programming: The GeRNAMo System

Michal¹,

Ivry

Schalit-Cohen

et al. 2007

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract-We focus on finding a consensus motif of a set of homologous or functionally related RNA molecules. Recent approaches to this problem have been limited to simple motifs, require sequence alignment, and make prior assumptions concerning the data set. We use genetic programming to predict RNA consensus motifs based solely on the data set. Our system-dubbed GeRNAMo (Genetic programming of RNA Motifs)-predicts the most common motifs without sequence alignment and is capable of dealing with any motif size. Our program only requires the maximum number of stems in the motif and, if prior knowledge is available, the user can specify other attributes of the motif (e.g., the range of the motif's minimum and maximum sizes), thereby increasing both sensitivity and speed. We describe several experiments using either ferritin iron response element (IRE), signal recognition particle (SRP), or microRNA sequences showing that the most common motif is found repeatedly and that our system offers substantial advantages over previous methods.

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Comparison of P-RnaPredict and mfold—algorithms for RNA secondary structure prediction

Abstract: P-RnaPredict is available for non-commercial usage. Interested parties should contact Kay C. Wiese (wiese@cs.sfu.ca).

Cited by 52 publications

References 30 publications

Computational prediction of RNA structural motifs involved in posttranscriptional regulatory processes

Computational prediction of RNA structural motifs involved in posttranscriptional regulatory processes

jViz.Rna - An Interactive Graphical Tool for Visualizing RNA Secondary Structure Including Pseudoknots

Finding a Common Motif of RNA Sequences Using Genetic Programming: The GeRNAMo System

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