scholarly journals A method for RNA structure prediction shows evidence for structure in lncRNAs

2018 ◽  
Author(s):  
Riccardo Delli ponti ◽  
Alexandros Armaos ◽  
Stefanie Marti ◽  
Gian Gaetano Tartaglia
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Sequence Information ◽  
Time Warping ◽  
Single Nucleotide ◽  
Rna Molecules ◽  
Rna Structure Prediction ◽  
Dynamic Time ◽  
Nucleotide Resolution

AbstractTo compare the secondary structures of RNA molecules we developed the CROSSalign method. CROSSalign is based on the combination of the Computational Recognition Of Secondary Structure (CROSS) algorithm to predict the RNA secondary structure at single-nucleotide resolution using sequence information, and the Dynamic Time Warping (DTW) method to align profiles of different lengths. We applied CROSSalign to investigate the structural conservation of long non-coding RNAs such as XIST and HOTAIR as well as ssRNA viruses including HIV. In a pool of sequences with the same secondary structure CROSSalign accurately recognizes repeat A of XIST and domain D2 of HOTAIR and outperforms other methods based on covariance modelling. CROSSalign can be applied to perform pair-wise comparisons and is able to find homologues between thousands of matches identifying the exact regions of similarity between profiles of different lengths. The algorithm is freely available at the webpage http://service.tartaglialab.com//new_submission/CROSSalign.

Predicting RNA Secondary Structure Using a Improved BPSO Based on Stem Combination

2013 ◽  
Vol 325-326 ◽  
pp. 1551-1554
Author(s):  
Yi Qi
Keyword(s):  
Secondary Structure ◽  
Sensitivity And Specificity ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Experimental Results ◽  
New Method ◽  
Rna Structure Prediction ◽  
New Strategies

In this paper, we present an improved BPSO to predict RNA secondary structure to improve the performance with two new strategies. First one is to reduce the searching space of PSO through super stem set construction. Second is to modify the general BPSO updating process to settle stem permutation and combination problems. The experimental results show that the new method is effective for RNA structure prediction in terms of sensitivity and specificity by different sequence datasets including simple pseudoknot.

scholarly journals RNA structure prediction: from 2D to 3D

2017 ◽  
Vol 1 (3) ◽  
pp. 275-285 ◽  
Author(s):  
Bernhard C. Thiel ◽  
Christoph Flamm ◽  
Ivo L. Hofacker
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
3D Structure ◽  
Rna Structure Prediction ◽  
2D Structure ◽  
3D Structure Prediction ◽  
Different Levels ◽  
2D To 3D

We summarize different levels of RNA structure prediction, from classical 2D structure to extended secondary structure and motif-based research toward 3D structure prediction of RNA. We outline the importance of classical secondary structure during all those levels of structure prediction.

scholarly journals RAG-Web: RNA structure prediction/design using RNA-As-Graphs

2019 ◽  
Author(s):  
Grace Meng ◽  
Marva Tariq ◽  
Swati Jain ◽  
Shereef Elmetwaly ◽  
Tamar Schlick
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Three Dimensional ◽  
Coarse Grained ◽  
Supplementary Information ◽  
Tree Graph ◽  
Rna Structure Prediction ◽  
Tree Graphs

Abstract Summary We launch a webserver for RNA structure prediction and design corresponding to tools developed using our RNA-As-Graphs (RAG) approach. RAG uses coarse-grained tree graphs to represent RNA secondary structure, allowing the application of graph theory to analyze and advance RNA structure discovery. Our webserver consists of three modules: (a) RAG Sampler: samples tree graph topologies from an RNA secondary structure to predict corresponding tertiary topologies, (b) RAG Builder: builds three-dimensional atomic models from candidate graphs generated by RAG Sampler, and (c) RAG Designer: designs sequences that fold onto novel RNA motifs (described by tree graph topologies). Results analyses are performed for further assessment/selection. The Results page provides links to download results and indicates possible errors encountered. RAG-Web offers a user-friendly interface to utilize our RAG software suite to predict and design RNA structures and sequences. Availability and implementation The webserver is freely available online at: http://www.biomath.nyu.edu/ragtop/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RNAProbe: a web server for normalization and analysis of RNA structure probing data

2020 ◽  
Vol 48 (W1) ◽  
pp. W292-W299 ◽  
Author(s):  
Tomasz K Wirecki ◽  
Katarzyna Merdas ◽  
Agata Bernat ◽  
Michał J Boniecki ◽  
Janusz M Bujnicki ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Structural Characteristics ◽  
Web Server ◽  
Rna Molecules ◽  
Chemical Probing ◽  
Structure Probing ◽  
Low Pass

Abstract RNA molecules play key roles in all living cells. Knowledge of the structural characteristics of RNA molecules allows for a better understanding of the mechanisms of their action. RNA chemical probing allows us to study the susceptibility of nucleotides to chemical modification, and the information obtained can be used to guide secondary structure prediction. These experimental results can be analyzed using various computational tools, which, however, requires additional, tedious steps (e.g., further normalization of the reactivities and visualization of the results), for which there are no fully automated methods. Here, we introduce RNAProbe, a web server that facilitates normalization, analysis, and visualization of the low-pass SHAPE, DMS and CMCT probing results with the modification sites detected by capillary electrophoresis. RNAProbe automatically analyzes chemical probing output data and turns tedious manual work into a one-minute assignment. RNAProbe performs normalization based on a well-established protocol, utilizes recognized secondary structure prediction methods, and generates high-quality images with structure representations and reactivity heatmaps. It summarizes the results in the form of a spreadsheet, which can be used for comparative analyses between experiments. Results of predictions with normalized reactivities are also collected in text files, providing interoperability with bioinformatics workflows. RNAProbe is available at https://rnaprobe.genesilico.pl.

scholarly journals RNA structure prediction using positive and negative evolutionary information

2020 ◽  
Author(s):  
Elena Rivas
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Evolutionary Information ◽  
Sequence Alignments ◽  
Consensus Secondary Structure ◽  
Rna Sequence ◽  
Rna Structure Prediction ◽  
Nested Structure ◽  
Covariation Analysis

AbstractKnowing the structure of conserved structural RNAs is important to elucidate their function and mechanism of action. However, predicting a conserved RNA structure remains unreliable, even when using a combination of thermodynamic stability and evolutionary covariation information. Here we present a method to predict a conserved RNA structure that combines the following three features. First, it uses significant covariation due to RNA structure and removes spurious covariation due to phylogeny. Second, it uses negative evolutionary information: basepairs that have variation but no significant covariation are prevented from occurring. Lastly, it uses a battery of probabilistic folding algorithms that incorporate all positive covariation into one structure. The method, named CaCoFold (Cascade variation/covariation Constrained Folding algorithm), predicts a nested structure guided by a maximal subset of positive basepairs, and recursively incorporates all remaining positive basepairs into alternative helices. The alternative helices can be compatible with the nested structure such as pseudoknots, or overlapping such as competing structures, base triplets, or other 3D non-antiparallel interactions. We present evidence that CaCoFold predictions are consistent with structures modeled from crystallography.Author SummaryThe availability of deeper comparative sequence alignments and recent advances in statistical analysis of RNA sequence covariation have made it possible to identify a reliable set of conserved base pairs, as well as a reliable set of non-basepairs (positions that vary without covarying). Predicting an overall consensus secondary structure consistent with a set of individual inferred pairs and non-pairs remains a problem. Current RNA structure prediction algorithms that predict nested secondary structures cannot use the full set of inferred covarying pairs, because covariation analysis also identifies important non-nested pairing interactions such as pseudoknots, base triples, and alternative structures. Moreover, although algorithms for incorporating negative constraints exist, negative information from covariation analysis (inferred non-pairs) has not been systematically exploited.Here I introduce an efficient approximate RNA structure prediction algorithm that incorporates all inferred pairs and excludes all non-pairs. Using this, and an improved visualization tool, I show that the method correctly identifies many non-nested structures in agreement with known crystal structures, and improves many curated consensus secondary structure annotations in RNA sequence alignment databases.

New Algorithm for Predicting RNA Secondary Structure with Pseudoknots

2012 ◽  
Vol 532-533 ◽  
pp. 1796-1799 ◽  
Author(s):  
Zhen Dong Liu ◽  
Da Ming Zhu
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Experimental Results ◽  
Rna Secondary Structure Prediction ◽  
Rna Molecules ◽  
Specificity And Sensitivity

Pseudoknots are complicated and stable RNA structure. Based on the idea of iteratively forming stable stems, and the character that the stems in RNA molecules are relatively stable, an algorithm is presented to predict RNA secondary structure including pseudoknots, it is an improvement from the previously used algorithm ,the algorithm takes O(n3) time and O(n2) sapce , in predicting accuracy, it outperforms other known algorithm of RNA secondary structure prediction, its performance is tested with the RNA sub-sequences in PseudoBase. The experimental results indicate that the algorithm has good specificity and sensitivity.

scholarly journals An algorithm for template-based prediction of secondary structures of individual RNA sequences

2017 ◽  
Author(s):  
Josef Pánek ◽  
Martin Černý
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
De Novo ◽  
Secondary Structures ◽  
Rna Structures ◽  
Rna Sequences ◽  
Biologically Relevant ◽  
Rna Molecules ◽  
Rna Structure Prediction ◽  
Limited Applicability

ABSTRACTWhile understanding the structure of RNA molecules is vital for deciphering their functions, determining RNA structures experimentally is exceptionally hard. At the same time, extant approaches to computational RNA structure prediction have limited applicability and reliability. In this paper we provide a method to solve a simpler yet still biologically relevant problem: prediction of secondary RNA structure using structure of different molecules as a template.Our method identifies conserved and unconserved subsequences within an RNA molecule. For conserved subsequences, the template structure is directly transferred into the generated structure and combined with de-novo predicted structure for the unconserved subsequences with low evolutionary conservation. The method also determines, when the generated structure is unreliable.The method is validated using experimentally identified structures. The accuracy of the method exceeds that of classical prediction algorithms and constrained prediction methods. This is demonstrated by comparison using large number of heterogeneous RNAs. The presented method is fast and robust, and useful for various applications requiring knowledge of secondary structures of individual RNA sequences.

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