Dataset Information

RPredictorDB: a predictive database of individual secondary structures of RNAs and their formatted plots.

ABSTRACT: Secondary data structure of RNA molecules provides insights into the identity and function of RNAs. With RNAs readily sequenced, the question of their structural characterization is increasingly important. However, RNA structure is difficult to acquire. Its experimental identification is extremely technically demanding, while computational prediction is not accurate enough, especially for large structures of long sequences. We address this difficult situation with rPredictorDB, a predictive database of RNA secondary structures that aims to form a middle ground between experimentally identified structures in PDB and predicted consensus secondary structures in Rfam. The database contains individual secondary structures predicted using a tool for template-based prediction of RNA secondary structure for the homologs of the RNA families with at least one homolog with experimentally solved structure. Experimentally identified structures are used as the structural templates and thus the prediction has higher reliability than de novo predictions in Rfam. The sequences are downloaded from public resources. So far rPredictorDB covers 7365 RNAs with their secondary structures. Plots of the secondary structures use the Traveler package for readable display of RNAs with long sequences and complex structures, such as ribosomal RNAs. The RNAs in the output of rPredictorDB are extensively annotated and can be viewed, browsed, searched and downloaded according to taxonomic, sequence and structure data. Additionally, structure of user-provided sequences can be predicted using the templates stored in rPredictorDB.

SUBMITTER: Jelinek J

PROVIDER: S-EPMC6482342 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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rPredictorDB: a predictive database of individual secondary structures of RNAs and their formatted plots.

Jelínek Jan J Hoksza David D Hajič Jan J Pešek Jan J Drozen Jan J Hladík Tomáš T Klimpera Michal M Vohradský Jiří J Pánek Josef J

Database : the journal of biological databases and curation 20190101

Secondary data structure of RNA molecules provides insights into the identity and function of RNAs. With RNAs readily sequenced, the question of their structural characterization is increasingly important. However, RNA structure is difficult to acquire. Its experimental identification is extremely technically demanding, while computational prediction is not accurate enough, especially for large structures of long sequences. We address this difficult situation with rPredictorDB, a predictive data ...[more]

PMID: 31032840

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Project description:BackgroundComputing the distance between two RNA secondary structures can contribute in understanding the functional relationship between them. When used repeatedly, such a procedure may lead to finding a query RNA structure of interest in a database of structures. Several methods are available for computing distances between RNAs represented as strings or graphs, but none utilize the RNA representation with dot plots. Since dot plots are essentially digital images, there is a clear motivation to devise an algorithm for computing the distance between dot plots based on image processing methods.ResultsWe have developed a new metric dubbed 'DoPloCompare', which compares two RNA structures. The method is based on comparing dot plot diagrams that represent the secondary structures. When analyzing two diagrams and motivated by image processing, the distance is based on a combination of histogram correlations and a geometrical distance measure. We introduce, describe, and illustrate the procedure by two applications that utilize this metric on RNA sequences. The first application is the RNA design problem, where the goal is to find the nucleotide sequence for a given secondary structure. Examples where our proposed distance measure outperforms others are given. The second application locates peculiar point mutations that induce significant structural alternations relative to the wild type predicted secondary structure. The approach reported in the past to solve this problem was tested on several RNA sequences with known secondary structures to affirm their prediction, as well as on a data set of ribosomal pieces. These pieces were computationally cut from a ribosome for which an experimentally derived secondary structure is available, and on each piece the prediction conveys similarity to the experimental result. Our newly proposed distance measure shows benefit in this problem as well when compared to standard methods used for assessing the distance similarity between two RNA secondary structures.ConclusionInspired by image processing and the dot plot representation for RNA secondary structure, we have managed to provide a conceptually new and potentially beneficial metric for comparing two RNA secondary structures. We illustrated our approach on the RNA design problem, as well as on an application that utilizes the distance measure to detect conformational rearranging point mutations in an RNA sequence.

Dataset Information

RPredictorDB: a predictive database of individual secondary structures of RNAs and their formatted plots.

Publications

rPredictorDB: a predictive database of individual secondary structures of RNAs and their formatted plots.

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