A novel approach to represent and compare RNA secondary structures

Mattei, Eugenio; Ausiello, Gabriele; Ferré, Fabrizio; Helmer-Citterich, Manuela

doi:10.1093/nar/gku283

Cited by 48 publications

(61 citation statements)

References 48 publications

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“…Matching relationships for piRNA-mRNA chimeras detected at least 5 times were summarized as follows Alignments from predicted duplexes were converted into matches, mismatches, and internal bulges using BEAR encoder (Mattei et al, 2014).…”

Section: Bioinformatic Analysis Of P-clash Datamentioning

confidence: 99%

Identification of piRNA binding sites reveals the Argonaute regulatory landscape of theC. elegansgermline

Shen

Hao

Öztürk³

et al. 2018

Preprint

119

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piRNAs (Piwi-interacting small RNAs) engage Piwi Argonautes to silence transposons and promote fertility in animal germlines. Genetic and computational studies have suggested that C. elegans piRNAs tolerate mismatched pairing and in principle could target every transcript. Here we employ in vivo cross-linking to identify transcriptomewide interactions between piRNAs and target RNAs. We show that piRNAs engage all germline mRNAs and that piRNA binding follows microRNA-like pairing rules.Targeting correlates better with binding energy than with piRNA abundance, suggesting that piRNA concentration does not limit targeting. In mRNAs silenced by piRNAs, secondary small RNAs accumulate at the center and ends of piRNA binding sites. In germline-expressed mRNAs, however, targeting by the CSR-1 Argonaute correlates with reduced piRNA binding density and suppression of piRNA-associated secondary small RNAs. Our findings reveal physiologically important and nuanced regulation of individual piRNA targets and provide evidence for a comprehensive posttranscriptional regulatory step in germline gene expression.3

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Section: Bioinformatic Analysis Of P-clash Datamentioning

confidence: 99%

Identification of piRNA binding sites reveals the Argonaute regulatory landscape of theC. elegansgermline

Shen

Hao

Öztürk³

et al. 2018

Preprint

119

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“…We assigned RNA secondary structure codes to rNT using DSSR and BEAR encoding following the protocol used in the RPI‐Bind method . A dot‐bracket notation sequence for entire RNA sequence was obtained using DSSR.…”

Section: Methodsmentioning

confidence: 99%

Partner‐specific prediction of RNA‐binding residues in proteins: A critical assessment

et al. 2018

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RNA‐protein interactions play essential roles in regulating gene expression. While some RNA‐protein interactions are “specific”, that is, the RNA‐binding proteins preferentially bind to particular RNA sequence or structural motifs, others are “non‐RNA specific.” Deciphering the protein‐RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein‐RNA interfaces, there is a need for computational methods to identify RNA‐binding residues in proteins. While most of the existing computational methods for predicting RNA‐binding residues in RNA‐binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner‐specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner‐specific protein‐RNA interface prediction tools, PS‐PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA‐specificity metric (RSM), for quantifying the RNA‐specificity of the RNA binding residues predicted by such tools. Our results show that the RNA‐binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner‐agnostic metrics, RNA partner‐specific methods are outperformed by the state‐of‐the‐art partner‐agnostic methods. We conjecture that either (a) the protein‐RNA complexes in PDB are not representative of the protein‐RNA interactions in nature, or (b) the current methods for partner‐specific prediction of RNA‐binding residues in proteins fail to account for the differences in RNA partner‐specific versus partner‐agnostic protein‐RNA interactions, or both.

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“…ERA is an efficient and accurate RNA secondary structure alignment tool using the sparse dynamic programming technique. In [32], a new context-aware encoding representation for RNA secondary structure named as BEAR is designed and an RNA structural alignment based on BEAR encoding is proposed by integrating a constructed substitution matrix of RNA structural elements with Needleman-Wunsch algorithm. The web server Web-Beagle [29] of RNA structural alignment based on BEAR encoding is developed.…”

Section: Introductionmentioning

confidence: 99%

RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation

Shi

Xie

et al. 2017

BMC Bioinformatics

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BackgroundRNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms.ResultsAn alignment-free RNA comparison algorithm was proposed, in which novel numerical representations RNA-TVcurve (triple vector curve representation) of RNA sequence and corresponding secondary structure features are provided. Then a multi-scale similarity score of two given RNAs was designed based on wavelet decomposition of their numerical representation. In support of RNA mutation and phylogenetic analysis, a web server (RNA-TVcurve) was designed based on this alignment-free RNA comparison algorithm. It provides three functional modules: 1) visualization of numerical representation of RNA secondary structure; 2) detection of single-point mutation based on secondary structure; and 3) comparison of pairwise and multiple RNA secondary structures. The inputs of the web server require RNA primary sequences, while corresponding secondary structures are optional. For the primary sequences alone, the web server can compute the secondary structures using free energy minimization algorithm in terms of RNAfold tool from Vienna RNA package.ConclusionRNA-TVcurve is the first integrated web server, based on an alignment-free method, to deliver a suite of RNA analysis functions, including visualization, mutation analysis and multiple RNAs structure comparison. The comparison results with two popular RNA comparison tools, RNApdist and RNAdistance, showcased that RNA-TVcurve can efficiently capture subtle relationships among RNAs for mutation detection and non-coding RNA classification. All the relevant results were shown in an intuitive graphical manner, and can be freely downloaded from this server. RNA-TVcurve, along with test examples and detailed documents, are available at: http://ml.jlu.edu.cn/tvcurve/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1481-7) contains supplementary material, which is available to authorized users.

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A novel approach to represent and compare RNA secondary structures

Cited by 48 publications

References 48 publications

Identification of piRNA binding sites reveals the Argonaute regulatory landscape of theC. elegansgermline

Identification of piRNA binding sites reveals the Argonaute regulatory landscape of theC. elegansgermline

Partner‐specific prediction of RNA‐binding residues in proteins: A critical assessment

RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation

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