Knotify+: Toward the Prediction of RNA H-Type Pseudoknots, Including Bulges and Internal Loops

Makris, Evangelos; Kolaitis, Angelos; Andrikos, Christos; Moulos, Vrettos; Tsanakas, Panayiotis; Pavlatos, Christos

doi:10.3390/biom13020308

Cited by 3 publications

(8 citation statements)

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“…In addition, Rtips (RNA sTructure prediction using IP Scheme) [106] is a web server in which IPknot is combined with RactIP [106] , the latter for predicting RNA-RNA interactions with kissing hairpins. Other methodologies, like Knotify+ [105] , address pseudoknots prediction by taking advantage of the combination between context-free grammar, maximum base pairing, and minimum free energy.…”

Section: Resultsmentioning

confidence: 99%

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

Ballarino,

Pepe,

Helmer-Citterich

et al. 2023

Computational and Structural Biotechnology Journal

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Section: Resultsmentioning

confidence: 99%

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

Ballarino,

Pepe,

Helmer-Citterich

et al. 2023

Computational and Structural Biotechnology Journal

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“…Knotify [ 20 ] predicts H-type pseudoknots and its variation, Knotify+ [ 19 ], predicts H-type with internal loops and bulges. Knotify for L-type [ 42 ], finally, introduces a grammar and a computational pipeline for predicting a rare pseudoknot type, the L-type.…”

Section: The Background Theorymentioning

confidence: 99%

“…The proposed computational framework, Knotify_V2.0, represents an advancement of the methodology introduced in [ 19 ], now expanded to include the prediction of hairpins in the loops. The fundamental model employed for pseudoknot prediction, specifically of the H-type, relies on the application of context-free grammar (CFG).…”

Section: The Background Theorymentioning

confidence: 99%

“…Knotify_V2.0, much like the approach presented in [ 19 , 20 ], integrates Yet Another Early Parser (YAEP) [ 62 ]. YAEP is a high-performance implementation of Earley’s [ 63 , 64 , 65 , 66 ] parser designed for handling ambiguous grammar, making it suitable for our RNA pseudoknot prediction grammar.…”

Section: The Background Theorymentioning

confidence: 99%

“…In the present study, we propose an updated version of Knotify+ [ 19 ], the latest version of Knotify [ 20 ] system, that extends its predictive capabilities to include except the detection of bulges and internal loops [ 21 ], a novel feature for predicting hairpins within the loops of the H-type pseudoknots. Initiating the process, a parser is employed to analyze the RNA sequence, generating a comprehensive list of potential core stems for a pseudoknot.…”

Section: Introductionmentioning

confidence: 99%

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Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops

Kolaitis,

Makris,

Karagiannis

et al. 2024

Genes

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Accurately predicting the pairing order of bases in RNA molecules is essential for anticipating RNA secondary structures. Consequently, this task holds significant importance in unveiling previously unknown biological processes. The urgent need to comprehend RNA structures has been accentuated by the unprecedented impact of the widespread COVID-19 pandemic. This paper presents a framework, Knotify_V2.0, which makes use of syntactic pattern recognition techniques in order to predict RNA structures, with a specific emphasis on tackling the demanding task of predicting H-type pseudoknots that encompass bulges and hairpins. By leveraging the expressive capabilities of a Context-Free Grammar (CFG), the suggested framework integrates the inherent benefits of CFG and makes use of minimum free energy and maximum base pairing criteria. This integration enables the effective management of this inherently ambiguous task. The main contribution of Knotify_V2.0 compared to earlier versions lies in its capacity to identify additional motifs like bulges and hairpins within the internal loops of the pseudoknot. Notably, the proposed methodology, Knotify_V2.0, demonstrates superior accuracy in predicting core stems compared to state-of-the-art frameworks. Knotify_V2.0 exhibited exceptional performance by accurately identifying both core base pairing that form the ground truth pseudoknot in 70% of the examined sequences. Furthermore, Knotify_V2.0 narrowed the performance gap with Knotty, which had demonstrated better performance than Knotify and even surpassed it in Recall and F1-score metrics. Knotify_V2.0 achieved a higher count of true positives (tp) and a significantly lower count of false negatives (fn) compared to Knotify, highlighting improvements in Prediction and Recall metrics, respectively. Consequently, Knotify_V2.0 achieved a higher F1-score than any other platform. The source code and comprehensive implementation details of Knotify_V2.0 are publicly available on GitHub.

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Syntactic Pattern Recognition for the Prediction of L-Type Pseudoknots in RNA

et al. 2023

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The observation and analysis of RNA molecules have proved crucial for the understanding of various processes in nature. Scientists have mined knowledge and drawn conclusions using experimental methods for decades. Leveraging advanced computational methods in recent years has led to fast and more accurate results in all areas of interest. One highly challenging task, in terms of RNA analysis, is the prediction of its structure, which provides valuable information about how it transforms and operates numerous significant tasks in organisms. In this paper, we focus on the prediction of the 2-D or secondary structure of RNA, specifically, on a rare but yet complex type of pseudoknot, the L-type pseudoknot, extending our previous framework specialized for H-type pseudoknots. We propose a grammar-based framework that predicts all possible L-type pseudoknots of a sequence in a reasonable response time, leveraging also the advantages of core biological principles, such as maximum base pairs and minimum free energy. In order to evaluate the effectiveness of our methodology, we assessed four performance metrics: precision; recall; Matthews correlation coefficient (MCC); and F1-score, which is the harmonic mean of precision and recall. Our methodology outperformed the other three well known methods in terms of Precision, with a score of 0.844, while other methodologies scored 0.500, 0.333, and 0.308. Regarding the F1-score, our platform scored 0.671, while other methodologies scored 0.661, 0.449, and 0.449. The proposed methodology surpassed all methods in terms of the MCC metric, achieving a score of 0.521. The proposed method was added to our RNA toolset, which aims to enhance the capabilities of biologists in the prediction of RNA motifs, including pseudoknots, and holds the potential to be applied in a multitude of biological domains, including gene therapy, drug design, and comprehending RNA functionality. Furthermore, the suggested approach can be employed in conjunction with other methodologies to enhance the precision of RNA structure prediction.

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Knotify+: Toward the Prediction of RNA H-Type Pseudoknots, Including Bulges and Internal Loops

Cited by 3 publications

References 53 publications

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops

Syntactic Pattern Recognition for the Prediction of L-Type Pseudoknots in RNA

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