Knotify: An Efficient Parallel Platform for RNA Pseudoknot Prediction Using Syntactic Pattern Recognition

Abstract

Obtaining valuable clues for noncoding RNA (ribonucleic acid) subsequences remains a significant challenge, acknowledging that most of the human genome transcribes into noncoding RNA parts related to unknown biological operations. Capturing these clues relies on accurate “base pairing” prediction, also known as “RNA secondary structure prediction”. As COVID-19 is considered a severe global threat, the single-stranded SARS-CoV-2 virus reveals the importance of establishing an efficient RNA analysis toolkit. This work aimed to contribute to that by introducing a novel system committed to predicting RNA secondary structure patterns (i.e., RNA’s pseudoknots) that leverage syntactic pattern-recognition strategies. Having focused on the pseudoknot predictions, we formalized the secondary structure prediction of the RNA to be primarily a parsing and, secondly, an optimization problem. The proposed methodology addresses the problem of predicting pseudoknots of the first order (H-type). We introduce a context-free grammar (CFG) that affords enough expression power to recognize potential pseudoknot pattern. In addition, an alternative methodology of detecting possible pseudoknots is also implemented as well, using a brute-force algorithm. Any input sequence may highlight multiple potential folding patterns requiring a strict methodology to determine the single biologically realistic one. We conscripted a novel heuristic over the widely accepted notion of free-energy minimization to tackle such ambiguity in a performant way by utilizing each pattern’s context to unveil the most prominent pseudoknot pattern. The overall process features polynomial-time complexity, while its parallel implementation enhances the end performance, as proportional to the deployed hardware. The proposed methodology does succeed in predicting the core stems of any RNA pseudoknot of the test dataset by performing a 76.4% recall ratio. The methodology achieved a F1-score equal to 0.774 and MCC equal 0.543 in discovering all the stems of an RNA sequence, outperforming the particular task. Measurements were taken using a dataset of 262 RNA sequences establishing a performance speed of 1.31, 3.45, and 7.76 compared to three well-known platforms. The implementation source code is publicly available under knotify github repo.

Navigating the Multiverse: A Hitchhiker’s Guide to Selecting Harmonisation Methods for Multimodal Biomedical Data

“…Tyagi et al used grammar-based representations to model the syntactic and semantic rule of RNA folding and used context-free grammars (CFG) to generate sequences and parse their structures (Tyagi et al (2008)). Andikos et al created Knotify (Andrikos et al (2022)), a tool to predict RNA pseudoknots using CFG. Onokpasa et al asserts that CFG representations improve compression ratios of RNA sequences and structures (Onokpasa et al (2023)).…”

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

“…It is modular and can be easily modified to include different criteria and structures depending on the needs, allowing a fast experimentation process. [20], leveraging an efficient Context-Free Grammar (CFG) parser. This involves first choosing primitive patterns, where nitrogenous bases G, U, C, and A are represented by the RNA sequences as the characters "G", "U", "C", and "A", respectively.…”

“…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.…”

“…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.…”

Computational tools to study RNA-protein complexes

“…Current RNA folding algorithms can predict RNA structures of short to very long RNA sequences. Structure-based prediction algorithms (Das and Baker, 2007;Das et al, 2008;Parisien and Major, 2008;Das et al, 2010;Cao and Chen, 2011;Zhao et al, 2012;Magnus et al, 2019;Andrikos et al, 2022) achieve the highest accuracy with efficient alignments. A part of a well-aligned structure can be extracted and used as a fragment and many such fragments can be assembled on a template to form a 3D structure.…”