A Graph Grammar for Modelling RNA Folding

Mamuye, Adane; Merelli, Emanuela; Tesei, Luca

doi:10.4204/eptcs.231.3

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Moreover, an improvement of the efficiency of the existing algorithms for the various classes of pseudoknots might be reached by exploiting suitable properties of our operators. The same problem can be faced using the algebraic representation together with learning algorithms and adaptability checking typical of complex systems [53–55].…”

Section: Discussionmentioning

confidence: 99%

An algebraic language for RNA pseudoknots comparison

2019

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Background RNA secondary structure comparison is a fundamental task for several studies, among which are RNA structure prediction and evolution. The comparison can currently be done efficiently only for pseudoknot-free structures due to their inherent tree representation. Results In this work, we introduce an algebraic language to represent RNA secondary structures with arbitrary pseudoknots. Each structure is associated with a unique algebraic RNA tree that is derived from a tree grammar having concatenation , nesting and crossing as operators. From an algebraic RNA tree, an abstraction is defined in which the primary structure is neglected. The resulting structural RNA tree allows us to define a new measure of similarity calculated exploiting classical tree alignment. Conclusions The tree grammar with its operators permit to uniquely represent any RNA secondary structure as a tree. Structural RNA trees allow us to perform comparison of RNA secondary structures with arbitrary pseudoknots without taking into account the primary structure.

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Section: Discussionmentioning

confidence: 99%

An algebraic language for RNA pseudoknots comparison

2019

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“…The modeling of protein structures with the help of neighborhood-controlled embedding (eNCE) graph grammars was presented in [197]. Algebraic (DPO) graph transformation systems were used for modeling RNA folding in [136]. These systems were also applied for analyzing metabolic networks in [179] and for modeling RNA tertiary structure motifs [185].…”

Section: Applications Of Graph-based Modelsmentioning

confidence: 99%

A Survey on Syntactic Pattern Recognition Methods in Bioinformatics

Flasiński

2024

csci

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Formal tools and models of syntactic pattern recognition which are used in bioinformatics are introduced and characterized in the paper. They include, among others: stochastic (string) grammars and automata, hidden Markov models, programmed grammars, attributed grammars, stochastic tree grammars, Tree Adjoining Grammars (TAGs), algebraic dynamic programming, NLC- and NCE-type graph grammars, and algebraic graph transformation systems. The survey of applications of these formal tools and models in bioinformatics is presented.

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“… Loop decomposition of a nested RNA structure into hairpin loops (no enclosed base pairs), stackings (adjacent enclosed base pairs), bulges (only one side adjacent to enclosed base pair), multibranched loops (more than one directly enclosed base pair), interior loops (no stacked enclosed base pairs), pseudoknots (nucleotides in a loop pair with a region outside the helices that close the loop) and stem-loops (combination of the stem, double helix, and a loop) (adapted from [ 256 ]). …”

Section: State-of-the-art Approaches For Rna Structure and Interactom...mentioning

confidence: 99%

A Hitchhiker's guide to RNA–RNA structure and interaction prediction tools

Tieng,

Abdullah-Zawawi,

Md Shahri

et al. 2023

Briefings in Bioinformatics

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RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA–RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA–RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA–RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

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A Graph Grammar for Modelling RNA Folding

Cited by 7 publications

References 17 publications

An algebraic language for RNA pseudoknots comparison

An algebraic language for RNA pseudoknots comparison

A Survey on Syntactic Pattern Recognition Methods in Bioinformatics

A Hitchhiker's guide to RNA–RNA structure and interaction prediction tools

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