Evidence map›Paper›PMID 36241988›Full record

ArticleBMC bioinformatics2022

IndelsRNAmute: predicting deleterious multiple point substitutions and indels mutations.

Alexander Churkin, Yann Ponty, Danny Barash

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors at 3 institutions in 2 countries.

Alexander ChurkinDepartment of Software Engineering, Sami Shamoon College of Engineering, Beersheba, Israel. alexach3@sce.ac.il.ORCID http://orcid.org/0000-0003-4275-257X
Yann PontyLaboratoire d'Informatique de l'École Polytechique (LIX CNRS UMR 7161), Ecole Polytechnique, Palaiseau, France.
Danny BarashDepartment of Computer Science, Ben-Gurion University, Beersheba, Israel.
Ben-Gurion University of the Negev · ILCentre National de la Recherche Scientifique · FRSami Shamoon College of Engineering · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRNA deleterious point mutation prediction was previously addressed with programs such as RNAmute and MultiRNAmute. The purpose of these programs is to predict a global conformational rearrangement of the secondary structure of a functional RNA molecule, thereby disrupting its function. RNAmute was designed to deal with only single point mutations in a brute force manner, while in MultiRNAmute an efficient approach to deal with multiple point mutations was developed. The approach used in MultiRNAmute is based on the stabilization of the suboptimal RNA folding prediction solutions and/or destabilization of the optimal folding prediction solution of the wild type RNA molecule. The MultiRNAmute algorithm is significantly more efficient than the brute force approach in RNAmute, but in the case of long sequences and large m-point mutation sets the MultiRNAmute becomes exponential in examining all possible stabilizing and destabilizing mutations.

resultsAn inherent limitation in the RNAmute and MultiRNAmute programs is their ability to predict only substitution mutations, as these programs were not designed to work with deletion or insertion mutations. To address this limitation we herein develop a very fast algorithm, based on suboptimal folding solutions, to predict a predefined number of multiple point deleterious mutations as specified by the user. Depending on the user's choice, each such set of mutations may contain combinations of deletions, insertions and substitution mutations. Additionally, we prove the hardness of predicting the most deleterious set of point mutations in structural RNAs.

conclusionsWe developed a method that extends our previous MultiRNAmute method to predict insertion and deletion mutations in addition to substitutions. The additional advantage of the new method is its efficiency to find a predefined number of deleterious mutations. Our new method may be exploited by biologists and virologists prior to site-directed mutagenesis experiments, which involve indel mutations along with substitutions. For example, our method may help to investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary structure.

Indexed as

INDEL MutationRNAMutationPoint MutationSequence Analysis, RNARNARNA indels predictionRNA mutations predictionSuboptimal RNA structure

Identifiers

PMID36241988
PMCPMC9569039
OpenAlexW3150992426

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.