AptaMat: a matrix-based algorithm to compare single-stranded oligonucleotides secondary structures

Abstract Motivation Comparing single-stranded nucleic acids (ssNAs) secondary structures is fundamental when investigating their function and evolution and predicting the effect of mutations on their structures. Many comparison metrics exist, although they are either too elaborate or not sensitive e...

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Vydáno v:Bioinformatics (Oxford, England) Ročník 39; číslo 1
Hlavní autoři: Binet, Thomas, Avalle, Bérangère, Dávila Felipe, Miraine, Maffucci, Irene
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 01.01.2023
Oxford Publishing Limited (England)
Oxford University Press (OUP)
Témata:
Algorithms
Availability
Bioinformatics
Cluster Analysis
Clustering
Humans
Life Sciences
Nucleic Acids
Oligonucleotides
Original Paper
Protein Structure, Secondary
Quantitative Methods
Sequences
Software
ISSN:1367-4811, 1367-4803, 1367-4811
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Shrnutí:Abstract Motivation Comparing single-stranded nucleic acids (ssNAs) secondary structures is fundamental when investigating their function and evolution and predicting the effect of mutations on their structures. Many comparison metrics exist, although they are either too elaborate or not sensitive enough to distinguish close ssNAs structures. Results In this context, we developed AptaMat, a simple and sensitive algorithm for ssNAs secondary structures comparison based on matrices representing the ssNAs secondary structures and a metric built upon the Manhattan distance in the plane. We applied AptaMat to several examples and compared the results to those obtained by the most frequently used metrics, namely the Hamming distance and the RNAdistance, and by a recently developed image-based approach. We showed that AptaMat is able to discriminate between similar sequences, outperforming all the other here considered metrics. In addition, we showed that AptaMat was able to correctly classify 14 RFAM families within a clustering procedure. Availability and implementation The python code for AptaMat is available at https://github.com/GEC-git/AptaMat.git. Supplementary information Supplementary data are available at Bioinformatics online.
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ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btac752