The super-n-motifs model: a novel alignment-free approach for representing and comparing RNA secondary structures

Glouzon, Jean-Pierre Séhi; Perreault, Jean‐Pierre; Wang, Shengrui

doi:10.1093/bioinformatics/btw773

Cited by 9 publications

(7 citation statements)

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“…Nevertheless, alignment-free algorithms are rapidly extending the range of their applications [ 151 – 154 ] and answering previously intractable questions in phylogenomics and horizontal gene transfer (reviewed in [ 57 ]), population genetics (reviewed in [ 111 ]), evolution of regulatory sequences, and links between the genome and epigenome (reviewed in [ 155 ]). Disadvantages of next-generation sequencing data processing and analysis seem to be particularly well addressed by the alignment-free methods (reviewed in [ 156 ]).…”

Section: Discussionmentioning

confidence: 99%

Alignment-free sequence comparison: benefits, applications, and tools

et al. 2017

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Alignment-free sequence analyses have been applied to problems ranging from whole-genome phylogeny to the classification of protein families, identification of horizontally transferred genes, and detection of recombined sequences. The strength of these methods makes them particularly useful for next-generation sequencing data processing and analysis. However, many researchers are unclear about how these methods work, how they compare to alignment-based methods, and what their potential is for use for their research. We address these questions and provide a guide to the currently available alignment-free sequence analysis tools.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1319-7) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Alignment-free sequence comparison: benefits, applications, and tools

et al. 2017

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“…aliFreeFold represents each sub-optimal structure of a RNA sequence using the n-motif representation ( Glouzon et al , 2017 ). The latter representation captures structural features of the sub-optimal structures by representing each structure by a vector of occurring n-motifs counts.…”

Section: Methodsmentioning

confidence: 99%

“…For example, S_3[H_5B_2] denotes a stem of 3 base pairs having a hairpin and bulge of, respectively, 5 and 2 single-stranded nucleotides as adjacent motifs. A detailed description of n-motifs is provided in ( Glouzon et al , 2017 ).…”

Section: Methodsmentioning

confidence: 99%

“…In this respect, aliFreeFold computes a weighted n-motifs representation such that each sub-optimal structure is represented by a vector of n-motifs occurring in the structure. A n-motif is a set of adjacent elementary motifs such as hairpin, stem, bulge, internal or multiple loops ( Glouzon et al , 2017 ). Each n-motif is weighted according to a conservation index measuring how well the n-motif is conserved across the set of sub-optimal structures.…”

Section: Introductionmentioning

confidence: 99%

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aliFreeFold: an alignment-free approach to predict secondary structure from homologous RNA sequences

Glouzon

Ouangraoua

2018

Bioinformatics

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MotivationPredicting the conserved secondary structure of homologous ribonucleic acid (RNA) sequences is crucial for understanding RNA functions. However, fast and accurate RNA structure prediction is challenging, especially when the number and the divergence of homologous RNA increases. To address this challenge, we propose aliFreeFold, based on a novel alignment-free approach which computes a representative structure from a set of homologous RNA sequences using sub-optimal secondary structures generated for each sequence. It is based on a vector representation of sub-optimal structures capturing structure conservation signals by weighting structural motifs according to their conservation across the sub-optimal structures.ResultsWe demonstrate that aliFreeFold provides a good balance between speed and accuracy regarding predictions of representative structures for sets of homologous RNA compared to traditional methods based on sequence and structure alignment. We show that aliFreeFold is capable of uncovering conserved structural features fastly and effectively thanks to its weighting scheme that gives more (resp. less) importance to common (resp. uncommon) structural motifs. The weighting scheme is also shown to be capable of capturing conservation signal as the number of homologous RNA increases. These results demonstrate the ability of aliFreefold to efficiently and accurately provide interesting structural representatives of RNA families.Availability and implementationaliFreeFold was implemented in C++. Source code and Linux binary are freely available at https://github.com/UdeS-CoBIUS/aliFreeFold.Supplementary information Supplementary data are available at Bioinformatics online.

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“…Over the past few years, many alignment-free algorithms have been proposed [ 58 , 59 , 60 ] to solve previously intractable challenges in phylogenomics, many of which have been compared in [ 9 , 45 ] using data from three categories—gene tree inference, genome-based phylogeny and horizontal gene transfer. Assessing the degree of topological disagreement between the inferred and reference trees, the report found that no single method performed best across all the data sets.…”

Section: Applications Of Information Theory In Computational Biolomentioning

confidence: 99%

Information Theory in Computational Biology: Where We Stand Today

Chanda

Costa²,

et al. 2020

Entropy

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“A Mathematical Theory of Communication” was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon’s work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology—gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.

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The super-n-motifs model: a novel alignment-free approach for representing and comparing RNA secondary structures

Abstract: Supplementary data are available at Bioinformatics o nline.

Cited by 9 publications

References 42 publications

Alignment-free sequence comparison: benefits, applications, and tools

Alignment-free sequence comparison: benefits, applications, and tools

aliFreeFold: an alignment-free approach to predict secondary structure from homologous RNA sequences

Information Theory in Computational Biology: Where We Stand Today

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