RNA Secondary Structure Analysis Using Abstract Shapes

Giegerich, Robert; Voß, Björn

doi:10.1002/9783527647064.ch27

Cited by 2 publications

(5 citation statements)

References 15 publications

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“…Phenotype robustness and mutation probabilities follow from . Schematic of the shapes sequence-structure map: for the example input sequence CCGCAGUCACACCAGCG, the shapes framework [38] represents the Boltzmann distribution of different folding possibilities in terms of coarse-grained shapes instead of full secondary structures, as illustrated in the central box. Each shape represents one or more full secondary structures.…”

Section: Robustness/mutation Probability Definitionsmentioning

confidence: 99%

“…Finally, a single shape, the shape with the highest Boltzmann frequency, is chosen as the output structure in the sequence-structure map. In this example, this is the stem-loop shape '[]' with a Boltzmann frequency of 71% (the shape notation uses square brackets, as described in [38]). Shapes other than the most frequent shape in the Boltzmann ensemble are only considered in our analysis of plastogenetic congruence.…”

Section: Robustness/mutation Probability Definitionsmentioning

confidence: 99%

“…The shape abstraction level [35] was chosen based on thermodynamic considerations: since single base pairs at the end of stacks only contribute a single stacking term, a desirable property of the shape abstraction would be that breaking base pairs at the end of long stacks is considered a minor change and not a change of coarse-grained structure. This requirement is not satisfied by level-1 abstractions, as defined in [38], because for example breaking the outermost base pair in (((…))) would lead to a change of coarse-grained structure. Therefore, we use level 2.…”

Section: Parametersmentioning

confidence: 99%

“…Therefore, we use level 2. In the level-2 shape abstraction, all paired and unpaired segments of the structure are recorded except the unpaired segments in multiloops, exterior loops and hairpin loops [38].…”

Section: Parametersmentioning

confidence: 99%

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Insertions and deletions in the RNA sequence–structure map

Martin

Ahnert

2021

J. R. Soc. Interface.

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Genotype–phenotype maps link genetic changes to their fitness effect and are thus an essential component of evolutionary models. The map between RNA sequences and their secondary structures is a key example and has applications in functional RNA evolution. For this map, the structural effect of substitutions is well understood, but models usually assume a constant sequence length and do not consider insertions or deletions. Here, we expand the sequence–structure map to include single nucleotide insertions and deletions by using the RNAshapes concept. To quantify the structural effect of insertions and deletions, we generalize existing definitions for robustness and non-neutral mutation probabilities. We find striking similarities between substitutions, deletions and insertions: robustness to substitutions is correlated with robustness to insertions and, for most structures, to deletions. In addition, frequent structural changes after substitutions also tend to be common for insertions and deletions. This is consistent with the connection between energetically suboptimal folds and possible structural transitions. The similarities observed hold both for genotypic and phenotypic robustness and mutation probabilities, i.e. for individual sequences and for averages over sequences with the same structure. Our results could have implications for the rate of neutral and non-neutral evolution.

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Section: Robustness/mutation Probability Definitionsmentioning

confidence: 99%

Section: Robustness/mutation Probability Definitionsmentioning

confidence: 99%

Section: Parametersmentioning

confidence: 99%

Section: Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Insertions and deletions in the RNA sequence–structure map

Martin

Ahnert

2021

J. R. Soc. Interface.

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“…Partition function calculation and sampling of 1000 secondary structures were performed using RNAstructure software ver. 5.8 with default parameters ( Mathews et al 2009 ; Giegerich and Voß 2014 ). The average Hamming distance between all combinations of the 1000 structures was used as the structural fluctuation.…”

Section: Methodsmentioning

confidence: 99%

Experimental evidence for the correlation between RNA structural fluctuations and the frequency of beneficial mutations

Maeda

Mizuuchi

Shigenobu

et al. 2022

RNA

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RNA has been used as a model molecule to understand the adaptive evolution process owing to the simple relationship between the structure (i.e., phenotype) and sequence (i.e., genotype). RNA usually forms multiple substructures with similar thermodynamic stabilities, called structural fluctuations. Ancel and Fontana theoretically proposed that structural fluctuation is directly related to the ease of change in structures by mutations and thus works as a source of adaptive evolution; however, experimental verification is limited. Here, we analyzed 76 RNA genotypes that appeared in our previous in vitro evolution to examine whether 1) RNA fluctuation decreases as adaptive evolution proceeds and 2) RNAs that have larger fluctuations tend to have higher frequencies of beneficial mutations. We first computationally estimated the structural fluctuations of all RNAs and observed that they tended to decrease as their fitness increased. We next measured the frequency of beneficial mutations for 10 RNA genotypes and observed that the total number of beneficial mutations was correlated with the size of the structural fluctuations. These results consistently support the idea that the structural fluctuation of RNA, at least those evaluated in our study, works as a source of adaptive evolution.

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RNA Secondary Structure Analysis Using Abstract Shapes

Cited by 2 publications

References 15 publications

Insertions and deletions in the RNA sequence–structure map

Insertions and deletions in the RNA sequence–structure map

Experimental evidence for the correlation between RNA structural fluctuations and the frequency of beneficial mutations

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