RNA Locally Optimal Secondary Structures

Saffarian, Azadeh; Giraud, Mathieu; Monte, Antoine de; Touzet, Hélène

doi:10.1089/cmb.2010.0178

Cited by 7 publications

(10 citation statements)

References 14 publications

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“…We take this example a step further and address the problem of building a multistructure for all locally optimal secondary structures on tRNA. A locally optimal secondary structure is a secondary structure to which it is not possible to add a supplementary helix (Clote, 2005;Saffarian et al, 2012). In other words, it is a maximal clique in our helix graph of Figure 3.…”

Section: Example Of Mitochondrial Trna (Cont'd)mentioning

confidence: 99%

“…Figure 4 represents all locally optimal secondary structures for tRNA. In Saffarian et al (2012), we have proved that locally optimal secondary structures are exactly the secondary structures that are partitioned into some maximal flat structures, called maximal for juxtaposition, and corresponding to some maximal plain paths in the helix graph.…”

Section: Example Of Mitochondrial Trna (Cont'd)mentioning

confidence: 99%

“…(adapted from Saffarian et al, 2012) Given a set of helices H, a flat structure w is maximal for juxtaposition if it satisfies the following condition: for each helix f of H not present in w, then f is contained into some helix of w, or if {f} W w is a secondary structure, then f is nested in some helix of w. Here, this result allows to define G 1 .…”

Section: Example Of Mitochondrial Trna (Cont'd)mentioning

confidence: 99%

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Modeling Alternate RNA Structures in Genomic Sequences

Saffarian

Giraud

Touzet

2015

Journal of Computational Biology

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We introduce the concept of RNA multistructures, which is a formal grammar-based framework specifically designed to model a set of alternate RNA secondary structures. Such alternate structures can either be a set of suboptimal foldings, or distinct stable folding states, or variants within an RNA family. We provide several such examples and propose an efficient algorithm to search for RNA multistructures within a genomic sequence.

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Section: Example Of Mitochondrial Trna (Cont'd)mentioning

confidence: 99%

Section: Example Of Mitochondrial Trna (Cont'd)mentioning

confidence: 99%

Section: Example Of Mitochondrial Trna (Cont'd)mentioning

confidence: 99%

See 1 more Smart Citation

Modeling Alternate RNA Structures in Genomic Sequences

Saffarian

Giraud

Touzet

2015

Journal of Computational Biology

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“…Figure 2). To overcome this, a method to predict representative suboptimal secondary structures, such as locally optimal secondary structures or alignments, has been proposed [19,32,33] (cf. Supplementary Section A).…”

Section: Non-stochastic Approachesmentioning

confidence: 99%

Fighting against uncertainty: an essential issue in bioinformatics

Hamada¹

2013

Briefings in Bioinformatics

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Many bioinformatics problems, such as sequence alignment, gene prediction, phylogenetic tree estimation and RNA secondary structure prediction, are often affected by the 'uncertainty' of a solution, that is, the probability of the solution is extremely small. This situation arises for estimation problems on high-dimensional discrete spaces in which the number of possible discrete solutions is immense. In the analysis of biological data or the development of prediction algorithms, this uncertainty should be handled carefully and appropriately. In this review, I will explain several methods to combat this uncertainty, presenting a number of examples in bioinformatics. The methods include (i) avoiding point estimation, (ii) maximum expected accuracy (MEA) estimations and (iii) several strategies to design a pipeline involving several prediction methods. I believe that the basic concepts and ideas described in this review will be generally useful for estimation problems in various areas of bioinformatics.

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“…RNA energy landscape analysis in the context of metastable conformations is presented, for example, in [ 10 – 15 ]. The tool [ 10 ] processes the output of the program by Wuchty et al [ 16 ] and returns all metastable conformations located in an energy range Δ E above the minimum free energy conformation, along with a variety of additional information about the distribution of local minima.…”

Section: Introductionmentioning

confidence: 99%

Random versus Deterministic Descent in RNA Energy Landscape Analysis

Day

Souki

Albrecht

et al. 2016

Advances in Bioinformatics

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Identifying sets of metastable conformations is a major research topic in RNA energy landscape analysis, and recently several methods have been proposed for finding local minima in landscapes spawned by RNA secondary structures. An important and time-critical component of such methods is steepest, or gradient, descent in attraction basins of local minima. We analyse the speed-up achievable by randomised descent in attraction basins in the context of large sample sets where the size has an order of magnitude in the region of ~106. While the gain for each individual sample might be marginal, the overall run-time improvement can be significant. Moreover, for the two nongradient methods we analysed for partial energy landscapes induced by ten different RNA sequences, we obtained that the number of observed local minima is on average larger by 7.3% and 3.5%, respectively. The run-time improvement is approximately 16.6% and 6.8% on average over the ten partial energy landscapes. For the large sample size we selected for descent procedures, the coverage of local minima is very high up to energy values of the region where the samples were randomly selected from the partial energy landscapes; that is, the difference to the total set of local minima is mainly due to the upper area of the energy landscapes.

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RNA Locally Optimal Secondary Structures

Cited by 7 publications

References 14 publications

Modeling Alternate RNA Structures in Genomic Sequences

Modeling Alternate RNA Structures in Genomic Sequences

Fighting against uncertainty: an essential issue in bioinformatics

Random versus Deterministic Descent in RNA Energy Landscape Analysis

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