RNA STRAND: The RNA Secondary Structure and Statistical Analysis Database

Andronescu, Mirela; Bereg, Vera; Hoos, Holger H.; Condon, Anne

doi:10.1186/1471-2105-9-340

Cited by 254 publications

(259 citation statements)

References 44 publications

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“…The SRP, hammerhead, and cis-regulatory sequences were obtained via the RNA strand website (Andronescu et al 2008). Performance of G FJC2 , measured by the percentage of times the ''most accurate'' structure for each sequence appears with the lowest free energy, remains similar to that of Mfold for these classes (see Table 1).…”

Section: Testing G Fjc2 With Mfoldmentioning

confidence: 99%

A two-length-scale polymer theory for RNA loop free energies and helix stacking

Aalberts

Nandagopal²

2010

RNA

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The reliability of RNA secondary structure predictions is subject to the accuracy of the underlying free energy model. Mfold and other RNA folding algorithms are based on the Turner model, whose weakest part is its formulation of loop free energies, particularly for multibranch loops. RNA loops contain single-strand and helix-crossing segments, so we develop an enhanced twolength freely jointed chain theory and revise it for self-avoidance. Our resulting universal formula for RNA loop entropy has fewer parameters than the Turner/Mfold model, and yet simulations show that the standard errors for multibranch loop free energies are reduced by an order of magnitude. We further note that coaxial stacking decreases the effective length of multibranch loops and provides, surprisingly, an entropic stabilization of the ordered configuration in addition to the enthalpic contribution of helix stacking. Our formula is in good agreement with measured hairpin free energies. We find that it also improves the accuracy of folding predictions.

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Section: Testing G Fjc2 With Mfoldmentioning

confidence: 99%

A two-length-scale polymer theory for RNA loop free energies and helix stacking

Aalberts

Nandagopal²

2010

RNA

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“…PDB 00434 PDB 00200 PDB 00876 Figure 3: Structures of PDB 00434, PDB 00200, and PDB 00876 obtained from RNA STRAND v2.0 website [34].…”

Section: Resultsmentioning

confidence: 99%

Exact Learning of RNA Energy Parameters from Structure

Chitsaz

Aminisharifabad

2014

Lecture Notes in Computer Science

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We consider the problem of exact learning of parameters of a linear RNA energy model from secondary structure data. A necessary and sufficient condition for learnability of parameters is derived, which is based on computing the convex hull of union of translated Newton polytopes of input sequences [1]. The set of learned energy parameters is characterized as the convex cone generated by the normal vectors to those facets of the resulting polytope that are incident to the origin. In practice, the sufficient condition may not be satisfied by the entire training data set; hence, computing a maximal subset of training data for which the sufficient condition is satisfied is often desired. We show that problem is NP-hard in general for an arbitrary dimensional feature space. Using a randomized greedy algorithm, we select a subset of RNA STRAND v2.0 database that satisfies the sufficient condition for separate A-U, C-G, G-U base pair counting model. The set of learned energy parameters includes experimentally measured energies of A-U, C-G, and G-U pairs; hence, our parameter set is in agreement with the Turner parameters.

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“…Many structures have been revised, and many new structures have become available. Structures were obtained as follows: Structures of small and large subunit ribosomal RNAs and group I introns were obtained from RNA STRAND v2.0 (Andronescu et al 2008). Structures of 5S ribosomal RNAs were obtained from the 2005 update of the 5S ribosomal RNA Database (Szymanski et al 1998).…”

Section: Database Of Test Structuresmentioning

confidence: 99%

Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures

Sloma

Mathews

2016

RNA

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RNA secondary structure prediction is widely used to analyze RNA sequences. In an RNA partition function calculation, free energy nearest neighbor parameters are used in a dynamic programming algorithm to estimate statistical properties of the secondary structure ensemble. Previously, partition functions have largely been used to estimate the probability that a given pair of nucleotides form a base pair, the conditional stacking probability, the accessibility to binding of a continuous stretch of nucleotides, or a representative sample of RNA structures. Here it is demonstrated that an RNA partition function can also be used to calculate the exact probability of formation of hairpin loops, internal loops, bulge loops, or multibranch loops at a given position. This calculation can also be used to estimate the probability of formation of specific helices. Benchmarking on a set of RNA sequences with known secondary structures indicated that loops that were calculated to be more probable were more likely to be present in the known structure than less probable loops. Furthermore, highly probable loops are more likely to be in the known structure than the set of loops predicted in the lowest free energy structures.

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RNA STRAND: The RNA Secondary Structure and Statistical Analysis Database

Cited by 254 publications

References 44 publications

A two-length-scale polymer theory for RNA loop free energies and helix stacking

A two-length-scale polymer theory for RNA loop free energies and helix stacking

Exact Learning of RNA Energy Parameters from Structure

Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures

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