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Sparse RNA folding revisited: space-efficient minimum free energy structure prediction

Overview of attention for article published in Algorithms for Molecular Biology, April 2016
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Title
Sparse RNA folding revisited: space-efficient minimum free energy structure prediction
Published in
Algorithms for Molecular Biology, April 2016
DOI 10.1186/s13015-016-0071-y
Pubmed ID
Authors

Sebastian Will, Hosna Jabbari

Abstract

RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by [Formula: see text], but are typically much smaller. The time complexity of RNA folding is reduced from [Formula: see text] to [Formula: see text]; the space complexity, from [Formula: see text] to [Formula: see text]. Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA-RNA-interaction prediction are expected to profit even stronger than "standard" MFE folding. SparseMFEFold is free software, available at http://www.bioinf.uni-leipzig.de/~will/Software/SparseMFEFold.

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Geographical breakdown

Country Count As %
United States 2 10%
Unknown 18 90%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 20%
Researcher 4 20%
Professor 2 10%
Student > Ph. D. Student 1 5%
Unspecified 1 5%
Other 2 10%
Unknown 6 30%
Readers by discipline Count As %
Computer Science 6 30%
Biochemistry, Genetics and Molecular Biology 5 25%
Agricultural and Biological Sciences 3 15%
Unspecified 1 5%
Unknown 5 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 April 2016.
All research outputs
#22,758,309
of 25,371,288 outputs
Outputs from Algorithms for Molecular Biology
#235
of 265 outputs
Outputs of similar age
#270,470
of 313,363 outputs
Outputs of similar age from Algorithms for Molecular Biology
#12
of 14 outputs
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