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ExpaRNA-P: simultaneous exact pattern matching and folding of RNAs

Overview of attention for article published in BMC Bioinformatics, December 2014
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Title
ExpaRNA-P: simultaneous exact pattern matching and folding of RNAs
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0404-0
Pubmed ID
Authors

Christina Otto, Mathias Möhl, Steffen Heyne, Mika Amit, Gad M Landau, Rolf Backofen, Sebastian Will

Abstract

BackgroundIdentifying sequence-structure motifs common to two RNAs can speed up the comparison of structural RNAs substantially. The core algorithm of the existent approach ExpaRNA solves this problem for a priori known input structures. However, such structures are rarely known; moreover, predicting them computationally is no rescue, since single sequence structure prediction is highly unreliable.ResultsThe novel algorithm ExpaRNA-P computes exactly matching sequence-structure motifs in entire Boltzmann-distributed structure ensembles of two RNAs; thereby we match and fold RNAs simultaneously, analogous to the well-known ¿simultaneous alignment and folding¿ of RNAs. While this implies much higher flexibility compared to ExpaRNA, ExpaRNA-P has the same very low complexity (quadratic in time and space), which is enabled by its novel structure ensemble-based sparsification. Furthermore, we devise a generalized chaining algorithm to compute compatible subsets of ExpaRNA-P¿s sequence-structure motifs. Resulting in the very fast RNA alignment approach ExpLoc-P, we utilize the best chain as anchor constraints for the sequence-structure alignment tool LocARNA. ExpLoc-P is benchmarked in several variants and versus state-of-the-art approaches. In particular, we formally introduce and evaluate strict and relaxed variants of the problem; the latter makes the approach sensitive to compensatory mutations. Across a benchmark set of typical non-coding RNAs, ExpLoc-P has similar accuracy to LocARNA but is four times faster (in both variants), while it achieves a speed-up over 30-fold for the longest benchmark sequences (¿400nt). Finally, different ExpLoc-P variants enable tailoring of the method to specific application scenarios. ExpaRNA-P and ExpLoc-P are distributed as part of the LocARNA package. The source code is freely available at http://www.bioinf.uni-freiburg.de/Software/ExpaRNA-P.Conclusions ExpaRNA-P¿s novel ensemble-based sparsification reduces its complexity to quadratic time and space. Thereby, ExpaRNA-P significantly speeds up sequence-structure alignment while maintaining the alignment quality. Different ExpaRNA-P variants support a wide range of applications.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 5%
Unknown 18 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 37%
Student > Master 4 21%
Researcher 4 21%
Professor 1 5%
Professor > Associate Professor 1 5%
Other 0 0%
Unknown 2 11%
Readers by discipline Count As %
Computer Science 7 37%
Biochemistry, Genetics and Molecular Biology 5 26%
Agricultural and Biological Sciences 4 21%
Mathematics 1 5%
Unknown 2 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 02 January 2015.
All research outputs
#13,418,835
of 22,775,504 outputs
Outputs from BMC Bioinformatics
#4,191
of 7,276 outputs
Outputs of similar age
#172,518
of 352,205 outputs
Outputs of similar age from BMC Bioinformatics
#70
of 152 outputs
Altmetric has tracked 22,775,504 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,276 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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We're also able to compare this research output to 152 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.