Evidence map›Paper›PMID 25551362›Full record

ArticleBMC bioinformatics2014

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

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

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Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Christina OttoBioinformatics, Institute of Computer Science, University of Freiburg, Freiburg, Germany. schmiedc@informatik.uni-freiburg.de.
Mathias MöhlBioinformatics, Institute of Computer Science, University of Freiburg, Freiburg, Germany. mamo181@googlemail.com.
Steffen HeyneMax Planck Institute of Immunobiology and Epigenetics, Stuebeweg 51, Freiburg, 79108, Germany. heyne@informatik.uni-freiburg.de.
Mika AmitDepartment of Computer Science, University of Haifa, Mount Carmel, Haifa, Israel. mika.amit2@gmail.com.
Gad M LandauDepartment of Computer Science, University of Haifa, Mount Carmel, Haifa, Israel. landau@univ.haifa.ac.il.
Rolf BackofenBioinformatics, Institute of Computer Science, University of Freiburg, Freiburg, Germany. backofen@informatik.uni-freiburg.de.
Sebastian WillBioinformatics, Institute of Computer Science, University of Freiburg, Freiburg, Germany. swill@csail.mit.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 .

conclusionsExpaRNA-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.

Indexed as

AlgorithmsRNA FoldingSequence Homology, Nucleic AcidRNASequence Analysis, RNASoftwareRNA

Identifiers

PMID25551362
PMCPMC4302096

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.