Memory-efficient RNA energy landscape exploration.
Ontology highlight
ABSTRACT: Motivation
Energy landscapes provide a valuable means for studying the folding dynamics of short RNA molecules in detail by modeling all possible structures and their transitions. Higher abstraction levels based on a macro-state decomposition of the landscape enable the study of larger systems; however, they are still restricted by huge memory requirements of exact approaches.Results
We present a highly parallelizable local enumeration scheme that enables the computation of exact macro-state transition models with highly reduced memory requirements. The approach is evaluated on RNA secondary structure landscapes using a gradient basin definition for macro-states. Furthermore, we demonstrate the need for exact transition models by comparing two barrier-based approaches, and perform a detailed investigation of gradient basins in RNA energy landscapes.Availability and implementation
Source code is part of the C++ Energy Landscape Library available at http://www.bioinf.uni-freiburg.de/Software/.
SUBMITTER: Mann M
PROVIDER: S-EPMC4155248 | biostudies-literature | 2014 Sep
REPOSITORIES: biostudies-literature
Publications
Memory-efficient RNA energy landscape exploration.
Mann Martin M Kucharík Marcel M Flamm Christoph C Wolfinger Michael T MT
Bioinformatics (Oxford, England) 20140514 18
<h4>Motivation</h4>Energy landscapes provide a valuable means for studying the folding dynamics of short RNA molecules in detail by modeling all possible structures and their transitions. Higher abstraction levels based on a macro-state decomposition of the landscape enable the study of larger systems; however, they are still restricted by huge memory requirements of exact approaches.<h4>Results</h4>We present a highly parallelizable local enumeration scheme that enables the computation of exact ...[more]
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