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Memory-efficient RNA energy landscape exploration.


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

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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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