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Memory-efficient RNA energy landscape exploration

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; how...

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Detalles Bibliográficos
Autores principales: Mann, Martin, Kucharík, Marcel, Flamm, Christoph, Wolfinger, Michael T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4155248/
https://www.ncbi.nlm.nih.gov/pubmed/24833804
http://dx.doi.org/10.1093/bioinformatics/btu337
Descripción
Sumario: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/. Contact: mmann@informatik.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.