期刊
BIOINFORMATICS
卷 30, 期 18, 页码 2584-2591出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btu337
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资金
- Austrian Science Fund (FWF) [F43]
- EU-FET grant RiboNets [323987]
- COST Action [CM1304]
- IK Computational Science - University of Vienna
- Austrian Science Fund (FWF) [F43] Funding Source: Austrian Science Fund (FWF)
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.
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