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

SUBMITTER: Mann M 

PROVIDER: S-EPMC4155248 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature