Random versus Deterministic Descent in RNA Energy Landscape Analysis.
Identifying sets of metastable conformations is a major research topic in RNA energy landscape analysis, and recently several methods have been proposed for finding local minima in landscapes spawned by RNA secondary structures. An important and time-critical component of such methods is steepest, or gradient, descent in attraction basins of local minima. We analyse the speed-up achievable by randomised descent in attraction basins in the context of large sample sets where the size has an order of magnitude in the region of ~10(6). While the gain for each individual sample might be marginal, the overall run-time improvement can be significant. Moreover, for the two nongradient methods we analysed for partial energy landscapes induced by ten different RNA sequences, we obtained that the number of observed local minima is on average larger by 7.3% and 3.5%, respectively. The run-time improvement is approximately 16.6% and 6.8% on average over the ten partial energy landscapes. For the large sample size we selected for descent procedures, the coverage of local minima is very high up to energy values of the region where the samples were randomly selected from the partial energy landscapes; that is, the difference to the total set of local minima is mainly due to the upper area of the energy landscapes.
Informatics Department, King's College London, London WC2R 2LS, UK.
Luke
L
Day
Informatics Department, King's College London, London WC2R 2LS, UK.
Ouala
O
Abdelhadi Ep Souki
School of Science and Technology, Middlesex University, London NW4 4BT, UK.
Andreas A
AA
Albrecht
Informatics Department, King's College London, London WC2R 2LS, UK.
Kathleen
K
Steinhöfel