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Platero-Rochart, D; González-Alemán, R; Hernández-Rodríguez, EW; Leclerc, F; Caballero, J; Montero-Cabrera, L.
RCDPeaks: memory-efficient density peaks clustering of long molecular dynamics.
Bioinformatics. 2022; 38(7): 1863-1869.
Doi: 10.1093/bioinformatics/btac021
Web of Science
PubMed
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- Führende Autor*innen der Med Uni Graz
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Platero Rochart Daniel de Jesus
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- Abstract:
- MOTIVATION: Density Peaks is a widely spread clustering algorithm that has been previously applied to Molecular Dynamics (MD) simulations. Its conception of cluster centers as elements displaying both a high density of neighbors and a large distance to other elements of high density, particularly fits the nature of a geometrical converged MD simulation. Despite its theoretical convenience, implementations of Density Peaks carry a quadratic memory complexity that only permits the analysis of relatively short trajectories. RESULTS: Here, we describe DP+, an exact novel implementation of Density Peaks that drastically reduces the RAM consumption in comparison to the scarcely available alternatives designed for MD. Based on DP+, we developed RCDPeaks, a refined variant of the original Density Peaks algorithm. Through the use of DP+, RCDPeaks was able to cluster a one-million frames trajectory using less than 4.5 GB of RAM, a task that would have taken more than 2 TB and about 3× more time with the fastest and less memory-hunger alternative currently available. Other key features of RCDPeaks include the automatic selection of parameters, the screening of center candidates and the geometrical refining of returned clusters. AVAILABILITY AND IMPLEMENTATION: The source code and documentation of RCDPeaks are free and publicly available on GitHub (https://github.com/LQCT/RCDPeaks.git). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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