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Article Dans Une Revue Bioinformatics Année : 2022

RCDPeaks: memory-efficient density peaks clustering of long molecular dynamics

Résumé

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.
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Dates et versions

hal-03767732 , version 1 (23-11-2022)

Identifiants

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Daniel Platero-Rochart, Roy González-Alemán, Erix Hernández-Rodríguez, Fabrice Leclerc, Julio Caballero, et al.. RCDPeaks: memory-efficient density peaks clustering of long molecular dynamics. Bioinformatics, 2022, 38 (7), pp.1863-1869. ⟨10.1093/bioinformatics/btac021⟩. ⟨hal-03767732⟩
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