MOTIVATION: The term clustering designates a comprehensive family of unsupervised learning methods allowing to group similar elements into sets called clusters. Geometrical clustering of molecular dynamics (MD) trajectories is a well-established analysis to gain insights into the conformational behavior of simulated systems. However, popular variants collapse when processing relatively long trajectories because of their quadratic memory or time complexity. From the arsenal of clustering algorithms, HDBSCAN stands out as a hierarchical density-based alternative that provides robust differentiation of intimately related elements from noise data. Although a very efficient implementation of this algorithm is available for programming-skilled users (HDBSCAN*), it cannot treat long trajectories under the de facto molecular similarity metric RMSD. RESULTS: Here, we propose MDSCAN, an HDBSCAN-inspired software specifically conceived for non-programmers users to perform memory-efficient RMSD-based clustering of long MD trajectories. Methodological improvements over the original version include the encoding of trajectories as a particular class of vantage-point tree (decreasing time complexity), and a dual-heap approach to construct a quasi-minimum spanning tree (reducing memory complexity). MDSCAN was able to process a trajectory of 1 million frames using the RMSD metric in about 21 h with <8 GB of RAM, a task that would have taken a similar time but more than 32 TB of RAM with the accelerated HDBSCAN* implementation generally used. AVAILABILITY AND IMPLEMENTATION: The source code and documentation of MDSCAN are free and publicly available on GitHub (https://github.com/LQCT/MDScan.git) and as a PyPI package (https://pypi.org/project/mdscan/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

MOTIVATION: Classical Molecular Dynamics (MD) is a standard computational approach to model time-dependent processes at the atomic level. The inherent sparsity of increasingly huge generated trajectories demands clustering algorithms to reduce other post-simulation analysis complexity. The Quality Threshold (QT) variant is an appealing one from the vast number of available clustering methods. It guarantees that all members of a particular cluster will maintain a collective similarity established by a user-defined threshold. Unfortunately, its high computational cost for processing big data limits its application in the molecular simulation field. RESULTS: In this work, we propose a methodological parallel between QT clustering and another well-known algorithm in the field of Graph Theory, the Maximum Clique Problem. Molecular trajectories are represented as graphs whose nodes designate conformations, while unweighted edges indicate mutual similarity between nodes. The use of a binary-encoded RMSD matrix coupled to the exploitation of bitwise operations to extract clusters significantly contributes to reaching a very affordable algorithm compared to the few implementations of QT for MD available in the literature. Our alternative provides results in good agreement with the exact one while strictly preserving the collective similarity of clusters. AVAILABILITY AND IMPLEMENTATION: The source code and documentation of BitQT are free and publicly available on GitHub (https://github.com/LQCT/BitQT.git) and ReadTheDocs (https://bitqt.readthedocs.io/en/latest/), respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational biology can be considered a supradisciplinary field of knowledge that merges biology, chemistry, physics, and computer science into a broad-based science that is important to furthering our understanding of the life sciences.Although a relatively new area of research, it is recognized as a crucial field for scientific advancement in developing countries.This Perspective introduces our vision of the role of computational biology in biomedical research and teaching in Cuba.Except where individuals are directly quoted, any opinions expressed herein should be considered those of the authors.(For author information, see Box 1.)The challenge for Cuba was to initiate this new field of research and development without existing expertise.In addition, we had to face the same problems experienced elsewhere in this field-most life scientists were not familiar with the potential of information processing tools, and conversely, computer scientists were unfamiliar with problems facing the life sciences, often brought about by large amounts of new data.Cuba also faced unique problems.As a result of the United States trade embargo, Cuban scientists buy most of their research supplies from Europe.This adds delays and can triple costs, especially for equipment or spare parts that are made only in the US and must be purchased from a third party.In addition, curbs on travel between the US and Cuba isolate Cuban scientists from colleagues and conferences in the United States.Finally, Internet access is also affected by this policy, and Cuban scientists lack fast and efficient access to scientific information from overseas.Despite these drawbacks, Cuban biotech products and activities from biomedical institutes within Cuba have been recognized by other authors [1-4].Further offsetting these drawbacks, our country has a high educational level, and Cuban universities produce hundreds of new scientists every year.The challenge then is to implement higher education using both the coherence of existing disciplinary education and the means to break down disciplinary walls for students interested in computational biology and other multidisciplinary fields.Since Cuba has scant resources for significant investments in scientific instrumentation, a science such as computational biology that relies mostly on computers, network connectivity, and human resources offers an excellent opportunity.The current challenges facing computational biology research in Cuba are: (1) getting results of direct interest and impact to motivate funding, (2) fostering young leading scientists to sustain present and future research, and (3) using limited infrastructure in the most efficient and productive way.Computational sciences in Cuba were first applied to physics and chemistry in the late 1960s at the University of Havana, with the use of quantum mechanics to model sugar cane derivative molecules [5].Such an early application of this technology, which was entirely absent in the majority of the world, indicated a new approach to scientific development in a Latin American country.Another important effort began in the early 1970s at the National Center for Scientific Research in the field of computational neurosciences, mostly with Cuban-designed and Cubanmanufactured hardware [6].Since then, after a heavy effort to build up human scientific potential, mostly with the influence and support of Eastern European countries, the problem of Cuba's limited computing potential was overcome and Cuban scientists gained valuable experience with microcomputers in the 1980s.Limitations to high-tech development during the 1970s originated from the obsolescence of devices built in the former Soviet bloc and a strong dependence on components from the same market, which hindered Cuba's national hardware production.Cuba was well-positioned to take advantage of the benefits of the microcomputer revolution of the 1980s.Applications of these new devices to the physical and biological sciences were frequent in that time and several computer programs were written or adapted to allow such applications [7,8].During the economic crisis of the 1990s, these developments continued in the established groups and appeared in new teams at the Cuban Center for Genetic Engineering and Biotechnology (CIGB) [9].