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Molecular dynamics (MD) simulation has become one of the key tools to obtain deeper insights into biological systems using various levels of descriptions such as all-atom, united-atom, and coarse-grained models. Recent advances in computing resources and MD programs have significantly accelerated the simulation time and thus increased the amount of trajectory data. Although many laboratories routinely perform MD simulations, analyzing MD trajectories is still time consuming and often a difficult task. ST-analyzer, http://im.bioinformatics.ku.edu/st-analyzer, is a standalone graphical user interface (GUI) toolset to perform various trajectory analyses. ST-analyzer has several outstanding features compared to other existing analysis tools: (i) handling various formats of trajectory files from MD programs, such as CHARMM, NAMD, GROMACS, and Amber, (ii) intuitive web-based GUI environment--minimizing administrative load and reducing burdens on the user from adapting new software environments, (iii) platform independent design--working with any existing operating system, (iv) easy integration into job queuing systems--providing options of batch processing either on the cluster or in an interactive mode, and (v) providing independence between foreground GUI and background modules--making it easier to add personal modules or to recycle/integrate pre-existing scripts utilizing other analysis tools. The current ST-analyzer contains nine main analysis modules that together contain 18 options, including density profile, lipid deuterium order parameters, surface area per lipid, and membrane hydrophobic thickness. This article introduces ST-analyzer with its design, implementation, and features, and also illustrates practical analysis of lipid bilayer simulations.

As recently demonstrated by the COVID-19 pandemic, large-scale pathogen genomic data are crucial to characterize transmission patterns of human infectious diseases. Yet, current methods to process raw sequence data into analysis-ready variants remain slow to scale, hampering rapid surveillance efforts and epidemiological investigations for disease control. Here, we introduce an accelerated, scalable, reproducible, and cost-effective framework for pathogen genomic variant identification and present an evaluation of its performance and accuracy across benchmark datasets of Plasmodium falciparum malaria genomes. We demonstrate superior performance of the GPU framework relative to standard pipelines with mean execution time and computational costs reduced by 27× and 4.6×, respectively, while delivering 99.9% accuracy at enhanced reproducibility.