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Introducing GPU Acceleration into the Python-Based Simulations of Chemistry Framework.
J Phys Chem A
January 2025
Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.
We introduce the first version of GPU4PySCF, a module that provides GPU acceleration of methods in PySCF. As a core functionality, this provides a GPU implementation of two-electron repulsion integrals (ERIs) for contracted basis sets comprising up to functions using the Rys quadrature. As an illustration of how this can accelerate a quantum chemistry workflow, we describe how to use the ERIs efficiently in the integral-direct Hartree-Fock build and nuclear gradient construction.
View Article and Find Full Text PDFBaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories.
J Chem Inf Model
January 2025
Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1218 S 5th Ave, Monrovia, California 91016, United States.
Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially.
View Article and Find Full Text PDFEpiMapper: A new tool for analyzing high-throughput sequencing from CUT&Tag.
Comput Biol Med
January 2025
Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Lørenskog, Norway; Medical Division (EpiGen), Akershus University Hospital, Lørenskog, Norway. Electronic address:
Since the invention of next-generation sequencing, new methods have been developed to understand the regulation of gene expression through epigenetic markers. Among these, CUT&Tag (Cleavage Under Targets and Tagmentation) analysis has emerged as an efficient epigenomic profiling technique with low input requirements, high sensitivity, and low background signals. Although wet-lab techniques are available, data analysis remains challenging for scientists without expert-level computational skills.
View Article and Find Full Text PDFBio- and Chemoinformatic Approaches for Metabolomics Data Analysis.
Methods Mol Biol
January 2025
Institute for Biomedicine, Eurac Research, Bolzano, Italy.
Metabolomics data analysis includes, next to the preprocessing, several additional repetitive tasks that can however be heavily dataset dependent or experiment setup specific due to the vast heterogeneity in instrumentation, protocols, or also compounds/samples that are being measured. To address this, various toolboxes and software packages in Python or R have been and are being developed providing researchers and analysts with bioinformatic/chemoinformatic tools to create their own workflows tailored toward their specific needs. This chapter presents tools and example workflows for common tasks focusing on the functionality provided by R packages developed as part of the RforMassSpectrometry initiative.
View Article and Find Full Text PDFAn Open-source Python Tool for Traction Force Microscopy on Micropatterned Substrates.
Bio Protoc
January 2025
Laboratoire Interdisciplinaire de Physique (LIPhy), Université Grenoble Alpes, CNRS, Grenoble, France.
Cell-generated forces play a critical role in driving and regulating complex biological processes, such as cell migration and division and cell and tissue morphogenesis in development and disease. Traction force microscopy (TFM) is an established technique developed in the field of mechanobiology used to quantify cellular forces exerted on soft substrates and internal mechanical tissue stresses. TFM measures cell-generated traction forces in 2D or 3D environments with varying mechanical and biochemical properties.
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