搜索结果：MacOS

As high-performance computing provides the ability to generate and analyze ever larger simulation trajectories, the challenges in learning, applying, and sharing the best analytical practices become more salient. Extracting reproducible scientific insights from simulation requires a thorough understanding of many computing topics unrelated to the molecular systems being modeled and simulated. While the rapid development of the technologies used for analysis makes previously impossible studies into routine work, the growing repertoire of software combined with the specificity of the ecosystems that they rely on can easily break the programs used in older studies. In this work, we present ST-Analyzer, a simulation trajectory analysis suite with command-line (CLI) and graphical (GUI) user interfaces. ST-Analyzer is distributed freely as an open-source conda-forge package with support for macOS, Linux, and Windows (via WSL2). Besides facilitating several common analysis tasks, the GUI shows users the exact commands necessary to repeat the same tasks on the command-line. We demonstrate ST-Analyzer's capabilities by reproducing several results from previously published simulation studies on the lipid parameters of heterogeneous biomembranes and the behavior of a SARS-CoV-2 spike protein-antibody complex. We expect ST-Analyzer to be useful to experts for quickly setting up common analysis tasks and to nonexperts as a guided introduction to simulation analysis using both GUI and CLI. ST-Analyzer is freely available at https://github.com/nk53/stanalyzer.

Accurate 2D characterization of X-ray tube focal spot dimensions (FS) and detector Point Spread Function (PSF) is essential for radiographic quality assurance, yet traditional methods (pinhole, slit cameras) are either limited to 1D characterization or require impractical setups. This article introduces SCOPE-XR, an open-source Python framework that implements and generalizes a previously established reconstruction technique, providing fully automated 2D estimation of FS distributions and detector PSF from a single radiograph of a basic test object. The software is targeted at medical physicists, researchers, and clinical quality control personnel to streamline and enhance routine acceptance testing. SCOPE-XR processes radiographic images to estimate the shape and dimensions of FS and PSF distributions. The underlying algorithm utilizes automatic circle detection, derivation, pseudo-CT reconstruction and incorporates an oversampling strategy to improve PSF reconstruction accuracy at limited sampling densities. The software was validated against both virtually simulated datasets and experimental clinical acquisitions, demonstrating high fidelity in characterizing source morphology and detector responses. SCOPE-XR is implemented in Python and is cross-platform compatible (Windows, macOS, Linux), requiring minimal computational resources. The software accepts standard radiographic image formats (e.g., [DICOM, TIFF, RAW]) as input and outputs 2D emission profiles, quantitative dimensional metrics, and performance plots. A small dataset of virtual and experimental acquisitions is included as an example for benchmarking and reproducibility. The source code, datasets, and comprehensive documentation are publicly accessible via its public repository: https://doi.org/10.15161/oar.it/hrrqs-cn059. SCOPE-XR provides a practical, fully automated alternative to traditional measurement techniques. Its primary clinical and scientific applications include the streamlined evaluation of imaging system performance during acceptance testing, routine quality control, and system design characterization.

Phylogenetic trees are ubiquitous and central to biology, but most published trees are available only as visual diagrams and not in the machine-readable Newick format. There are, thus, thousands of published trees in the scientific literature that are unavailable for follow-up analyses, comparisons, and supertree construction. Experts can easily read such diagrams, but the manual construction of a Newick string from a diagram is laborious, error-prone, and time-consuming. Previous attempts to semi-automate the reading of tree images relied on image processing techniques. These often encounter difficulties as typical published tree diagrams contain various graphical elements and annotations that overlap the branches, such as error bars on internal nodes. Here we introduce Treemble, a user-friendly desktop application for generating Newick strings from tree images. The user simply clicks to mark node locations, assisted by a deep learning-based node detection tool, and Treemble algorithmically assembles the tree from the node coordinates alone. Treemble also facilitates the automatic reading of tip name labels and can be used for both rectangular and circular trees. Treemble is a native desktop application for macOS and Windows and is freely available, with documentation, at treemble.org. Source code is available at github.com/John-Allard/Treemble. The trained node detection model is available at huggingface.co/John-Allard/treemble-1.

Mixtum is a Python-based code that estimates ancestry contributions in a process of two-way admixture based on bi-allelic genotype data. The outcomes of Mixtum come from the geometric interpretation of the f-statistics formalism. Designed with user-friendliness as a priority, Mixtum allows to interactively handle a menu of user-supplied populations to build different mixture models in conjunction with the set of auxiliary populations required by the framework. The results are presented graphically and numerically. Importantly, Mixtum provides a novel index (an angle) that assesses the quality of the ancestral reconstruction of the model under scrutiny. The use and interpretation of the outcomes of Mixtum are explained and illustrated with case studies. The open source code is available on GitHub at https://github.com/jmcastelo/mixtum and on Zenodo at https://doi.org/10.5281/zenodo.17789375. Mixtum is implemented in Python and runs on Linux, Windows and macOS.

Single-cell RNA sequencing (scRNA-seq) enables detailed profiling of cellular heterogeneity, but complex workflows and diverse data formats limit accessibility for clinicians and researchers without programming expertise. We introduce ShinySC, an R/Shiny-based desktop application designed to streamline comprehensive scRNA-seq analysis through an intuitive graphical interface. ShinySC supports various input formats, including 10x Genomics, Seurat, Scanpy, BD Rhapsody, and CellView. The tool integrates essential analytical procedures such as quality control, normalization, dimensionality reduction, clustering, marker gene identification, batch correction, differential expression analysis, and trajectory inference. Notably, ShinySC implements multiple automatic cell-type annotation methods-reference-based (SingleR), marker-based (ScType, scCATCH), and GPT-based (GPTCelltype)-with features for side-by-side comparison and manual label refinement. Benchmarking indicates robust performance for datasets containing up to 200,000 cells on standard desktop systems with 64 GB RAM, with analysis duration dependent on specific tasks and annotation methods. Demonstrative analyses of PBMC and interferon-stimulated datasets confirm ShinySC's efficacy in accurately annotating cell types and capturing condition-specific transcriptional dynamics. ShinySC provides a unified, user-friendly, and scalable platform for scRNA-seq analysis explicitly tailored for non-programming users. It surpasses existing limitations by accommodating multiple data formats, employing versatile annotation strategies, and generating high-quality, publication-ready figures. Available freely across Windows, macOS, and Linux platforms, ShinySC enhances the accessibility and reproducibility of single-cell transcriptomic research. http://tardis.cgu.edu.tw/ShinySC.

The residue interaction network (RIN) model has been a useful approach for identifying functional residues and ligand-binding sites in different protein structures based on their contact topology. Here, we present RinPy, a pip-installable Python package, and a graphical user interface (GUI) that depends on a RIN model. RinPy is designed for constructing, visualizing, and analyzing RINs for single or multiple protein input structures as well as molecular dynamics trajectories, allowing the calculation of three centrality measures, namely degree, closeness, and betweenness of the residues. The nodes with the highest betweenness scores are used to suggest putative allosteric sites, and the graph spectral analysis provides hinge regions linking close-neighboring clusters. The program is enhanced using a multiprocessing module to enable efficient analysis of large protein structures and employing user-defined parameters necessary for network analysis. The package supports the integration of nucleotides, cofactors, small molecules, water, and ions directly into the network analysis, which may be highly beneficial to understand the functional interactions in protein complexes. The program consists of numerous modules producing multiple outputs, including interactive visualizations to facilitate interpretation of the results. It also provides comparative network analysis to quantify how perturbations, such as ligand binding or mutations, may alter the contact topology of protein complexes. RinPy is implemented to a data set of monomeric KRAS and KRAS-SOS1 complexes as a case study, demonstrating its ability to identify known functional and allosteric site residues and highlighting its rapid calculation and analysis of RINs. The RinPy package is compatible with Windows, macOS, and Linux operating systems. RinPy is an open-source Python package distributed via the Python Package Index (PyPI) under the name rinpy, with its source code available on GitHub. The RinPy GUI is also available as a standalone executable for Windows.

Drafting chemical compound patents, particularly those involving Markush structures, is a complex task often hindered by manual workflows that are time-consuming and error-prone and may result in incomplete protection. To address these challenges, we introduce SpaceExpander, an open-source tool designed to automate the generation of Markush patent claims with improved accuracy and efficiency. The tool systematically extracts molecular scaffolds, identifies R-groups, and constructs comprehensive Markush structures to expand patent coverage. To enhance accessibility and usability, we provide a publicly available web server for online usage, along with standalone executables compatible with Windows, macOS, and Linux to support offline access.

The rapid expansion of multi-omics data has enabled the development of molecular signatures-coordinated patterns of molecular features that serve as powerful biomarkers for diagnosis, prognosis, and therapeutic decision-making. Despite their potential, many published signatures suffer from limited reproducibility and narrow applicability, partly due to challenges in summarizing complex, multi-feature profiles into a single, statistically sound and biologically meaningful score. Here, we introduce sigscores, an R package that streamlines the computation of summary scores for molecular signatures. Building on the quality control principles of our earlier tool, sigQC, sigscores supports an extensive array of scoring metrics-including measures of central tendency, dispersion, and aggregation. It incorporates a resampling framework to generate empirical null distributions for rigorous significance assessment and provides integrated visualization tools for diagnostic evaluation. Optimized for parallel execution on multi-core systems, sigscores is well-suited for both exploratory research and high-throughput large-scale applications. Source code freely available for download on GitHub at https://github.com/alebarberis/sigscores, implemented in R and supported on MacOS and MS Windows.

m6AnetAnalyzer is an R package that streamlines post-processing and interpretation of site-level m6A predictions from m6Anet. It summarizes m6A distributions across transcripts, genes, biotypes, and transcript regions, and enables functional annotation using user-provided BED files or built-in datasets, including RNA-binding proteins and SNPs. Condition-specific changes in m6A methylation are quantified using the log2-transformed weighted modification ratio, with statistical tests applied when appropriate to identify significant differential methylation. By integrating differential gene expression data, m6AnetAnalyzer links methylation changes with expression differences, offering biotype- and region-specific insights into how m6A localization patterns relate to transcriptional regulation. Availability  and implementation m6AnetAnalyzer is freely available at https://github.com/hannalee809/m6AnetAnalyzer. It is compatible with Linux, macOS, and Windows platforms. Detailed installation instructions, example input and output files, and a step-by-step analysis workflow are provided in the package vignette.

Although modern web technologies increasingly rely on high-performance rendering methods to support rich visual content across a range of devices and operating systems, the field remains significantly under-researched. The performance of animated visual elements is affected by numerous factors, including browsers, operating systems, GPU acceleration, scripting load, and device limitations. This study systematically evaluates animation performance across multiple platforms using a unified set of circle-based animations implemented with eight web-compatible technologies, including HTML, CSS, SVG, JavaScript, Canvas, and WebGL. Animations were evaluated under controlled feature combinations involving random motion, distance, colour variation, blending, and transformations, with object counts ranging from 10 to 10,000. Measurements were conducted on desktop operating systems (Windows, macOS, Linux) and mobile platforms (iOS, Android), using CPU utilisation, GPU memory usage, and frame rate (FPS) as key metrics. Results show that DOM-based approaches maintain stable performance at 100 animated objects but exhibit notable degradation by 500 objects. Canvas-based rendering extends usability to higher object counts, while WebGL demonstrates the most stable performance at large scales (5000-10,000 objects). These findings provide concrete guidance for selecting appropriate animation technologies based on scene complexity and target platform.

Infectious disease dynamics result from the complex interplay of epidemiological, ecological and evolutionary (epi-eco-evo) processes. Accurately modelling these coupled processes is crucial for understanding pathogen spread and informing public health strategies. However, existing genomic epidemiology simulators typically assume conditional independence among these processes: generating transmission trees independently of pathogen evolution, and then superimposing neutral mutations onto fixed genealogies without ecological feedback. This simplification fails to capture how pathogen evolution dynamically reshapes epidemic trajectories.We introduce e3SIM, an open-source, agent-based, forward-time simulator for macOS and Linux that explicitly integrates pathogen transmission dynamics, molecular evolution and environmental factors. e3SIM incorporates configurable compartmental models, user-defined host contact networks, customizable pathogen genetic architectures and optional eco-evolutionary features (e.g. within-host dynamics, multi-strain infections). This integration enables realistic modelling of pathogen spread and evolution. Key features include modularity, flexible epidemiological and population-genetic modelling, time-varying environmental factors and a user-friendly graphical interface.We demonstrated e3SIM's capabilities by simulating SARS-CoV-2 and Mycobacterium tuberculosis outbreaks. e3SIM captured the emergence and spread of drug-resistant variants under sequential treatments, highlighting how pathogen evolution and environmental variations dynamically reshape epidemic trajectories. We also illustrated how interactions between pathogen transmissibility and host population structures, particularly those involving socially active "superspreaders", strongly influence pathogen lineage expansion and transmission clusters. Runtime profiling demonstrated computational efficiency and scalability.e3SIM provides a powerful tool for simulating infectious disease dynamics through the explicit integration of epi-eco-evo processes, substantially enhancing realism and predictive accuracy in genomic epidemiology. Its modular, user-friendly design supports broad applications across diverse host-pathogen systems, enabling rigorous exploration of scenarios critical to public health.

Characterizing the physical organization of the genome is essential for understanding long-range gene regulation, chromatin compartmentalization, and epigenetic accessibility. Hi-C experiments generate two-dimensional (2D) genome-wide contact maps of chromatin interactions by capturing the spatial proximity between genomic loci, which reveal interaction frequencies but lack the spatial resolution needed to interpret the three-dimensional (3D) genome structure(s). Emerging evidence suggests that epigenetic regulation is closely linked to 3D genome architecture, and that structural changes over time (4D) drive key biological processes in development, disease, and environmental response. Thus, integrating 3D structure with functional data is critical for a more complete understanding of genome regulation. Previous work, most notably the 4DHiC chromosome modeling framework, has shown that physical multi-dimensional modeling approaches rooted in polymer physics and molecular dynamics can resolve these structures at biologically meaningful resolutions by integrating temporal Hi-C data with physical constraints to uncover dynamic chromosome reorganization. Thus, molecular dynamics simulations, constrained by Hi-C contact matrices, can resolve fine-scale structural changes and reveal functionally significant transitions in chromatin conformation. Herein, we present the 4D Genome Browser Workflow (4DGBWorkflow) and the 4D Genome Browser (4DGB). The algorithm is based on the 4DHiC method, and the containerized tool is an end-to-end workflow that can transform, filter, and view 4D epigenomics and chromatin datasets, allowing non-specialists to apply three-dimensional modeling principles to diverse datasets and experimental conditions. The software executes on a laptop running macOS, Linux or Windows. From input Hi-C files (.hic), the 4DGBWorkflow produces 3D reconstructions of chromosomes, integrates the reconstruction with track data (e.g., epigenetic marks, transcriptome profiles), and provides comparative visualization of the results in a single workflow. The 4DGBWorkflow and 4D Genome Browser are open-source tools for comparative analysis and visualization of 4D chromosome datasets, including chromatin architecture and epigenomic signals. Automatic integration of Hi-C data with molecular dynamics democratizes the construction of time resolved 3D genome structures, simplifying complex simulations and data integration schemes. The online version contains supplementary material available at 10.1186/s12859-025-06361-4.

EZR (Easy R) is a statistical software package that is freely available on our website ( https://www.jichi.ac.jp/usr/hema/EZR/statmed.html ) and can be used on both Windows (Microsoft Corporation, USA) and macOS (Apple, USA) systems. EZR is built on R and R Commander and offers a range of statistical functions, including survival analyses with competing risks or time-dependent covariates, receiver operating characteristic curve analyses, meta-analyses, and sample size calculations, all accessible through a point-and-click graphical interface. A previous report that described the installation and basic operation of EZR ("Investigation of the freely available easy-to-use software 'EZR' for medical statistics", Bone Marrow Transplant, 2013) has been cited in more than 14,000 scientific papers as of November 2025. This report describes the procedures for performing propensity score (PS) analysis, including PS matching and inverse probability weighting, and compares these approaches with conventional multivariate analyses.

Multiple sequence alignments are a crucial step in many bioinformatic and computational biology analyses, from protein structure and function prediction to the inference of phylogenetic trees. However, highly divergent sequence alignments often contain a significant amount of noise. Reducing noise is normally achieved by filtering the alignment to remove columns that are poorly aligned or offer minimal useful information-either automatically using various software tools or through manual inspection. Manual approaches are labor-intensive and less reproducible but can utilize the researcher's specialist knowledge, rather than relying on filtering criteria that might not be adequate for each alignment. AliFilter bridges these two approaches to alignment curation, using machine learning to automate manual alignment filtering. AliFilter uses a supervised learning approach to create a model from a small number of manually annotated alignments, then applies this model to reproduce the manual annotation on different datasets. Users can employ the program with a default model or create customized models for individual datasets or filtering criteria. AliFilter accurately reproduces the results of manual annotation (98% accuracy) while being resilient to mistakes in the training data. In a typical phylogenomic workflow, AliFilter reduced the runtime by 35% and produced results that were almost identical to the full alignment, unlike other filtering tools we tested. AliFilter is free and open-source software; it is written in C# and distributed under a GPLv3 license from https://github.com/arklumpus/AliFilter, where both the source code and standalone executables for Windows, macOS, and Linux are available.

We present PES-trotter, a cross-platform application for the exploration and analysis of 3D potential-energy landscapes generated from multi-dimensional potential-energy surfaces (PESs) of molecular systems. Inspired by the main ideas behind the previously released software AVATAR (Martino et al., Journal of Computational Chemistry 41 (2020): 1310-1323) relying on virtual-reality technology and third-party commercial software, PES-trotter is based on the open-source game engine Godot and related open-source assets, and introduces radically new important features for the navigation and topological analysis of the PES, among which the possibility of (1) navigating in walk, fly or free-mouse mode, (2) plotting energy profiles from custom curvilinear paths, (3) computing critical points and minimum-energy paths, and (4) playing back dynamical trajectories in first-person ride or spectator mode. Designed to match high portability and adaptability, PES-trotter can be run either as a stand-alone application on Windows, Linux, Android and macOS operating systems or in the window of a simple web browser on devices as widespread as mobile phones. In the article, the main features of PES-trotter are thoroughly described through two illustrative applications (the conformational analysis of a functionalized glycine and the analysis of classical trajectories for the H + LiH+ → $$ \to $$ H2 + Li+ reactive process) highlighting the versatilty of PES-trotter as an innovative and accessible tool supporting chemical research and education.

This note introduces HaploThread, a user-friendly desktop software with a graphical user interface designed for constructing and visualizing haplotype networks. Developed in C++ using the Qt library, HaploThread integrates network visualization with multiple multithreaded haplotype construction algorithms-including McAN and fastHaN (which incorporates MSN, MJN, and TCS)-through a modular plugin architecture. It provides an intuitive workflow for building and visualizing haplotype networks from large-scale datasets, while also supporting functional extensions via plugins to facilitate the analysis of genetic variation and evolutionary relationships. HaploThread is released as open-source software under the GNU General Public License. Its source code and precompiled executables for Windows and macOS are freely available at https://ngdc.cncb.ac.cn/biocode/tool/BT007948 and https://github.com/git-xubo/HaploThread.

The plasma membrane and accompanying cortex serve as one of the major hubs of the signal transduction and cytoskeletal activities that collectively regulate numerous cell physiological processes such as migration, polarity, macropinocytosis, phagocytosis, cytokinesis, etc. Yet, dynamically tracking membrane-cortex associated protein or lipid kinetics over time from live-cell image series remains a challenging task, primarily due to the difficulty of accurately extracting and aligning the cell boundary between consecutive frames, as the cell continuously deforms and moves. Here, we present Membrane Kymograph Generator, a cross-platform software that accepts multichannel time-lapse live-cell fluorescent imaging datasets as input and automates the cumbersome, heuristic process of boundary tracking, inter-frame alignment, and intensity sampling along the boundary. The software implements a rotational offset minimization algorithm that circularly aligns boundaries across consecutive frames by exhaustively searching for the optimal angular shift that minimizes point-to-point distances, while handling variations in boundary point counts due to cell shape changes. The software outputs kymographs that represent spatiotemporal dynamics of different membrane-associated proteins or biosensors, allows users to fine-tune visualization parameters through an interactive interface, and provides built-in correlation analysis tools for multi-channel datasets. Furthermore, the software allows advanced programmatic usage for batch processing and further analysis via a native API. Our validation tests demonstrated that the Membrane Kymograph Generator can be used to accurately track, visualize, and quantitate the spatial kinetics of a wide array of membrane proteins and lipid biosensors over extended time periods, in a variety of cell types, including Dictyostelium amoeba, human neutrophils, mouse macrophages, and different mammalian cancer cells. The GUI-based software is user-friendly, does not require any technical expertise from users, and significantly reduces the manual effort and time required for kymograph generation and downstream analysis, while ensuring high accuracy and reproducibility. Membrane Kymograph Generator is a free and open-source software, licensed under GNU General Public License 3.0 or later. This software is cross-platform: It can be graphically installed on both x86-64 and AArch64/ARM64 computers, running either Windows, macOS, or any standard Linux distribution. The software is distributed as single installer files (and portable executables) targeting specific hardware architectures and operating systems, and hence, it can be installed natively without any dependency resolution. The source code, detailed documentation, specific installers, portable binaries, and test data are freely available at https://github.com/tatsatb/membrane-kymograph-generator. Additionally, since the software is written in Python, it can also be installed inside any Python environment using PIP package manager (package ID: https://pypi.org/project/membrane-kymograph) and can also be interacted via a built-in Python API.

Next-generation sequencing (NGS) has expanded the scope of forensic genetics by providing sequence-level resolution of Short Tandem Repeats (STRs). We developed HipSTR-UI, a cross-platform graphical interface that integrates precompiled HipSTR binaries for Windows, macOS, and Linux. The interface automates the genotyping workflow, from alignment files (BAM/CRAM) and target regions with the reference genome (FASTA) to the generation of genotypes in VCF format. HipSTR-UI includes a graphical parameter panel and exportable results. The validation was performed using Phase 3 of the 1000 Genomes Project, previously analyzed with the HipSTR command-line version. HipSTR-UI accurately reproduced the results of HipSTR, achieving 100 % concordance in genotype calls. As expected, no discrepancies were observed in concordance rates, discordant loci, or quality scores, since the interface executes the same commands as the original HipSTR tool. HipSTR-UI combines the robustness of HipSTR with a user-friendly and multilingual interface, bridging the gap between advanced sequencing technologies and routine forensic applications. Additionally, it facilitates adoption in routine forensic workflows, including human identification and kinship analysis.