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Protein language models (PLMs) capture intricate amino-acid dependencies, producing embeddings that encode rich structural, functional, and evolutionary information. Despite their potential, current extraction workflows rely on arbitrary choices, with respect to embedding layer, pooling, and padding, that frequently yield suboptimal representations for feature extraction and downstream analyses. Large-scale embedding generation is further limited by inefficiencies in computation and memory: (i) accumulating all model outputs in memory before writing to disk causes severe bottlenecks, and (ii) repeatedly embedding identical sequences to extract different modes introduces redundant computation and drastically reduces throughput and scalability. We introduce PEPE (Parallel Extraction for Protein Embeddings), a command-line tool and Python library that enables efficient, high-throughput, and multimodal extraction from protein language models. PEPE's parallelized and streaming-based architecture achieves runtimes several orders of magnitude faster than sequential approaches. Unlike conventional methods-whose peak memory usage scales linearly with output size and fails when memory capacity is exceeded-PEPE maintains stable, low memory consumption, enabling multimodal embedding extraction even beyond available RAM. PEPE supports a wide range of state-of-the-art and custom PLMs through a simple, flexible interface. By combining scalability, robustness, and ease of use, PEPE allows researchers to generate massive, information-rich embedding datasets efficiently, and facilitate the discovery of optimal representations for structural, functional, and evolutionary downstream tasks. By streamlining the generation of diverse embedding configurations, PEPE provides researchers with the necessary data to identify high-performing latent states for specific biological contexts without requiring additional computational resources. PEPE is a command-line tool written in Python and published under MIT license. The source code and documentation are available at https://github.com/csi-greifflab/pepe-cli. PEPE is also available for installation from PyPI under https://pypi.org/project/pepe-cli and deposited on Zenodo at https://zenodo.org/records/20268104.

Imaging Mass Cytometry (IMC) enables highly multiplexed, spatially resolved single-cell proteomics, providing simultaneous measurement of dozens of protein markers while preserving tissue architecture. Despite its analytical power, IMC data analysis remains fragmented across multiple software environments, requiring researchers to combine independent tools for visualization, preprocessing, segmentation, feature extraction, phenotyping, batch correction, and spatial analysis. This fragmentation increases technical barriers, complicates reproducibility, and limits accessibility for non-computational users. We developed OpenIMC, an open-source platform that integrates the major stages of IMC analysis within a unified graphical and command-line framework. OpenIMC supports image visualization, quality control, preprocessing, segmentation, feature extraction, dimensionality reduction, batch effect correction, clustering, phenotyping, and spatial analysis while maintaining interoperability with established community tools. The platform incorporates automated provenance tracking, records analytical parameters and software versions, and enables export and sharing of complete analytical sessions. Benchmarking demonstrated deterministic behavior across repeated runs, complete concordance between graphical and command-line workflows, and strong agreement with established IMC analysis pipelines. OpenIMC additionally provides support for high-resolution IMC workflows, including signal attenuation modeling and image deconvolution. We apply OpenIMC to two datasets of circulating cells and breast tissue to demonstrate the platform's ability to support integrated single-cell and spatial proteomics analysis. OpenIMC reduces the complexity of IMC data analysis by providing a unified, reproducible, and extensible framework for common IMC workflows. By combining interactive visualization with scalable computational analysis, OpenIMC lowers technical barriers and facilitates reproducible single-cell and spatial proteomics research.

Modern bioinformatics workflows, particularly in imaging and representation learning, can generate thousands to tens of thousands of quantitative phenotypes from a single cohort. In such settings, running genome-wide association analyses trait by trait rapidly becomes a computational bottleneck. While established GWAS tools are highly effective for individual traits, they are not optimized for phenotype-rich screening workflows in which the same genotype matrix is reused across a large phenotype panel. We present TorchGWAS, a framework for high-throughput association testing of large phenotype panels through hardware acceleration. The current public release provides stable Python and command-line workflows for linear GWAS and multivariate phenotype screening, supports NumPy, PLINK, and BGEN genotype inputs, aligns phenotype and covariate tables by sample identifier, and performs covariate adjustment internally. In a benchmark with 8.9 million markers and 23,000 samples, fastGWA required approximately 100 second per phenotype on an AMD EPYC 7763 64-core CPU, whereas TorchGWAS completed 2,048 phenotypes in 10 minute and 20,480 phenotypes in 20 minutes on a single NVIDIA A100 GPU, corresponding to an approximately 300- to 1700-fold increase in phenotype throughput. TorchGWAS therefore makes large-scale GWAS screening practical in phenotype-rich settings where thousands of quantitative traits must be evaluated efficiently. TorchGWAS is implemented in Python and distributed as a documented source repository at https://github.com/ZhiGroup/TorchGWAS. The current release provides a command-line interface, packaged source code, tutorials, benchmark scripts, and example workflows.

We present the Pharmacon toolkit, a pure Python command-line software package for analyzing molecular dynamics (MD) simulations of biomacromolecules. Pharmacon supports trajectories generated by widely used MD simulation engines, such as Amber, Gromacs, CHARMM, NAMD, and OpenMM. Pharmacon is written entirely in Python and uses popular third-party libraries, including MDAnalysis for trajectory handling and NumPy for data processing. The toolkit provides a streamlined, automated workflow that simplifies the routine analysis and postprocessing of MD simulation results of biomacromolecular complexes, making them more accessible and reproducible by aggregating many tedious and time-consuming tasks into a single command-line workflow. As a proof of concept, we applied Pharmacon to analyze, in comparison with other toolkits, results from MD simulations, including intermolecular interactions and geometric measures, across different test protein complexes, including three membrane proteins and one soluble protein complex.

Variants in promoters and enhancers can alter the binding of transcription factors (TFs), but their functional assessment remains difficult. FABIAN-variant is a web application that predicts the effects of DNA variants on TF binding by comparing position weight matrix (PWM) and transcription factor flexible model (TFFM) scores between reference and variant alleles. Here, we present FABIAN-variant 2026, a major update that expands the prediction model library from ~5000 to over 40 000 models for >1500 human TFs, sourced from nine PWM databases and including 1290 TFFMs. The application now supports the mouse genome (GRCm38 and GRCm39) with over 35 000 models for >1100 mouse TFs. An optional BPNet deep learning scorer provides neural network-based binding predictions for 240 human TFs. Known TF binding site information has been expanded from three to five sources. Predictions for over 1400 heterodimer TF complexes have been added. The web server has been rewritten in Rust and the scoring engine optimized, reducing runtime by ~70%. A RESTful JSON API and a standalone command-line version enable programmatic access and local high-throughput analysis. FABIAN-variant 2026 is available at https://fabianapp.org/variant26/. The web server is free and open to all users and there is no login requirement.

Modeling cerebral aneurysms using patient-specific geometries demands significant computational resources, particularly when analyzing large datasets or to retrieve training data for machine learning techniques. Smaller domains representing the aneurysm and nearby vasculature are preferred to reduce computational cost, but manual extraction of these regions introduces limitations in time, repeatability, and user-dependent bias. In this work, we present and study the accuracy of AneuSI, an open-source software tool designed to automatically extract a region of interest around the aneurysm from the arterial tree without requiring user intervention. The tool is specifically optimized for the AneuriskWeb database format and its centerline data structure, due to its extensive case collection and high quality, curated data. While its modular structure enables future adaptation to other datasets, the current implementation and validation focus on this specific format. AneuSI is developed in C++ and based on the open-source Visualization Toolkit library. It operates as a command-line tool for Linux, supporting automation through bash scripting. Isolation length is defined as the multiple of a clip factor k and the vessel's inner diameter, obtaining results relative to each aneurysm dimensions. Clip quality was evaluated by comparing AneuSI's output against a trained operator's results for 10 patient-specific lateral aneurysm geometries. The tool was further challenged by processing 102 aneurysm cases from 99 patients across 7 different k values. AneuSI achieved results comparable to manual isolation in a fraction of the time, averaging 2 s per case in a single processor desktop PC, compared to the 10-15 min needed for manual processing. Applied to the AneuriskWeb database, AneuSI demonstrated a 100% success rate, obtaining 714 isolated models through a total of 2592 cuts. AneuSI provides a fast, robust, and reproducible solution for automatic aneurysm-centered region extraction, reducing variability while drastically improving efficiency. Its modular architecture enables straightforward integration into existing tools or libraries, and its compatibility with batch processing make it suitable for large-scale biomechanical studies and machine learning dataset generation in cerebrovascular modeling. Certain geometric heuristics were prioritized for computational efficiency; robustness on heterogeneous datasets or alternative centerline formats requires further adaptation.

The protein design field is rapidly advancing, with frequent emergence of new models and pipelines for designing de novo proteins with tailored properties and functions not found in nature. However, the current tool landscape is fragmented, tools are hard to install and deploy, and require significant computational expertise to integrate into end-to-end, scalable pipelines. A particular challenge is managing many sequences, structures, and metrics for downstream testing and retrospective analysis of input parameters. To address this need, we introduce Ovo, an open-source de novo protein design ecosystem that consolidates models, workflows, data management, and interactive visualization into a scalable, infrastructure-agnostic platform. Ovo features Nextflow-based workflow orchestration, a storage layer, and both command-line and graphical interfaces that democratize scaffold design, binder design and diversification, and validation workflows. Ovo's novel ProteinQC module computes comprehensive sequence and structure descriptors, contextualizing designs against reference sets. Ovo plugins let the community add new workflows and user interfaces to accelerate adoption of emerging methods and facilitate community-driven benchmarking. Ovo lowers engineering barriers and demystifies the design process, allowing experts and non-technical users to design proteins at scale. With community-driven development, Ovo can accelerate de novo protein design and advance discovery in therapeutics and biotechnology.

Structure-based virtual screening (VS) via molecular docking is a pivotal approach for hit identification. Many artificial intelligence (AI)-powered protein-ligand docking and scoring methods have demonstrated impressive speed and accuracy. Retrospective benchmarking studies using enrichment rate and computational efficiency on curated datasets have corroborated their potential for discovering bioactive compounds. However, determining which method suits a specific application and implementing it efficiently remains challenging. Here we present the Comprehensive VS Platform with AI Engine (CVSP-AIE) for drug discovery from compound libraries. It integrates three AI models: KarmaDock, a fast docking model that directly updates atomic coordinates; CarsiDock, an accurate docking model that predicts protein-ligand distances and reconstructs binding poses; and RTMScore, an accurate scoring model that learns residue-atom distance distributions for affinity prediction. Their hierarchical application enables dynamical balances in screening speed and accuracy. CVSP-AIE is available as an online web server ( https://cadd.zju.edu.cn/cvsp/ ) and a local software package. Users can efficiently initiate drug screening by uploading a protein and a known binder that defines the binding pocket. The following workflow involves (1) preprocessing, including protein structure repair and molecule standardization, (2) binding pose and affinity prediction powered by KarmaDock, CarsiDock and RTMScore and (3) postprocessing, comprising protein-ligand interaction calculation and visualization. It takes 30-45 min to hierarchically screen 100,000 compounds, and the output is a ranked list of molecules with predicted binding scores, intermolecular interaction profiles and interactive chemical space analysis. Users can also install locally the hierarchical screening module through command-line package for arbitrary-scale screening.

The rational design of biomolecule immobilization strategies requires molecular-level understanding of how surface properties, tethering geometry, and structural dynamics jointly influence stability and function. Recently, coarse-grained molecular dynamics simulations based on the Martini force field have emerged as an efficient framework for studying enzyme-surface interactions. However, the reproducible construction of immobilized systems with controlled orientations remains technically challenging, usually involving multiple computational tools. Here we present MartiniSurf, an open-source command-line package for the preparation of protein and DNA systems immobilized on solid supports within the Martini paradigm. MartiniSurf integrates automated structure retrieval and cleaning, coarse graining via tools from the Martini force field software ecosystem, customizable surface generation, and biomolecule orientation based on user-defined anchoring residues, producing complete GROMACS-ready simulation systems. The package supports both implicit restraint-based anchoring and explicit linker-mediated immobilization, including surfaces functionalized with user-defined ligands or linker-like moieties, enabling representation of mono- and multivalent attachment geometries at different modeling resolutions. Structure-based Go̅Martini potentials can be incorporated for proteins, while DNA systems are modeled using Martini 2. Optional substrate insertion, pre-coarse-grained complex handling, and automated solvation and ionization further extend system flexibility. By integrating these components into a unified workflow, MartiniSurf enables reproducible preparation and in silico exploration of surface-tethered biomolecular systems.

This article describes a longitudinal dataset of high-interaction SSH honeypot logs collected over a four-month period from July to November 2025. The data was captured using a custom modular multi-threaded honeypot developed in Python 3.10, utilizing the Paramiko library to simulate an interactive SSHv2 environment. Unlike low-interaction systems, this dataset records the full spectrum of adversary activity, including initial connection attempts, authentication brute-forcing, and post-authentication command execution. The resulting dataset contains 145,425 security events, structured to support session reconstruction via unique identifiers [1]. The telemetry includes source IPv4 addresses, credential pairs, and granular command-line payloads, featuring rare instances of file-less exploitation via bash sockets. This data provides a ground-truth source for training machine learning models for anomaly detection, enhancing SIEM rule-sets, and conducting empirical research into botnet orchestration and manual intrusion tactics in cloud infrastructures.

Population genomic workflows frequently rely on fragmented command-line utilities, custom conversion scripts, and programming language-specific environments, complicating computational reproducibility and obscuring data provenance. As analytical workflows become increasingly automated and computationally intensive, dependence on disparate preprocessing tools can introduce friction between raw genotype files, quality-control decisions, statistical analyses, and downstream workflows. We developed SNPio, a Python-native framework that consolidates single nucleotide polymorphism data parsing, filtering, visualization, numerical genotype encoding, and population genomic summary-statistic calculation within a unified software architecture. VCF file parsing and filtering benchmarks were compared against vcfR and SNPfiltR. SNPio demonstrated faster execution times but used more memory than its R-based comparators, reflecting SNPio's retention of genotype arrays, metadata, and provenance-tracking attributes. Pairwise Weir and Cockerham's FST and Nei's genetic distance estimates aligned with HierFstat expectations based on Pearson correlations and aggregate error metrics. D-statistics conformed to theoretical expectations across eleven simulated datasets spanning a range of introgression signal strengths. SNPio provides a reproducible Python-native workflow for processing, filtering, encoding, visualizing, and analyzing SNP datasets. It integrates common early-stage population genomic operations into a transparent, scriptable framework, which ultimately promotes workflow provenance and reduces reliance on disjointed software tools, unsaved terminal commands, and custom scripts. SNPio is particularly suited for population genomic studies of non-model organisms in ecological, evolutionary, and conservation contexts, where reproducible preprocessing and interoperability with downstream analyses are becoming increasingly important.

The analysis of large-scale next-generation sequencing (NGS) data requires substantial computational power, often necessitating the use of high-performance computing (HPC) environments. However, the command-line interfaces for these resources create a significant barrier for many researchers. To bridge this gap, we developed AgrOmicSo (Agri-bio Omics Solution), a software solution designed as a user-friendly interface to a powerful server-side analysis engine. AgrOmicSo's client-server architecture allows researchers to manage and execute complex, large-scale NGS data analysis pipelines on a remote server directly from an intuitive graphical user interface on their local computer. The software integrates a comprehensive suite of bioinformatics tools for quality control, read mapping, variant calling, and annotation. Notably, it supports three distinct variant calling algorithms-GATK, DeepVariant, and VarScan-offering users flexibility for their specific research needs. AgrOmicSo provides both a "One-Step" mode for rapid, automated batch processing and a "Step-by-Step" mode for detailed, customized analyses. This paper describes the architecture, implementation, and utility of AgrOmicSo as an interface for large-scale genomic analysis, highlighting its potential to advance research by making powerful computational resources more accessible, efficient, and reproducible for a broader scientific community. The client and server program of AgrOmicSo are freely available at https://agromicso.com.

RNA-seq data analysis is a multi-step process that typically includes demultiplexing, alignment to the reference genome, read counting and genome annotation, ultimately leading to differential expression analysis (DEA). Several pipelines are currently available to support these steps and produce outputs adapted to specific experimental needs. Such pipelines must also ensure reproducibility, offer user-friendly implementation, support the analysis of complex genomes, allow batch correction, and remain sufficiently flexible to enable customization. Here , we present version 2.2 of a fully customizable command-line RNA-seq pipeline, developed within the Compi framework through the conversion and extension of the previously established DEWE graphical user interface tool. This updated version includes tasks for FASTQ quality control, two alternative read-counting strategies, and recently developed tools for DEA and pathway analysis, such as DElite and RCPA. Furthermore, we apply the pipeline to an exploratory RNA-seq study comparing transcriptomic profiles of Alzheimer's disease (AD) patients with healthy controls (HC) to identify putative disease molecular signatures and assess the pathways affected by the disease.

Stress inference offers a rapid computational approach for estimating the mechanical state of a tissue without requiring invasive experiments. Here, we present a protocol for inferring mechanical stresses in tissues using ForSys, an open-source Python tool. We describe steps for preparing the Python environment, using the command-line interface, installing and using the graphical user interface (GUI), and importing data. We then detail procedures for correcting automatic connections between time points, determining scale parameters, and plotting and quantifying the inference results. For complete details on the use and execution of this protocol, please refer to Borges et al.1.

Collagen fibrils are the primary load-bearing units of connective tissues. However, generating atomistic, simulation-ready models remains challenging due to collagen's hierarchical organization and the diversity of its crosslinking network across tissues, ages, and metabolic states. Notably, non-enzymatic advanced glycation end-product (AGE) crosslinks-central to aging and diabetic complications-are largely absent from current atomistic fibril modelling workflows. Here, we present an extension of the ColBuilder framework to generate atomistic collagen fibril models that incorporate three representative AGE-derived crosslinks (glucosepane, pentosidine, and MOLD) alongside enzymatic crosslinks. Amber99-compatible parameters are provided and assessed against QM-optimized reference geometries using all-atom molecular dynamics (MD) simulations. As proof-of-concept, we examine the mechanical response of single D-period collagen microfibrils featuring enzymatic-only, AGE-only, and mixed crosslink patterns in molecular dynamics simulations under force, and observe that AGE crosslinks differently impact the fibril structure compared to enzymatic crosslinks. The extension to ColBuilder can aid future structure-based research on collagen aging. ColBuilder is available as an open-source Python command-line package at https://github.com/graeter-group/colbuilder.

Influenza viruses remain a significant global health threat due to their high mutation rates and extensive subtype diversity. Genetic variability promotes the mixing of viral subpopulations and facilitates the emergence of novel, potentially virulent, strains. Seasonal influenza vaccines, typically targeting the A/H1N1pdm09 subtype, A/H3N2 subtype, B/Victoria lineage, and B/Yamagata lineage must be reformulated annually, yet their effectiveness and antiviral resistance are continually challenged by antigenic drift. Notably, the H275Y mutation confers a marked reduction in sensitivity to oseltamivir. Comparative variant analysis provides an effective approach for understanding these genetic variations, which is essential for enhancing vaccine effectiveness and predicting drug resistance. Existing tools enable pairwise comparison of variant call format (VCF) files through command-line interfaces but a major limitation is that they only merge variants, and users must manually group variants across multiple samples. Here, we present ShinyVar, a web-based application developed using the R Shiny framework for comparative variant analysis. The application automatically identifies and visualizes mutations in influenza viruses (IFVs) through interactive comparison of variant profiles across subpopulations. The analysis included the A/Wisconsin/67/2022 (H1N1)pdm09-like virus, A/District of Columbia/27/2023 (H3N2)-like virus, B/Austria/1359417/2021 (B/Victoria lineage)-like virus, and B/Phuket/3073/2013 (B/Yamagata lineage)-like virus as representative subtypes for comparison across the past four years. Variant analysis revealed 181 unique variants in B/Victoria lineage 2025, 101 unique variants in A/H1N1pdm09 2024, 37 unique variants in A/H3N2 subtype 2024, and seven unique variants in B/Yamagata lineage 2025. Structural analysis focused on HA_A/H1N pdm091, NA_A/H1N1 pdm09, and NA_B/Victoria. In HA_A/H1N1pdm09, eight non-synonymous mutations were identified. For downstream structural analysis, p.Thr137Ala or p.Thr120Ala mature numbering led to the loss of a hydrogen bond within the 5J8 antibody binding site, resulting in a slight decrease in binding affinity. In the NA_B/Victoria lineage, eleven non-synonymous mutations marginally disrupted key oseltamivir interaction residues (E276 and R292), resulting in lower binding affinity compared with NA_A/H1N1pdm09. ShinyVar is a fast and efficient tool for variant detection and visualization, making it suitable for monitoring not only IFV evolution but also the evolution of other pathogens. ShinyVar facilitates the identification of novel, shared, and unique variants linked to important traits, including drug resistance and virulence under selective pressure, contributing to improved insights into pathogen evolution and the rational design of vaccines and antiviral drugs.

Semi-quantitative positron emission tomography (PET) analysis, particularly Centiloid and CenTauRz scaling, is essential for Alzheimer's disease (AD) research and diagnosis. However, standard quantification workflows often depend on structural MRI for spatial normalization (SN) or rely on computationally intensive software, limiting clinical accessibility. In this retrospective, multi-center study (3539 patients; 6535 scans; 2005-2025), we compiled data across 7 modalities and 13 tracers to develop and validate the Deep Cascaded Cerebral Calculator (DCCC). This fully automated, PET-only framework employs cascaded CNN-based rigid/affine and VoxelMorph-based elastic registration modules for rapid SN. We benchmarked DCCC against the standard MRI-guided SPM12 pipeline and other PET-only tools using meta region-of-interest (ROI) standard uptake value ratio (SUVr) and correlation analyses. DCCC achieved a mean absolute relative SUVr error of 1.34±0.59% and a voxelwise Pearson correlation of 0.96±0.02, demonstrating robust generalization to unseen tracers and modalities including neuroinflammation and methionine metabolism imaging, with superior consistency compared to conventional template-based PET-only methods. Centiloid and CenTauRz estimates were highly accurate (R²>0.97) with a processing speed of 1.22±0.64 s per image. We further demonstrated DCCC's utility across 3 scenarios: (1) longitudinal tracking, where it identified a distinct low-Centiloid AD subgroup; (2) deep learning preprocessing, yielding classification AUCs comparable to standard methods (P = 0.36); and (3) exploratory clinical support, where DCCC-derived metrics were adopted in 79% Aβ and 61% tau cases and were associated with changes in interpretation and increased agreement with reference labels in a multi-reader survey. Collectively, DCCC provides accurate, PET-only standardization, facilitating harmonized biomarker estimation without MRI and enabling large-scale, tracer-agnostic analyses in AD neuroimaging. A free standalone command-line interface program and a 3D Slicer plugin are provided.

Accurate annotation of secreted and membrane proteins requires detecting N-terminal secretion signals, locating their cleavage sites, and distinguishing secretion-signal classes, while also predicting full transmembrane topology for both alpha-helical and beta-barrel proteins. Current tools typically address either whole-protein topology with a generic signal-peptide category or signal-peptide type classification without integrated topology annotation, leaving end-to-end labels incomplete when both features must be resolved together. Here, we present PRED-TMSdeep, a deep learning method that jointly predicts transmembrane topology and three signal peptide classes: secretory pathway/signal peptidase I (Sec/SPI), secretory pathway/signal peptidase II (Sec/SPII), and twin-arginine translocation/signal peptidase I (Tat/SPI). We introduce a two-step constrained decoding procedure that first detects transmembrane segments and signal peptides and then resolves global orientation and refines boundaries under stricter biological constraints. On redundancy-reduced datasets curated from the Orientation of Proteins in Membranes and the Protein Data Bank of Transmembrane Proteins, PRED-TMSdeep matches leading predictors for segment-level topology while improving signal peptide classification and yielding the highest overall top-1 cleavage-site accuracy. Top-1 cleavage-site accuracy reached 89.2%, compared with 84.7% for TMbed and 86.2% for SignalP 6.0, mainly reflecting strong performance on the predominant Sec/SPI class. The software is available as a web server and a batch command-line tool with pretrained models and reproducible workflows.