SoftwareX

Understanding how neural populations support natural behaviour requires knowing what a population does while an animal performs a specific, self-initiated action. For responses evoked by an experimenter-delivered stimulus, the signal can be aligned by the trigger itself, and no behavioural scoring is needed to define the event. Complex behaviours such as parental care and social interaction provide no such trigger. They are defined from the video record, usually offline and post hoc, and are conventionally scored in one program and exported to another for analysis. In this study we developed EthoTrace, a single self-contained HTML file that runs in a desktop browser without installation, server or network access and carries a session from behaviour and calcium signal through to the statistical comparison. Behaviour is annotated on a timeline shared with the {Delta}F/F trace, the signal is corrected and scaled under a stated baseline convention, single bouts stay inspectable in their behavioural context, and repeated bouts are aligned without filling absent data. We demonstrate the complete workflow on a parenting behaviour experiment, in which behaviour was scored and the photometry signal analysed in a single session folder. We expect EthoTrace to facilitate integrative analysis in neuroscience, where behavioural observation and fiber photometry signals are interpreted together.

Magnetic field effects (MFEs) in biological systems are typically small and experimentally challenging to measure reproducibly across large sample populations. Existing approaches to measure such effects often rely on low-throughput microscopy or custom-built magnetic stimulation systems that provide limited control over magnetic field geometry, synchronization, or experimental automation. Here, we present an open-source magnetofluorescence imaging platform designed for bacterial plate-scale screening of MFEs in live colonies. The instrument integrates a programmable three-axis vector electromagnet, synchronized fluorescence excitation and imaging, and integrated acquisition software with per-frame metadata logging on a hardware-synchronized data acquisition card. An extensive calibration procedure enables accurate generation of arbitrary magnetic field vectors, while synchronized triggering ensures deterministic alignment between field application, illumination, and image acquisition. The system images an entire 100 mm Petri dish in a single acquisition. Typical experiments monitor hundreds of bacterial colonies simultaneously over multi-hour acquisition sequences. Control software, calibration routines, mechanical design files, and acquisition workflows are provided openly to facilitate replication. Instrument performance is demonstrated through detection of magnetic field-dependent fluorescence changes in E. coli expressing the engineered magnetosensitive fluorescent protein MagLOV2. This instrument provides a flexible and scalable platform for high-throughput magnetobiology, synthetic biology, and quantum biology experiments.

labapi is a Python library that enables computational workflows to connect to LabArchives' electronic lab notebook (ELN). Without an Application Programming Interface (API) connection, researchers must manually add workflow outputs through the LabArchives web interface, navigating to the appropriate page and uploading each output so that it appears with the experimental notes that provide context. labapi translates the flat LabArchives API into a Python object model following the existing hierarchy of the web interface, allowing workflows to navigate and modify notebook content through familiar paths. labapi enables researchers to build interconnected workflows that both read in and write data to LabArchives' ELN automatically. Researchers can inspect those outputs in the notebook, and later analysis code can read them back for another stage of analysis.

High-speed atomic force microscopy (HS-AFM) experiments provide unique insights into nanoscale biological processes by directly visualizing the surface topography dynamics of biomolecules, such as proteins, over extended periods of time and generating large imaging data sets. However, the lack of computational methods for automated data analysis has thus far limited the full utilization of these data for the quantitative understanding of single-molecule dynamic. Here, we present the AFM Analysis Arena (A3) within BioAFMviewer as a software platform for automated, high-throughput quantitative analysis of AFM topographic imaging data within a user-friendly interactive graphical interface, implementing a convenient workflow from raw HS-AFM (AFM) imaging to statistical analysis. In several applications to HS-AFM imaging data, we demonstrate methods of automatized molecule detection and dynamic tracking to highlight the available capacities, aiming to transform HS-AFM measurements from observations towards an analytical tool in nanoscale biology.

One goal of Scientific Machine Learning (SciML) is to advance traditional scientific computing frameworks with modern machine learning tools. This includes extending established methods, such as the finite element method, with cardiac function applications due to their complexity and need for very rapid execution times for real time clinical use. In this work, we present an advanced form of the Neural Network Finite Element (NNFE) method specialized for cardiac simulations, termed CARDIAX-NNFE. The NNFE method learns the parameter-to-displacement field map by training over the residual of the hyperelastic material PDE, using the domain represented by finite elements. The implementation is developed in Python using JAX to leverage its automatic differentiation, highly parallel GPU, and JIT-compilation capabilities. To demonstrate CARDIAX-NNFE effectiveness, we trained full cardiac pressure-volume responses using a simplified heart model, spanning the entire cardiac physiological functional range. Results indicated the ability to simulate a family of pressure-volume solutions with average nodal positional error of 0.023 mm and maximal error of 0.054 mm, with a single complete PV loop evaluated in 0.002 seconds. The CARDIAX-NNFE software platform thus provides for a robust platform for cardiac functional simulations. Moreover, it provides the structure for residual-based SciML methods, which can apply to a variety of physics-based biomedical problems that require high execution speed for clinical applications.

For ten years, baltic (Backronymed Adaptable Lightweight Tree vIsualization Code) has been used to make annotated phylogeny figures in molecular epidemiology, including during the West African Ebola epidemic, the Zika epidemic in the Americas, and the SARS-CoV-2 pandemic, as well as other fields. At its core, baltic is a Python library used for the efficient parsing, traversal, manipulation, and visualisation of phylogenetic trees. It is a small library with few dependencies that reads tree formats common in phylodynamic analyses and gives the user leverage to interact with a lightweight tree data structure to produce publication-ready figures with matplotlib. We present baltic v1.0, its first formally released and documented version. baltic reads and writes BEAST Nexus, Newick, and Nextstrain/Auspice JSON, and can process large BEAST posterior tree files in parallel to extract user-defined posterior statistics. The same objects are used for tree manipulation and for plotting in a single script. New to this release: a set of rooting methods (midpoint rooting, rerooting on any branch, and root-to-tip regression); support for reticulate evolution, with reassortment and recombination edges; and composite figures that combine a tree with other data, such as Muller plots, skygrid plots, tanglegrams, and plots connecting trees to maps. The release includes a documentation site with an API reference, tutorials, and a matplotlib-style gallery of worked examples.

As multisite and multi-study data aggregation becomes increasingly common for improving statistical power and sample diversity, robust harmonization methods are needed to address biases introduced by batch variation, particularly in neuroimaging research. Although a variety of harmonization approaches are available, the lack of systematic guidance for diagnosing batch effects and selecting appropriate methods remains a major challenge. To address this gap, we introduce ComBatFamQC, a comprehensive R package designed to streamline batch-effect diagnosis, harmonization, and post-harmonization analysis. ComBatFamQC integrates a user-friendly Shiny app for interactive batch-effect diagnostics, state-of-the-art harmonization methods from the ComBat family, including ComBat, longitudinal ComBat, ComBat-GAM, and CovBat, and tools for downstream analysis after harmonization. The package provides qualitative visualizations, statistical tests for batch-effect assessment, and a consistent interface that supports both in-sample and out-of-sample harmonization through the Shiny app, the R console, or the command line. In addition, it includes functions for post-harmonization analyses to facilitate downstream modeling. Its modular design also supports the systematic incorporation of future harmonization methods and expanded downstream analysis capabilities.

BackgroundResearch software is essential for modern data analysis but is often developed and maintained by a small number of researchers. When developers leave, software may become orphaned, limiting reuse and risking the loss of valuable domain knowledge and computational methods. While the FAIR Principles for Research Software (FAIR4RS) provide an essential foundation for improving the reuse of research software, compliance with these principles alone does not guarantee practical reusability. Here, we investigate whether orphaned scientific software can be systematically rescued and transformed into sustainable, reusable workflows using established software engineering practices and community standards. FindingsWe re-engineered the abandoned MATLAB-based NuMorph toolkit for large-scale light-sheet microscopy image analysis into nf-core/lsmquant, a Nextflow-based workflow developed according to nf-core community guidelines. The re-engineered workflow preserved the original scientific methods at comparable computational cost while improving the softwares FAIRness, portability, and reproducibility. Integration into the nf-core ecosystem provides a community-driven framework that supports software sustainability through distributed maintenance and shared development practices, while the modular workflow architecture simplified adaptation of nf-core/lsmquant to additional light-sheet microscopy datasets beyond the original application ConclusionOur work demonstrates that orphaned scientific software can be successfully rescued through systematic re-engineering guided by FAIR and software sustainability principles. By transforming a legacy codebase into a community-maintained workflow, we preserve valuable domain-specific methods while improving usability, maintainability, and reproducibility. This approach provides a practical strategy for recovering orphan research software and integrating it into modern, reusable research ecosystems.

Automated home-cage systems measure individual behavior in social groups for days to months. Among these systems, the IntelliCage has become a widely used platform for longitudinal and socially embedded behavioral phenotyping. Yet the analysis layer often remains less standardized than the experiment itself: raw exports, phase definitions, exclusion rules, time alignment, and derived behavioral metrics are transformed by lab-specific scripts that are difficult to audit, compare, or reuse. We present ic-analysis, an open-source Python toolkit for standardized, scriptable, and shareable IntelliCage workflows. The toolkit separates user-defined experiment metadata and workflow scripts from a reusable analysis core with modular analysis and plotting functions, allowing users to flexibly assemble experiment-specific pipelines without editing package internals. Due to its modular design, the analysis core can be applied to a broad range of experimental paradigms, including general activity, exploratory, motivational, cognitive, and social readouts, rather than being limited to a single fixed protocol. It aligns biological phase windows across staggered cage runs and exports plots together with quantitative result tables, applied settings, and audit files that support reproducible and FAIR reporting. Here, we demonstrate this flexible design in a realistic synthetic place-learning/place-reversal experiment with two mouse groups and deliberately offset cage starts. The workflow recovered the implanted behavioral differences while preserving the required experimental-time alignment. Group A showed stronger endpoint saccharin preference (81.7 +/- 1.7% vs. 27.4 +/- 3.4%, p = 1.9e-), higher liquid uptake, faster place-learning onset (56.6 +/- 8.1 vs. 278.3 +/- 33.2 visits), and better reversal performance (64.1 +/- 1.3% vs. 25.6 +/- 1.7% rewarded correct-corner visits) compared to Group B. This demonstration shows how standardized, explicitly defined readouts can turn complex IntelliCage exports into interpretable behavioral profiles while preserving the analysis history needed for inspection and reuse. ic-analysis therefore provides both a working analysis scaffold and an extensible, community-friendly route toward IntelliCage workflows that are easier to reproduce, compare, extend, and share.

SummaryContemporary proteomics methods can now generate large-scale DIA datasets of thousands of files that demand substantial computational resources for efficient analysis. Spectronaut is a widely used platform for DIA data processing; however, large-scale searches are often constrained by computational performance and long execution times when run on single workstations. Here, we present Spectronaut-nf, a Nextflow-based pipeline that enables scalable and parallelized execution of Spectronaut analyses across high-performance computing (HPC) environments. The workflow divides directDIA analysis into modular stages, including spectral library generation, DIA searching, and merging results, allowing efficient distribution of tasks across multiple compute nodes. Benchmarking using 72 diaPASEF raw files using typical hardware demonstrated that Spectronaut-nf completed searches in 23.77 hours, compared with 39.09 hours on a Windows workstation and 67.04 hours on a single-node Linux HPC setup. Stress testing with 1,037 diaPASEF raw files further demonstrated the scalability and robustness of the workflow for large proteomics datasets. Across platforms, protein and peptide identifications remained consistent, with only minimal variability attributable to platform-specific differences. Overall, Spectronaut-nf provides a flexible, scalable, and efficient framework for high-throughput DIA proteomics analysis in HPC environments. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/741433v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@4a7107org.highwire.dtl.DTLVardef@14287d8org.highwire.dtl.DTLVardef@e484e9org.highwire.dtl.DTLVardef@d21c07_HPS_FORMAT_FIGEXP M_FIG C_FIG

In many bioinformatic data analysis projects, it is convenient to visualize plots and results through an interactive web app or dashboard. These interactive reports can then be shared with customers, collaborators, or the general public. Publishing and sharing these apps is not straightforward, becoming especially cumbersome when the number of projects and customers start growing. Docker containers offer a convenient way to package, distribute, and run interactive web apps, and their use is already widespread in the bioinformatics community. We developed Kiosc to simplify the orchestration of containerized web apps, organize them into projects, and regulate access control. We implemented it as a web server based on the Django framework, with a user- and admin-friendly interface as well as a REST API for programmatic tasks. Users can select Docker containers packaging apps like Plotly Dash, Shiny, or Quarto, and configure them to display the results of their analysis. Kiosc runs the containers with the appropriate network configuration and acts as a proxy to the web services running inside the containers. We have been maintaining a Kiosc instance for more than 5 years, serving 321 containers in 150 projects across multiple institutions. In this article, we introduce the main functionality in Kiosc and describe four use-cases that show how Kiosc can prove helpful to the broader bioinformatics community, such as configuring and running web apps for the interactive visualization of workflow results, and publishing companion apps for scientific articles. Kiosc is a self-hosted platform for publishing web apps, which doesn't require significant expertise in either Docker or network administration to be deployed. It provides a similar service to Kubernetes, but with a convenient web interface and much lower administration overhead.

Summary: Existing workflows for extracting morphological features from network-like biomedical structures often require several heterogeneous tools, which can limit integration and reproducibility. We present MaSkel and napari-MaSkel, open-source Python tools that provide GUI- and CLI-based workflows for 2D and 3D skeletonization and graph-based feature extraction, using a substantially accelerated implementation of the widely used Lee94 thinning algorithm. Availability and Implementation: MaSkel and napari-MaSkel are open-source Python packages available under the MIT license on PyPI and GitHub: https://github.com/bionetslab/maskel/, https://github.com/bionetslab/napari-maskel/. Documentation: https://bionetslab.github.io/maskel/, https://bionetslab.github.io/napari-maskel/. Benchmarking and validation: https://github.com/bionetslab/maskel-evaluations.

Reproducible data analysis and visualization are essential for reliable biological and biomedical research, yet creating high-quality, publication-ready figures often requires moving data and results between multiple analysis, visualization, and graphics tools. This fragmented, multi-layered process can make it difficult to trace how individual figure panels were generated, preserve the underlying analytical decisions, and reproduce them later. Here, we introduce MakeMyFigure, a free and open-source platform for data visualization, analysis, and creation of multi-panel scientific figures. MakeMyFigure integrates data processing, statistical analysis, visualization, and figure assembly within a single workflow. It supports a range of quantitative data formats and structures, including feature-by-sample matrices and precomputed statistical results, and provides data-aware visualization recommendations to help users select appropriate plots from a library of 38 visualization types. These capabilities allow researchers to move directly from experimental measurements to commonly used statistical analyses and graphical representations without requiring programming expertise. Eighteen statistical procedures are implemented, and their results are drawn directly onto the plot families that support statistical annotation, keeping analytical results linked to the panels they generate. Importantly, MakeMyFigure uses machine-readable JSON specifications to record the identity and checksum of the source data, the processing steps, visualization settings, and statistical parameters used to generate each figure panel. It can also save a figure as a portable package, freezing the specifications together with the data. This allows figures to be regenerated from their recorded specifications and source data, or from the package alone on another computer, rather than relying on manually reconstructed workflows. Using published datasets from several biological fields, independent statistical validation in R, and a comparison with 15 representative analysis, visualization, and figure-generation tools, we show that MakeMyFigure combines accessible, code-free figure creation with panel-level computational reproducibility. Overall, MakeMyFigure provides a unified approach for creating, documenting, and reproducing scientific figures. Source code and documentation are available at https://github.com/surPoudel/make-my-figure, https://github.com/surPoudel/make-my-figure/releases/tag/v1.1.0.

SummaryIntegrating spatial multi-omics data requires coordinated preprocessing, cross-modality alignment and feature registration across modalities that differ in file format, coordinate system and spatial resolution. No existing tool addresses this pipeline end-to-end from raw experimental files till aligned data object. We present FOCUS, an open-source Python package that takes raw data from spatial transcriptomics, mass spectrometry imaging, Raman spectroscopy imaging and brightfield or fluorescence microscopy through modality-specific preprocessing, interactive spatial alignment and resolution-matching registration to a unified MuData object, driven by a single configuration file. Its modular, registry-based architecture allows straightforward extension to additional modalities. FOCUS is accessible via a command-line interface, a browser-based GUI and a Python API. Availability and implementationFOCUS is implemented in Python 3.11, with a browser-based GUI built on a Vue.js 3 frontend served by a Flask backend. Source code, documentation and container recipes are available at https://github.com/sifrimlab/FOCUS; a versioned release is archived on Zenodo (10.5281/zenodo.21700038). Outputs use the AnnData and MuData formats and are directly compatible with the scverse ecosystem.

This work presents DSSNA (Define Spatial Structure of Nucleic Acids) v2026, a free and open-source standalone GROMACS module for the automated analysis of nucleic acid structure based on atomic coordinates. DSSNA reproduces the core functionality of the X3DNA and DSSR approach while extending it to the analysis of molecular dynamics trajectories through integration with the GROMACS software package. The algorithm identifies and classifies structural features, including canonical and noncanonical base pairs, hydrogen-bond networks, stacking interactions, helices, and many other elements of nucleic acid secondary and tertiary structure. Its modular output system enables users to select the structural metrics required for a particular analysis, while the use of neighbor-search algorithms and data-processing capabilities facilitates the analysis of large trajectory datasets. The performance and applicability of DSSNA were demonstrated using a molecular dynamics trajectory of a GUAA tetraloop mutant of the sarcin-ricin domain from Escherichia coli 23S ribosomal RNA (PDB ID: 1MSY). The analysis showed that only a subset of the observed base pairs and stacking interactions remains stable throughout the trajectory. Persistent canonical base pairs formed the structural core responsible for maintaining the secondary-structure profile, whereas most noncanonical base pairs and stacking interactions were transient. The results demonstrate that DSSNA can be used to characterize both static nucleic acid structures and the temporal evolution of structural interactions in molecular dynamics simulations. The work also provides a glossary of terms relevant to the analysis and interpretation of nucleic acid structures.

Network-based analyses of molecular interactions are useful for interpreting high-throughput omics data and identifying therapeutic targets. Cytoscape is the standard platform for these tasks, but users face a trade-off between accessible graphical workflows that are difficult to document and reproducible automation in Python or R that requires programming expertise. General-purpose coding assistants can generate Cytoscape Automation scripts, but remain external to Cytoscape. We present CyChat, a Cytoscape Desktop app that integrates a chat interface and a large language model (LLM) agent into the application. CyChat translates natural language into executable Cytoscape Automation workflows, runs generated Python code, and exports chat sessions with executed code as standalone Jupyter notebooks. To reduce setup barriers, CyChat includes an embedded Python runtime and supports both cloud-based and locally hosted LLMs. CyChat was evaluated across ten Cytoscape workflows using seven LLM providers, each represented by one LLM. The strongest configuration achieves a pass rate above 99%. In a qualitative evaluation based on a published network visualization, CyChat completes the task in 1.5-5 minutes, compared with 15-20 minutes for manual GUI workflows by computational biologists. CyChat is available through the Cytoscape App Store at https://apps.cytoscape.org/apps/cychat.

Background: Reproducible taxonomic collapsing and geological-timescale annotation of time-calibrated phylogenetic trees in R often require coordination among several packages and repeated code for label parsing, clade validation, plotting, and export. Workflow-managed analyses additionally benefit from non-interactive configuration, predictable diagnostics, and machine-readable exit status. Results: We present Rclade, an R package that consolidates the multi-package coordination required for taxonomic collapsing into a streamlined, single-function interface. Rclade provides (1) custom ggproto objects (GeomPolygonStraight/GeomSegmentStraight) that bypass coord_munch() interpolation to achieve straight-edge rendering of collapsed triangles in circular layouts; (2) automatic detection and parsing of four taxonomic-label formats (GTDB, Silva, NCBI, embedded) plus user-supplied custom regex, with explicit input-validation contracts and parsing-accuracy evaluation on real and derived test sets; and (3) workflow embeddability through YAML configuration, library-mode APIs, and standard Unix exit codes. Benchmarks on synthetic and real datasets (200-10,000 synthetic tips and real reference trees up to 10,122 tips; 5 replicates at every scale under a unified fully rendered measurement protocol) show that the full-pipeline overhead is modest for interactive use (median {approx}0.87 s in-session rendering and {approx}8.4 s process-level wall-clock at 10,000 tips). Conclusions: Rclade is a convenience layer over the ggtree/deeptime ecosystem that reduces boilerplate while adding targeted technical improvements for circular-layout rendering and format heterogeneity management.

Artificial intelligence methods now span the drug-discovery pipeline --structure prediction, de novo design, docking, affinity estimation, and ADMET -- yet each tool ships with its own, often incompatible, GPU software stack, so chaining several into a workflow requires reproducing conflicting software environments and access to datacenter-class hardware that most labs do not have. bioq is a dependency-light command-line client backed by bioq-services (accontrol-plane gateway plus a growing fleet of 38+ containerized drug-discovery tools spanning 7 discovery stages and 6 molecular modalities). The bioq CLI is self-describing and consistent across the fleet of computational tools, giving researchers and coding agents uniform access to any tool from a laptop, with no local model code, CUDA setup, or cloud configuration, running on serverless GPUs billed per job. The interface-gateway-services architecture makes bioq an execution substrate for automated, agent-driven discovery. bioq and bioq-services are open source under the MIT License and available at https://github.com/wolfsonliu/bioq and https://github.com/wolfsonliu/bioq-services. bioq runs on Python > 3.10 with httpx as its only runtime dependency; services run as Linux containers and are self-hostable via the provided local deployment scripts (alongside scripts for Alibaba Cloud Function Compute). Install, quickstart, test data, and self-hosting instructions are in the repository. The project is actively maintained and will continue to receive updates to both features and services.

Structural model building and refinement need many decisions between different programs. These decisions often depend on an experienced researcher who knows how the programs work together and how to read experimental data. We used Claude Code with Claude Opus 5.5 to supervise this work for 24 cryo-EM and X-ray cases. The work finished in about 36 hours on one workstation. The agent prepared the inputs, ran established programs, read the validation results, compared candidate models and chose the next step. For 15 deposited cryo-EM models with weak starting geometry, the median MolProbity score improved from 2.62 to 1.76. For three crystal structures, the agent models came close to the published Rfree values. Chemical information such as ligands, ions and modifications must be provided by the researcher, in the same way as the sequence. With this information, the agent can place and check these components. These results show that agent supervision is acceptable when every decision can be inspected. The researcher makes the final scientific judgment. We suggest practical requirements for this kind of use. Future AI agents may help researchers obtain high quality and reliable structures of biological macromolecules.

We introduce Bravais Lattice Sampling (BLS), a two-phase method for detecting connected high-density regions in three-dimensional space. BLS places probe sites on a Bravais lattice scaled to the expected nearest-neighbour distance dNN of the target structures, then recovers cluster boundaries by depth-first expansion seeded only from occupied probes, replacing the exhaustive raster scan that conventional connected-component labelling uses to discover seeds. The spacing between probe sites is set from the covering radius of the lattice, which is what allows the method to state in advance the size below which a cluster may escape detection. The second phase, an expansion refinement activated only on probes that return an occupied voxel, verifies every edge, so the components returned are true connected components. BLS versatility allows for selection of different Bravais lattice unit cells to match the target structure; for amorphous, non-crystalline shapes, BLS can default to a simple face-centred cubic unit cell, where the expected minimum cluster size is the only parameter that needs to be set. The current BLS implementation has been developed as a post-processing tool for molecular dynamics trajectories, and was tested for searching water ice clusters of different morphologies. BLS returns component counts and maximum cluster sizes identical to exhaustive-labeller algorithms, with 100% recall; it runs at about 0.94 times the cost of depth-first search, and at 0.84 to 0.90 times the cost of the fastest other labeller in our benchmark set. This algorithm, although implemented by us for molecular dynamics applications, could be of interest in other domain areas where searching for high-density elements in 3D space is relevant.