SoftwareX

Although canonical protein design has benefited from machine learning methods trained on databases of protein sequences and structures, synthetic heteropolymer design still relies heavily on physics-based methods. The Rosetta software, which provides diverse physics-based methods for designing sequences, exploring conformations, docking molecules, and performing analysis, has proven invaluable to this field. Nevertheless, Rosettas aging architecture, monolithic structure, non-open source code, and steep development learning curve are beginning to hinder new methods development. Here, we introduce the Masala software suite, a free, open-source set of C++ libraries intended to extend Rosetta and other software, and ultimately to be a successor to Rosetta. Masala is structured for modern computing hardware, and its build system automates the creation of application programming interface (API) layers, permitting Masalas use as an extension library for existing software, including Rosetta. Masala features modular architecture in which it is easy for novice developers to add new plugin modules, which can be independently compiled and loaded at runtime, extending functionality of software linking Masala without source code alteration. Here, we describe implementation of Masala modules that accelerate protein and synthetic peptide design. We describe the implementation of Masala real-valued local optimizers and cost function network optimizers that can be used as drop-in replacements for Rosettas minimizer and packer when designing heteropolymers. We explore design-centric guidance terms for promoting desirable features, such as hydrogen bond networks, or discouraging undesirable features, such as unsatisfied buried hydrogen bond donors and acceptors, which we have re-implemented far more efficiently in Masala, providing up to two orders of magnitude of speedup in benchmarks. Finally, we discuss development goals for future versions of Masala.

BackgroundMolecular Dynamics (MD) simulation of biomolecules provides important insights into conformational changes and dynamic behavior, revealing critical information about folding and interactions with other molecules. This enables advances in drug discovery and the design of therapeutic interventions. The collection of simulations stored in computers across the world holds immense potential to serve as training data for future Machine Learning models that will transform the prediction of structure, dynamics, drug interactions, and more. A needIdeally, there should exist an open access repository that enables scientists to submit and store their MD simulations of proteins and protein-drug interactions, and to find, retrieve, analyze, and visualize simulations produced by others. However, despite the ubiquity of MD simulation in structural biology, no such repository exists; as a result, simulations are instead stored in scattered locations without uniform metadata or access protocols. A solutionHere, we introduce MDRepo, a robust infrastructure that supports a relatively simple process for standardized community contribution of simulations, activates common downstream analyses on stored data, and enables search, retrieval, and visualization of contributed data. MDRepo is built on top of the open-source CyVerse research cyberinfrastructure, and is capable of storing petabytes of simulations, while providing high bandwidth upload and download capabilities and laying a foundation for cloud-based access to its stored data.

Real-time tracking and automated response systems are essential for standardising experiments, reducing observer bias, and improving reproducibility in studies of movement and behaviour. However, existing solutions face significant challenges: AI-based tracking systems require expensive hardware and impose computational delays, creating challenges for closed-loop experiments; existing real-time tracking tools lack standardised implementations for response delivery; and steep learning curves limit accessibility for users without programming or computer vision expertise. Here, we introduce TracktorLive, an open-source Python package designed to overcome these challenges through concurrency and a modular, cassette-based architecture. TracktorLive leverages traditional computer vision techniques to perform image-based object detection without the need for expensive hardware or deep learning. By parallelizing object tracking and response delivery into separate, concurrent server and client processes, the software minimizes frame processing time, enabling rapid, real-time analysis and response delivery. User-friendly cassettes--portable code snippets that can be copy-pasted into scripts--enable users with minimal programming experience to implement complex workflows for use in experiments and practical applications. We demonstrate TracktorLives utility through several applications, including microcontroller-based stimulus delivery for location-dependent manipulations; conditional video recording that activates only during events of interest; kinematic-based response triggering using real-time velocity computations; and multi-cassette experimental designs combining multiple functionalities. Detailed tutorials are provided to familiarize users with TracktorLives operation and functionality, and a growing library of cassettes supports diverse applications out of the box. We validated the software by comparing its response timing to human experimenters in a stimulus delivery task involving two fish species, where TracktorLive demonstrated consistently higher accuracy and lower variability, particularly for fast-moving subjects. Beyond experimental biology, TracktorLives unique architecture and versatility could support many different applications in fields ranging from neuroscience to wildlife management. As an open-source software combining accessibility, modularity, and computational efficiency, TracktorLive can help democratize real-time tracking and automated response systems across disciplines.

Metabolomics offers a sophisticated analytical framework for characterising the molecular phenotype of biological organisms and complex living systems at a high resolution. As the functional endpoint of the omics cascade, the metabolome serves as a close reflection of cellular activity. It integrates genetic, transcriptomic and proteomic variations with external environmental influences. However, the inherent complexity of metabolomic datasets, characterised by high-dimensional chemical diversity, wide dynamic ranges, and significant matrix effects, necessitates a rigorous suite of chemometric and bioinformatic workflows. For researchers uninitiated in computational biology, the multi-stage requirement for raw data pre-processing, signal deconvolution, and multivariate statistical modelling (such as PCA or PLS-DA) presents a substantial barrier to entry. Navigating these convoluted data architectures remains a primary challenge in deriving biological meaning from the global metabolic profile. Here, we present a workflow to use Python Dash Apps to create a user-friendly interface for simplifying data processing and statistical calculations. Users can select their desired samples to initiate calculations for various statistical tests, generating interactive and publication-quality figures to explore their results. These apps were deployed on an Apache server via cPanel, allowing individuals to share their findings with collaborators and for research facilities to share metabolomics results with their users.

BackgroundThe need of digital tools for integrative analysis is today important in most scientific areas. It leads to several community-driven initiatives to standardize the sharing of data and computational workflows. However, there exists no open agnostic framework to model and implement computation workflows, in particular in bioinformatics. It is therefore difficult for data scientists to share transparently and integrate heterogeneous analysis processes coming from different scientific domains, programing languages, projects or teams. ResultsWe present here BioTracs, a transversal framework for computational workflow standardization and traceability. It is based on PRISM architecture (Process Resource Interfacing SysteM), an agnostic open architecture we introduce here to standardize the way processes and resources can be modelled and interfaced in computational workflows to ensure traceability, reproducibility and facilitate sharing. BioTracs is today implemented in MATLAB and available under open source license on GitHub. Several BioTracs-derived applications are also available online. They were successfully applied to large-scale metabolomics and clinical studies and demonstrated flexibility and robustness. ConclusionsAs an implementation of the PRISM architecture, BioTracs paved the way to an open framework in which bioinformatics could specify ad model workflows. PRISM architecture is designed to provide scalability and transparency from the code to the project level we less efforts. It could also be implemented using open object-oriented languages such as Python, C++ or java. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/951624v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@3775deorg.highwire.dtl.DTLVardef@11b5672org.highwire.dtl.DTLVardef@1c87ff7org.highwire.dtl.DTLVardef@495f2d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Automation in microscopy is the key to success in long and complex experiments. Most microscopy manufacturers provide Application Programming Interfaces (API) to enable communication between a user-defined program and the hardware. Although APIs effectively allow the development of complex routines involving hardware control, the developers need to build the applications from basic commands. Here we present a Software Development Kit (SDK) for easy control of Focussed Ion Beam Scanning Electron Microscopes (FIB/SEM) microscopes. The SDK, which we named OpenFIBSEM consists of a suite of building blocks for easy control that simplify the development of complex automated workflows.

Horus is a versatile, multi-platform reproducible-workflow management system designed for biomolecular modelling and computational life sciences. Available as both a desktop application and a web-based service, Horus offers researchers an intuitive graphical interface alongside a robust Python API, democratising the creation, execution, and management of complex scientific workflows. Its modular architecture uses individual blocks that can be easily customised and extended, allowing for an integration with diverse computational tools and resources. Horus supports remote execution via SSH to efficiently dispatch tasks to high-performance computing clusters in remote machines. The platform also features integrated 3D and 2D molecular visualizers for interactive structure analysis, which enhances the interpretability of simulation results. Its design emphasises reproducibility, scalability, and user-friendliness, positioning Horus as a valuable tool to advance research in structural bioinformatics and related fields.

O_LIBayesian modeling is a powerful paradigm in modern statistics and machine learning. However, practitioners face significant obstacles in building bespoke models. C_LIO_LIThe landscape of Bayesian software is fragmented across programming languages and abstraction levels. Newcomers often gravitate towards high-level interfaces, like R, in order to use simple generalized linear models (GLMs) through interfaces like brms. C_LIO_LIFor niche problems, researchers must often transition to writing directly in lower-level programming languages, like Stan or JAX, which require specialist knowledge. C_LIO_LIFurthermore, computational demands remain a significant bottleneck, often limiting the feasibility of applying Bayesian methods on large datasets and complex, high-dimensional models. C_LIO_LIThe Bayesian Inference (BI) is a cross-platform software distributed as a Python, R and Julia library. It provides an intuitive model-building syntax with the flexibility of low-level abstraction coding, while also providing pre-built GLM functions. Further, by facilitating hardware-accelerated GPU computation under-the-hood, BI permits high-dimensional models to be fit in a fraction of the time of comparable Stan models (up to 200-fold). C_LI

Modern biomedical image analyses workflows contain multiple computational processing tasks giving rise to problems in reproducibility. In addition, image datasets can span both spatial and temporal dimensions, with additional channels for fluorescence and other data, resulting in datasets that are too large to be processed locally on a laptop. For omics analyses, software containers have been shown to enhance reproducibility, facilitate installation and provide access to scalable computational resources on the cloud. However, most image analyses contain steps that are graphical and interactive, features that are not supported by most omics execution engines. We present the containerized and cloud-enabled Biodepot-workflow-builder platform that supports graphics from software containers and has been extended for image analyses. We demonstrate the potential of our modular approach with multi-step workflows that incorporate the popular and open-source Fiji suite for image processing. One of our examples integrates fully interactive Fiji macros with Jupyter notebooks. Our second example illustrates how the complicated cloud setup of an computationally intensive process such as stitching 3D digital pathology datasets using BigStitcher can be automated and simplified. In both examples, users can leverage a form-based graphical interface to execute multi-step workflows with a single click, using the provided sample data and preset input parameters. Alternatively, users can interactively modify the image processing steps in the workflow, apply the workflows to their own data, change the input parameters and macros. By providing interactive graphics support to software containers, our modular platform supports reproducible image analysis workflows, simplified access to cloud resources for analysis of large datasets, and integrated workflows across imaging, genomics and transcriptomics data.

SummaryBio-Aurac is an open-source web browser plugin designed to support the research community in drug discovery and life sciences. The tool has been designed to help researchers, scientists, and curators to better explore, navigate and understand content from literature accessing valuable integrated information from third party resources. It identifies, highlights, and provides additionally knowledge for names of biochemical entities like genes and proteins. Availability and ImplementationBio-Aurac has been implemented using a microservice architecture which is open-source and abstracted from non-technical users by utilizing Docker containers (Nust, 2020). It can be run with minimal prerequisites on both Chrome and Firefox browsers. The code for installing and running the web browser plugin can be found here: https://github.com/mdcatapult/bio-aurac (Along with detailed installation instructions). A comprehensive collection of all the code involved in building this open-source project can be found: https://github.com/mdcatapult/aurac-web-plugin, https://github.com/mdcatapult/PDF-Converter, https://github.com/mdcatapult/entity-recognition ContactSoftwareEngineering@md.catapult.org.uk, ian.dunlop@md.catapult.org.uk

MotivationIonic calcium (Ca2+) plays the role of the second messenger in eukaryotic cells associated with cellular functions of regulation of the cell cycle, such as transport, motility, gene expression, and metabolism (Permyakov and Kretsinger, 2009). The use of fluorometric techniques in isolated cells, loaded with Ca2+ sensitive fluorescent probes allows the quantitative measurement of dynamic events that occur in living, functioning cells. The Cardiomyocytes Images Analyzer Application (CardIAP) covers the need for tools to analyze and retrieve information from confocal microscopy images, in a systematic, accurate, and fast way. ResultsHere we present the CardIAP web app, an automated method for the identification of spatio-temporal patterns in a calcium fluorescence imaging sequence. Through this tool, users can analyze single or multiple Ca2+ transients from confocal line-scan images and obtain quantitative information on the dynamic response of the stimulated myocyte. Our web application also allows the user the extraction of data on calcium dynamics in downloadable tables and plots, simplifying the calculation of the alternation and discordance indices and their classification. CardIAP could assist in studying the underlying mechanisms of anomalous calcium release phenomena. Availability and implementationCardIAP is an open-source app, entirely developed in Python, which can be freely accessed and used at http://cardiap.herokuapp.com/.

Deep learning has greatly accelerated research in biological image analysis yet it often requires programming skills and specialized tool installation. Here we present Piximi, a modern, no-programming image analysis tool leveraging deep learning. Implemented as a web application at Piximi.app, Piximi requires no installation and can be accessed by any modern web browser. Its client-only architecture preserves the security of researcher data by running all computation locally. Piximi offers four core modules: a deep learning classifier, an image annotator, measurement modules, and pre-trained deep learning segmentation modules. Piximi is interoperable with existing tools and workflows by supporting import and export of common data and model formats. The intuitive researcher interface and easy access to Piximi allows biological researchers to obtain insights into images within just a few minutes. Piximi aims to bring deep learning-powered image analysis to a broader community by eliminating barriers to entry.

Flow cytometry is widely used for high-throughput single-cell analysis. However, its data analysis relies on either costly commercial software or programming-intensive open-source tools. To bridge this gap, we developed FlowWeb, a freely accessible, web-based platform that combines the flexibility of the R/Bioconductor ecosystem with an intuitive graphical user interface. FlowWeb enables integrated workflows for data handling, quality control, gating, visualization and statistical analysis within a unified environment. FlowWeb integrates raw data, metadata, and analytical state within synchronized Bioconductor structures, enabling coherent analysis and visualization workflows. FlowWeb supports both manual and automated data-driven gating workflows. To evaluate its performance, we applied FlowWeb to an in-house flow cytometry dataset and compared its automated cell cycle and gating workflows to established commercial tools. FlowWebs automated cell cycle workflow produced consistent and reproducible results across replicates and demonstrated high concordance with reference analyses, highlighting the platforms robustness. FlowWebs advanced visualization tools include a wide range of fully customizable individual, overlay, and statistical plots. To enhance usability and reproducibility, the FlowWeb platform provides optional user-accounts that allow storage of reusable configurations, including quality control presets, gating definitions, and plot templates. By lowering technical barriers without compromising analytical rigor, FlowWeb facilitates accessible, reproducible, and scalable flow cytometry data analysis for a broad range of users in research and clinical settings.

Introducing SimpliPyTEM, a Python library and accompanying GUI that simplifies the post-acquisition evaluation of transmission electron microscopy (TEM) images, helping streamline the workflow. After an imaging session, a folder of image and/or video files, typically containing low contrast and large file size 32-bit images, can be quickly processed via SimpliPyTEM into high-quality, high-contrast .jpg images with suitably sized scale-bars. The app can also generate HTML or PDF files containing the processed images for easy viewing and sharing. Additionally, SimpliPyTEM has a specific focus on in situ TEM videos, an emerging field of EM, allowing for fast data processing into preview movies, averages, image series, or motion corrected averages using MotionCor2. The accompanying Python library offers many standard image processing methods, all simplified to a single command, plus a module to analyse particle morphology and population. This latter application is particularly useful for life sciences investigations. User-friendly tutorials and clear documentation are included to help guide users through the processing and analysis. We invite the EM community to contribute to and further develop this open-source package.

Structures of membrane proteins are challenging to determine experimentally and currently represent only about 2% of the structures in the ProteinDataBank. Because of this disparity, methods for modeling membrane proteins are fewer and of lower quality than those for modeling soluble proteins. However, better expression, crystallization, and cryo-EM techniques have prompted a recent increase in experimental structures of membrane proteins, which can act as templates to predict the structure of closely related proteins through homology modeling. Because homology modeling relies on a structural template, it is easier and more accurate than fold recognition methods or de novo modeling, which are used when the sequence similarity between the query sequence and the sequence of related proteins in structural databases is below 25%. In homology modeling, a query sequence is mapped onto the coordinates of a single template and refined. With the increase in available templates, several templates often cover overlapping segments of the query sequence. Multi-template modeling can be used to identify the best template for local segments and join them into a single model. Here we provide a protocol for modeling membrane proteins from multiple templates in the Rosetta software suite. This approach takes advantage of several integrated frameworks, namely RosettaScripts, RosettaCM, and RosettaMP with the membrane scoring function.

Single cell analysis of multi-dimensional microscopy images is repetitive, time consuming, and arduous. Numerous analysis steps are required to quantify and visualize cell heterogeneity and trends between experimental groups. The open-source community has created tools to facilitate this process. To further simplify analysis, we created a library of functions called cell-analysis-tools. This library includes functions that can streamline single-cell analysis for faster quality checking and automation. This library also includes example code with randomly generated data for dimensionality reduction [t-distributed stochastic neighbor embedding (t-SNE), principal component analysis (PCA), Uniform Manifold Approximation and Projection (UMAP)] and machine learning models [random forest, support vector machine (SVM), linear regression] that scientists can swap with their own data to visualize trends. Lastly, this library includes template scripts for feature extraction that can help identify differences between experimental groups and cell heterogeneity within a group. This library can significantly decrease user time while increasing robustness and reproducibility of results.

Quantitative analysis of rodent behaviour is fundamental to neuroscience, preclinical drug discovery and neurotoxicology research. Although several commercial and open-source software packages are available for behavioural assessment, some are expensive, some require programming expertise, and some provide limited flexibility for user-defined experimental configurations. To address these limitations, we developed Robean, a freely available standalone software platform for automated rodent neurobehavioural analysis from both live camera feeds and pre-recorded videos. Robean provides an intuitive graphical user interface that enables users to design experimental arenas, define custom analysis zones, perform spatial calibration, and automatically track rodent movement without requiring programming knowledge. The software currently supports automated analysis of three widely used behavioural paradigms: the Morris Water Maze, Elevated Plus Maze, and Open Field Test. Robean extracts behavioural metrics including escape latency, path efficiency, platform crossings, target quadrant preference, thigmotaxis, locomotor activity, zone occupancy, arm entries, and centre exploration specific to behavioural tests. In addition, the software generates trajectory maps, occupancy heatmaps, comma-separated value (CSV) datasets, comprehensive PDF reports, and batch study summaries for multiple experimental sessions. Developed using open-source software technologies and distributed as a standalone freeware application, Robean provides an accessible and reproducible solution for behavioural neuroscience laboratories. Its modular architecture facilitates future integration of additional behavioural paradigms and analytical modules, making it a flexible platform for automated rodent behavioural assessment.

--Neuroimaging study results can vary significantly depending on the datasets and processing pipelines utilized by researchers to run their analyses, contributing to reproducibility issues. These issues are compounded by the fact that there are a large variety of seemingly equivalent tools and methodologies available to researchers for processing neuroimaging data. Here we present NeuroCI, a novel software framework that allows users to evaluate the variability of their results across multiple pipelines and datasets. NeuroCI makes use of Continuous Integration (CI), a software engineering technique, to facilitate the reproducibility of computational experiments by launching a series of automated tests when code or data is added to a repository. However, unlike regular CI services, our CI-based framework uses distributed computation and storage to meet the large memory and storage requirements of neuroimaging pipelines and datasets. Moreover, the frameworks modular design enables it to continuously ingest pipelines and datasets provided by the user, and to compute and visualize results across the multiple different pipelines and datasets. This allows researchers and practitioners to quantify the variability and reliability of results in their domain across a large range of computational methods.

The goal of this article is to present "The Labbing Project"; a novel neuroimaging data aggregation and preprocessing web application built with Django and VueJS. Neuroimaging data can be complex and time-consuming to work with, especially for researchers with limited programming experience. This web application aims to streamline the process of aggregating and preprocessing neuroimaging data by providing an intuitive, user-friendly interface that allows researchers to upload, organize, and preprocess their data with minimal programming requirements. The application utilizes Django, a popular Python web framework, to create a robust and scalable platform that can handle large volumes of data and accommodate the needs of a diverse user base. This robust infrastructure is complemented by a user-friendly VueJS frontend application, supporting commonplace data querying and extraction tasks. By automating common data processing tasks, this web application aims to save researchers time and resources, enabling them to focus on their research rather than data management.

SummaryMany single-cell transcriptomics datasets are stored in the HDF5-backed AnnData (H5AD) file format, as popularised by the Python scverse ecosystem. However, accessing these datasets from R, allowing users to take advantage of the strengths of each language, can be difficult. anndataR facilitates this access by allowing users to natively read and write H5AD files in R, convert them to and from SingleCellExperiment or Seurat objects, or even work with the resulting R AnnData object directly. We perform rigorous testing to ensure compatibility between Python-written and R-written H5AD files, guaranteeing long-term interoperability between languages. AvailabilityanndataRs source code is available on GitHub at scverse/anndataR under the MIT license. It is compatible with R version 4.5, has been archived at 10.5281/zenodo.18775712 and included within Bioconductor: 10.18129/B9.bioc.anndataR. Installation instructions and tutorials can be found in the online documentation at anndatar.scverse.org. Issues can be reported at the GitHub repository. Code to reproduce the analyses performed can be found on GitHub at LouiseDck/anndataR-paper, archived at 10.5281/zenodo.18792241. Contactrobrecht.cannoodt@gmail.com