journal menu
computer programs
 | JOURNAL OF SYNCHROTRON RADIATION |
accessBSxCuBE-Web – a web application for bioSAXS high-throughput collection and experimental control
aEuropean Molecular Biology Laboratory (EMBL), 71 avenue des Martyrs, CS 9018 38042, Grenoble, France, and bMolecular Crystallography, Imaging and Scattering Group (MXIS), European Synchrotron Radiation Facility (ESRF), 71 avenue des Martyrs, F-38000 Grenoble, France
*Correspondence e-mail: [email protected], [email protected]
The biological small-angle X-ray scattering (bioSAXS) beamline BM29 at the ESRF, operated by the ESRF–EMBL Joint Structural Biology and bioImaging Group (JSBIG), resumed user operation in September 2020 following the ESRF extremely brilliant source (EBS) upgrade. To exploit the high quality of X-ray beam delivered by this new fourth-generation synchrotron source, BM29 underwent significant refurbishment, including source optimization, instrument upgrades, and a complete redesign of the whole experimental control system. Here, we introduce the BioSAXS Customized Beamline Environment (BSxCuBE-Web), a new open-source, web-based platform designed to streamline, automate and enhance bioSAXS data collection. BSxCuBE-Web offers an intuitive and user-friendly interface for expert and non-expert users alike, to easily define and run bioSAXS experiments, as well as to monitor both raw and processed data. Its adaptable architecture facilitates its deployment on beamlines beyond the ESRF. In 2025 alone, BSxCuBE-Web supported more than 97 experiments involving over 256 users at BM29. The interface has been successfully validated using a wide range of biological samples and continues to be actively developed in response to bioSAXS user feedback and evolving experimental requirements. User experience reports have been overwhelmingly positive, highlighting significant improvements in efficiency, accessibility, and reliability during data collection.
Keywords: web technologies; biological small-angle X-ray scattering; experimental control; automation.
1. Introduction
The ESRF–EMBL Joint Structural Biology and bioImaging Group (JSBIG) operates a modern suite of synchrotron-based beamlines, together with other facilities on the Grenoble EPN campus that provide integrated structural biology services (McCarthy et al., 2025
). BM29 is a dedicated biological small angle X-ray scattering (bioSAXS) beamline for the study of biological macromolecules in solution, operational since 2012. The beamline originally began as an endstation on the undulator source ID14-3 before being significantly improved and relocated to a dedicated bending magnet source on BM29 in 2012 (Pernot et al., 2013) as part of the ESRF Phase-1 upgrade program (Mueller-Dieckmann et al., 2015). This experimental setup included a temperature-controlled robotic liquid-handling sample changer (SC) with an integrated sample exposure unit (SEU) (Round et al., 2015) and a high-performance liquid chromatography (HPLC) system for size exclusion chromatography (SEC)-SAXS measurements (Round et al., 2013). A dedicated and user-friendly graphical interface, the BioSAXS Customized Beamline Environment (BSxCuBE) was developed for BM29 (Pernot et al., 2013) and used until shutdown for the ESRF–Extremely Brilliant Source (EBS) upgrade in 2019. The original version of BSxCuBE was built on the ESRF `framework4', a library with model-view-controller (MVC) architecture that uses bricks instead of widgets. It was built with Python and Qt4.
BM29 underwent extensive refurbishment during the ESRF–EBS upgrade (Raimondi et al., 2023) to maximize its scientific potential following completion of this new fourth generation synchrotron source (Tully et al., 2023). This included an in-vacuum PILATUS 2M detector (DECTRIS, Baden, Switzerland), a new sample changer (ARINAX, Moirans, France), an SEU for standard capillary measurements (SEU-2A), and a flexible SEU for specialized sample delivery devices such as microfluidic chips (SEU-2B). The ESRF–EBS shutdown period also provided the opportunity to completely refactor the whole software stack on BM29 using a modern open-source suite of control software. This included installation of the ESRF-developed BeamLine Instrument Support Software (BLISS) (Guijarro et al., 2023) and FreeSAS, a new open-source data analysis software to reduce and analyse SAXS data (Kieffer et al., 2022), an evolution of the previous version developed using the EDNA framework (Brennich et al., 2016).
Building on the successful deployment of the web-based experimental interface MXCuBE-Web from the MXCuBE collaborative project (Oscarsson et al., 2019) on the macromolecular crystallography (MX) JSBIG beamlines, we adopted a similar strategy and developed a web-based version of BSxCuBE for the refurbished BM29. The primary objective was to develop an intuitive and user-friendly interface for bioSAXS experimental setups on BM29. BSxCuBE-Web controls the sample changer, HPLC, and coordinates experimental protocols. After data collection, it launches automated data analysis routines uploaded to the experimental database dedicated to bioSAXS experiments, ISPyB for bioSAXS (De Maria Antolinos et al., 2015), and to the new ESRF data portal, DRAC (De Maria et al., 2024). BSxCuBE-Web represents a significant improvement over BSxCuBE, providing more data collection options and hardware control interfaces than the previous version. Here, we describe the technology used and how BSxCuBE-Web is currently deployed on BM29.
2. The evolving state of beamline control software for bioSAXS beamlines
The development of BSxCUBE-Web has been guided by software strategies implemented across synchrotron facilities worldwide. Many of these facilities utilize distinct software design philosophies to address the challenges of modern bioSAXS experiments, such as high-throughput screening, SEC-SAXS, and time-resolved experiments. In many cases, the growing complexity of modern experiments has led to a departure from generic control systems towards the development of specialized graphical user interfaces (Table 1).
|
One common approach is the use of facility-wide frameworks, such as the Generic Data Acquisition (GDA) system deployed at Diamond Light Source (Cowieson et al., 2020) or the TANGO-based Sardana suite used at ALBA (https://sardana-controls.org). This approach allows centralized maintenance and a consistent user experience across beamlines. In contrast, other beamlines have opted for entirely bespoke systems, like BECQUEREL deployed at EMBL-Hamburg@PETRA III, which is highly tailored for the specific hardware on the P12 beamline (Hajizadeh et al., 2018). A third approach involves the adaptation of established software from adjacent fields, principally MX. This is exemplified by the development of a customized version, Blu-Ice for SAXS (Classen et al., 2010), for the SIBYLS beamline at the Advanced Light Source, which was based on Blu-Ice for MX experimental control at Stanford Synchrotron Radiation Source (McPhillips et al., 2002) that is still under active development.
Regardless of development origin, an increasingly popular architectural approach has been to develop a modern, high-level application layer that communicates with a facility-wide standardized hardware control system. This layered approach combines centralized control and maintenance of the core functionality while allowing the flexibility to develop technique-specific applications. This trend is visible at facilities like the National Synchrotron Light Source II, which uses Bluesky (Allan et al., 2019), a high-level Python environment that other synchrotrons such as the Advanced Photon Source and Australian Synchrotron are now adopting. While many acquisition software solutions can be adapted to a more layered structure schema, our approach was to leverage the already existing dedicated management of core controls at ESRF, allowing beamline scientists and specialist software engineers to focus their efforts on developing user-facing applications.
BSxCuBE-Web follows this layered philosophy. As a foundation, it uses ESRF's facility-wide Python-based control framework BLISS (Guijarro et al., 2023) to handle real-time hardware communication, while providing a highly optimized, user-focused web interface specifically designed to remove the complexity of modern bioSAXS experiments. This architecture allows BSxCuBE-Web developers to solely focus on improving the user experiment and scientific productivity, while the complexities of hardware control remain isolated within the BLISS layer.
3. Design of BSxCuBE-Web
The development of BSxCuBE-Web is the result of the close JSBIG collaboration between ESRF and EMBL Grenoble, with both institutes contributing scientific expertise and software engineering resources. This collaborative approach ensures that the platform remains tailored to meet the specific needs of the bioSAXS user community, while also being continuously optimized and expanded by a dedicated team of scientists and developers. By pooling knowledge and resources, BSxCuBE-Web continues to remain at the forefront of automated bioSAXS data collection and analysis, responding to the evolving needs of the European structural biology user community.
BSxCuBE-Web enables the automation of key steps in experimental procedures, such as sample environment control, specification of data acquisition parameters, and queuing of experimental data collections. It also integrates the SEU-2B advanced sample environment controls, enabling precise 2D spatial scanning and microfluidic pump coordination directly from a unified web browser interface. This allows users to focus on their scientific objectives rather than technical operations. The BSxCuBE-Web graphical user interface (GUI) offers a simple and intuitive layout that enables users to easily navigate through the interface and configure experiments, monitor data collection in real time, and quickly visualize results. This reduces the complexity of bioSAXS experiments for non-expert users who may be unfamiliar with BM29. To further help non-expert users, BSxCuBE-Web also provides feedback through log messages.
BSxCuBE-Web integrates a simple-to-use queuing system to allow researchers to schedule and run multiple bioSAXS experiments automatically, enabling efficient and high-throughput data collection. This queuing system minimizes down-time between experiments and maximizes beam time usage, particularly during longer experimental sessions or when processing a large number of samples. Users can conveniently prepare batches of samples, specify the corresponding parameters for each, and then add them to the queue through the BSxCuBE-Web interface. This is especially useful when working with robotic systems such as the BioSAXS Sample Changer or the HPLC system (Round et al., 2015; Tully et al., 2023), which can load, process and measure multiple samples sequentially without manual intervention.
BSxCuBE-Web facilitates the visualization of both raw and processed data immediately after collection (Kieffer et al., 2022). These online feedback tools ensure that users can assess data quality in real time and adjust data collection parameters if necessary to optimize subsequent measurements. In addition to manually adjusting experimental parameters, users have the flexibility to add, remove, or reorder items in the queue while it is running. This dynamic queue management supports real-time adjustments based on immediate data feedback or changing experimental needs. For instance, if preliminary results indicate that further data are needed for a specific sample, the user can reprioritize the queue accordingly.
For more experienced users and beamline staff, BSxCuBE-Web provides extended configuration options concealed in the standard interface. These options, accessible via a dedicated beamline setup menu, include advanced control parameters such as selecting the X-ray energy or configuring beamline-specific options such as slit alignments. As a web-based application, BSxCuBE-Web supports secure remote access, enabling users to control their experiments remotely. This feature is particularly useful for beamline staff who need to troubleshoot technical issues outside regular working hours and can also be used by larger beamline allocation groups, which often need to coordinate multiple experiments and monitor automated data collections remotely during their scheduled beam time. However, a physical user presence at the beamline remains required to load samples, and especially for column equilibration and running of SEC-SAXS experiments.
By balancing usability for non-experts with advanced control options for experienced users, BSxCuBE-Web ensures that a wide range of researchers can conduct bioSAXS experiments effectively, improving both accessibility and overall user productivity. We anticipate that this dual accessibility design will broaden the scientific community's engagement in the use of the BM29 beamline, leading to more diverse applications and innovative discoveries.
4. BSxCuBE-Web technology stack
The development of BSxCuBE-Web leverages a modern and powerful technology stack (Fig. 1) to create an efficient and user-friendly interface for high-throughput bioSAXS data collection and experimental control. The front-end user interface (UI) is built using the React JavaScript library together with Bootstrap 5, ensuring a responsive and consistent design that delivers a seamless user experience. Hypertext markup language (HTML) and cascading style sheets (CSS) underpin the structural and stylistic foundation for the web interface, delivering an intuitive and accessible layout. The front-end was initially implemented in JavaScript (ES2015) and later upgraded to ESNext standards. Since late 2024, new components have been progressively migrated to TypeScript. Currently, approximately 42% of front-end files (35% by line count) are written in TypeScript, following an incremental migration strategy aimed at improving type safety and maintainability. Redux is employed as the state management tool, ensuring that the UI remains synchronized with real-time updates, which is critical for effective experimental monitoring and control.
![[Figure 1]](tv5085fig1thm.gif) | Figure 1 Schematic of the BSxCuBE-Web architecture and technology stack, showing the software components implemented at each layer. |
On the server side, the Representational State Transfer (REST) application programming interface (API) is implemented in Flask, a lightweight Python-based web application framework, providing the bridge between the web interface, the BLISS control system, and the data analysis pipelines. To maintain continuous, low-latency communication, WebSockets are used to deliver real-time experiment status, scan data, and results to the user without manual page refresh.
4.1. Back-end and control system
The BSxCuBE-Web back-end is built on the Daiquiri framework (Fisher et al., 2021), which provides a robust foundation for managing beamline hardware control, experimental data acquisition, and communication between multiple beamline components (Fig. 2). Within the BSxCuBE-Web architecture, Daiquiri functions as the middleware layer, enabling seamless communication between the web-based interface and the BLISS control software. Communication between BSxCuBE-Web and Daiquiri occurs through RESTful APIs using JavaScript Object Notation (JSON) for structured data exchange, while WebSockets enable real-time, bidirectional communication. The extensible architecture of Daiquiri supports the addition of new features and experimental workflows, making it possible to implement components tailored to bioSAXS experiments on BM29. Furthermore, BSxCuBE-Web shares its back-end infrastructure with other ESRF beamline applications, thereby simplifying maintenance and reducing development overhead.
![[Figure 2]](tv5085fig2thm.gif){kind=small} | Figure 2 Schematic representation of the Daiquiri framework within the BSxCuBE-Web architecture. |
The BSxCuBE-Web backend integrates multiple essential components of the beamline through BLISS, enabling control over various hardware devices and management of experimental procedures. BLISS (Guijarro et al., 2023) allows Python packages or scripts to be loaded as external modules, providing control over beamline devices such as the sample stage, detector, sample changer robot, HPLC and SEU. This integration enables core beamline operations such as sample environment regulation, on-line sample purification via HPLC, and automation of complex measurement sequences.
A key aspect of the technology stack is the use of Python as a core programming language for both back-end development and data analysis pipelines. Python, with its extensive scientific libraries and versatility for scientific computing and system integration, ensures seamless communication between BSxCuBE-Web and various beamline components. In addition, Python facilitates integration of hardware components, making it ideal for orchestrating complex beamline operations. It can also be used to script and automate various functions required during data collection.
While BSxCuBE-Web primarily operates through the BLISS control system, the incorporation of the Daiquiri framework enhances adaptability by allowing communication with alternative control systems such as Tango, EPICS or other equivalent control frameworks. Furthermore, the modular nature of Daiquiri makes BSxCuBE-Web a scalable solution for beamline automation, regardless of the underlying control infrastructure. This adaptability ensures straightforward deployment of BSxCuBE-Web on bioSAXS beamlines at other synchrotron facilities.
BSxCuBE-Web implements input validation in both the client and server. On the client side, validation combines HTML5 built-in form checks, React component constraints, and schema-based validation using React JSON Schema Form (RJSF) to prevent invalid ranges, missing fields, and incorrectly formatted inputs before submission. On the server side, the Flask API enforces request and response validation using Marshmallow together with marshmallow-jsonschema, ensuring that all exchanged data conform to predefined schemas. This layered approach helps catch erroneous inputs early, reducing the risk of runtime failures during experiment execution, and improving overall reliability.
Overall, the combination of a React-based front-end, Redux state management, Python for backend control and data processing, and the powerful backend infrastructure based on Daiquiri and BLISS provides a reliable and powerful software solution for bioSAXS data collection and control at BM29. Crucially, this modular design directly translates into real-world benefits. By isolating complex hardware control from the user interface, it prevents UI interruptions from affecting ongoing data collection, maximizing reliability. This separation of concerns also significantly eases software maintenance and collaborative development. Furthermore, because the Daiquiri middleware abstracts the underlying hardware communication, the architecture is inherently adaptable, allowing the interface to be deployed at other synchrotron facilities by connecting to their local control systems. The combined use of modern web technologies and synchrotron-specific software frameworks enables BSxCuBE-Web to meet the demanding requirements of a modern bioSAXS beamline by facilitating high-throughput data collection with minimal user intervention.
5. Graphical user interface
Adhering to the principle of a user-friendly and intuitive interface, the BSxCuBE-Web home screen prominently presents the experiment control for each beamline sample environment configuration (Fig. 3). The `Scan & Custom Collect' button is primarily intended for staff and links to pages for beamline configuration and calibration. Once an experiment type is selected, a new tab opens with a header containing relevant beamline information, such as storage ring beam current and X-ray energy, which are coloured-coded in red, yellow, or green to indicate the beamline status (Fig. 4). The experiment type buttons remain present as a side bar on every experimental page, allowing users to switch between experiment types and monitor the beamline status.
![[Figure 3]](tv5085fig3thm.gif){kind=small} | Figure 3 Home screen of BSXCuBE-Web showing the primary buttons for each experiment type available on BM29. |
![[Figure 4]](tv5085fig4thm.gif) | Figure 4 BSxCuBE-Web GUI for batch data collection using the bioSAXS sample changer. The layout mirrors the sample positioning in the sample changer robot to support intuitive configuration and automated queuing. |
5.1. Data collection tabs and experimental queue
The data collection tabs provide access to high-throughput workflows, making it possible to collect data from hundreds of samples within a single experimental session using an automated experimental queuing mechanism. By integrating different sample environments and robotic systems, such as the bioSAXS sample changer and HPLC system available on BM29, BSXCuBE-Web manages large-scale sample measurements efficiently while maintaining systematic consistency. The queuing system is a core component of BSxCuBE-Web to enable fully unattended, high-throughput data collection. It allows users to prepare, monitor, and manage a sequence of experiments efficiently; specifically, measurements from each sample environment can be queued from the SC and SEC-SAXS, along with equilibration steps and single measurements when required. The SEU-2B can also be run through a queue, but must be scheduled independently because it requires a distinct experimental setting.
5.1.1. Data collection using the ESRF–EMBL bioSAXS sample changer
For high-throughput and efficient sample collection, BM29 utilizes the EMBL-designed bioSAXS sample changer, developed in collaboration with the ESRF (Round et al., 2015) and commercially available from ARINAX (Moirans, France). This bioSAXS sample changer is specifically engineered to handle a large number of biological solution samples with minimal user intervention, facilitating rapid and reliable data acquisition for bioSAXS experiments. The robot operates via a Java based proprietary control GUI software (Round et al., 2015) and includes motor controllers communicating via TwinCAT protocols, all running on a dedicated Windows-based PC. BSxCuBE-Web provides precise control over automated by directly controlling the bioSAXS robot, reducing the risk of contamination and ensuring repeatability of measurements. In addition, it replicates the layout of the three sample holders within the robot, enabling intuitive operation (Fig. 4).
The robotic sample changer can handle a wide variety of sample types, including proteins, nucleic acids, lipids, and other biological macromolecules in aqueous solutions. Under basic bioSAXS operation, each sample must be paired with a corresponding buffer position for background subtraction. This is easily achieved by switching between the `Buffer' and `Sample' tabs and selecting the appropriate position for each sample/buffer in the sample holder, followed by assigning the buffer through a handy drop-down menu. This pairing can then be added to the sample table, which supports different measurement configurations per sample type. Each entry in the sample table can be edited or deleted if required. Once the sample table is configured, it can be added to the `Queue' and executed through the `Run' command to start an automated data collection.
5.1.2. Data collection using HPLC (SEC-SAXS)
SEC is routinely employed to enable online purification and analysis of biological macromolecules prior to SAXS measurements, ensuring that only homogeneous, high-quality samples are analysed. SEC-SAXS is particularly beneficial for proteins and other macromolecules that may degrade or aggregate (Pérez et al., 2022). To facilitate SEC-SAXS experimental measurements, an HPLC system (Shimadzu Corp., Kyoto, Japan) is integrated into BSxCuBE-Web within a dedicated sample environment tab, which includes controls for sample position selection, column equilibration, buffer management and HPLC operation (Fig. 5). This tab provides full access to HPLC functions controlled through the BSxCuBE-Web interface, including pump flow, pump wash, pressure readout, temperature regulation, as well as automated sample loading and injection onto the SEC column. A real-time plot within the interface displays column flow rate and current pressure in the system, allowing users to monitor chromatographic stability during data collection.
![[Figure 5]](tv5085fig5thm.gif) | Figure 5 SEC-SAXS interface within the BSxCuBE-Web GUI showing sample holder and data entry tabs for Sample Collect, Equilibration, and HPLC Control panels, with real-time system diagnostics. |
5.1.3. Experimental queue implementation in BSxCuBE-Web
A dedicated queue button, located in the upper-right corner of the GUI, becomes active when a data collection is added or explicitly opened by the user (Fig. 6). The queue panel provides a clear overview of all scheduled experiments, displaying their order, execution status (e.g. pending, running, completed, failed), and key metadata such as sample ID and data collection parameters. Users can edit, reorder, or remove any queued item prior to execution, enabling rapid adaptation to changing experimental priorities. The queue system is designed for robust operation, maintaining a persistent execution log and providing manual controls to abort individual data collections or terminate the entire queue process. For convenience, users may also import batch definitions directly from sample description files (formatted as either JSON or CSV) to populate the queue. This offline preparation is especially advantageous for time-critical sessions, complex experimental designs, and remote mail-in experiments, as it allows users to fully define their data collection parameters in advance of their beam time. A key feature is graceful error recovery. Depending on the nature of the error, different recovery strategies are applied. Recoverable, sample-specific errors may result in skipping to the next experiment, allowing unattended data collection to continue. In contrast, hardware faults always trigger a full stop of the queue to ensure experimental safety. Transient events such as a temporary beam loss trigger a pause, allowing the experiment to resume once stable beam conditions are restored. These queue errors are automatically logged, providing the beamline staff and users with detailed diagnostic feedback. Together, all these features make the BSxCuBE-Web queue a powerful tool for maximizing beam-time productivity and minimizing manual intervention.
![[Figure 6]](tv5085fig6thm.gif) | Figure 6 BSxCuBE-Web GUI with queue displayed. |
5.1.4. Data collection using the sample exposure unit 2B
The recently integrated SEU-2B further expands the capabilities of BM29, enabling more complex bioSAXS experiments that require controlled positioning and alternative sample formats (Tully et al., 2023; Blanchet et al., 2025). The SEU-2B supports a more specialized sample environment, including different types of customized sample holders such as microfluidic devices, and it is designed for experiments that require precise exposure conditions. One of the key features of the SEU-2B is the ability to perform 2D scans using piezo motor stages to target specific areas of interest within a mounted sample. This level of spatial precision, enabled by the integration of the piezo motor control in BLISS, allows for the analysis of complex sample types such as biological gels or liquid–liquid phase separation (LLPS) condensates. Furthermore, the integration of CETONI Nemesys P (CETONI GmbH, Korbussen, Germany) pump control via BLISS also allows their precise control for studying dynamic biological processes. Together, these developments will allow the collection of time-resolved bioSAXS data from complex biological systems within a confined microfluidic device.
BSxCuBE-Web has been further developed to allow users to define and control SEU-2B scans, specifying the exact points or regions to be scanned and optimizing exposure parameters for each location. This is highly advantageous for researchers who need to examine fine-scale heterogeneities or study reaction kinetics under controlled flow conditions within microfluidic devices. Importantly, these advanced capabilities in BSxCuBE-Web are made accessible through the same intuitive interface, lowering the barrier of use for non-experts (Fig. 7). This combination of advanced sample environment control and user-friendly automation significantly broadens the scope of BM29, facilitating novel studies in structural biology and material sciences where traditional static sample environments would be insufficient.
![[Figure 7]](tv5085fig7thm.gif) | Figure 7 The SEU-2B tab in the BSxCuBE-Web GUI, enabling 2D scan definition and microfluidic coordination for advanced bioSAXS workflows. Different tabs allow control of different experiment types including pump collect, gradient collect, mesh collect, and time-resolved collect. |
5.1.5. Single collect/scan and collect
For smaller-scale experiments or more specialized cases, users can manually inject samples directly into the capillary using either the bioSAXS sample changer or manually with a syringe. This option offers flexibility for unique or highly sensitive samples that may not be compatible with automated workflows or SEC-SAXS setups. These manual data collection modes have been implemented in BSxCuBE-Web to ensure support for a broad range of experimental workflows, including highly customized manual setups that accommodate diverse user needs (Fig. 8).
![[Figure 8]](tv5085fig8thm.gif) | Figure 8 The BSxCuBE-Web GUI provides a real-time scan visualization using h5web integration. |
To streamline such data collection modes, BSxCuBE-Web features real-time scan visualization and diode signal plotting, leveraging the h5web library (Bocciarelli et al., 2022) for interactive and dynamic data representation (Fig. 8). While this implementation provides immediate feedback during experiments, ongoing developments aim to enhance responsiveness and interactivity. Further upgrades will focus on refining the user interface and expanding visualization to better support advanced SAXS measurement workflows.
5.2. Data analysis pipelines
BSxCuBE-Web uses an efficient and automated data analysis pipeline to process SAXS data in real time, allowing users to quickly assess the quality of their results. The pipeline relies on Dahu, an open-source software framework that uses efficient algorithms implemented in FreeSAS (Kieffer et al., 2022), which provides a comprehensive set of tools for SAXS data processing and analysis. The connection between BSxCuBE-Web and Dahu is managed by EWOKS, a workflow execution engine designed at ESRF to build and run modular data processing workflows (De Nolf et al., 2024). EWOKS wrappers ensure that the different steps of the SAXS pipeline are executed reliably and in the correct order, while also providing flexibility for adding or customizing analysis tasks.
After data collection, raw scattering curves undergo automatic processing, including background subtraction, data averaging, and normalization. Advanced data analysis tools, such as Guinier fitting, molecular weight estimation, and pair distribution function [P(r)] calculations, are then applied to extract the structural characteristics of the sample. The combination of EWOKS workflow orchestration and the robust, fast algorithms of FreeSAS ensures that accurate and reproducible SAXS analysis results are obtained quickly. This reduces manual effort and accelerates the path from data collection to meaningful data interpretation, improving overall experimental efficiency.
Essential parts of the processed data can be visualized directly through the BSxCuBE-web interface, giving immediate feedback to users (Figs. 8 and 9). A more detailed inspection of current and previous data collections is available using ISPYB-Exi for bioSAXS (De Maria Antolinos et al., 2015) and via the new ESRF ISPyB-DRAC experimental database (De Maria et al., 2024). These two databases also allow users to download their datasets.
![[Figure 9]](tv5085fig9thm.gif) | Figure 9 BSxCuBE-Web GUI representation of data processing results from FreeSAS (Kieffer et al., 2022 ). |
6. Conclusion and future perspectives
The development and deployment of BSxCuBE-Web represents the culmination of nearly seven years of continual development and optimization. While the MxCuBE international consortium (Oscarsson et al., 2019) provided an initial template, its adaption to bioSAXS beamline control required the resolution of significant technical challenges, spanning from computing infrastructure to application performance. A foundational hurdle was the outdated beamline control environment, which relied on a deprecated operating system (Debian 8) and an obsolete web browser. This legacy setup lacked compatibility with modern web standards and posed significant security risks. This was addressed by spearheading a necessary infrastructure migration to the latest version of Ubuntu and web browser, which provided a stable, secure, and performant foundation for the web-based application. Further upgrades on the infrastructure are foreseen to continue to maintain an optimal user experience and support the evolving needs of high-throughput bioSAXS experiments.
On the application side, a major challenge was efficient browser-side memory management. The continuous rendering of large, real-time data streams in Chrome led to progressive memory leakage and reduced interface responsiveness over prolonged sessions. This issue was mitigated by optimizing the rendering pipeline through lazy-loading components and by adopting the high-performance plotting library, h5web. These changes significantly improved stability and responsiveness during long-running experiments. Synchronization between the GUI, Daiquiri, and BLISS also posed initial challenges. Early versions exhibited latency in status updates, creating inconsistencies between the GUI and the actual beamline state. We resolved this by refining the WebSockets protocol for near real-time push updates and implementing the redundant REST API for critical state polling as a fallback synchronization mechanism.
Retrospective analysis identified opportunities for considering alternative state management approaches as the project evolves. While Redux and WebSockets are a reliable and effective foundation for data synchronization, newer reactive solutions (e.g. TanStack Query or RxJS) offer additional features that could further simplify asynchronous data handling and reduce the amount of custom logic required. These options may therefore be worth evaluating in future iterations of BSxCuBE-Web to continue improving scalability and maintainability. Integrating automated performance profiling earlier in development would likewise have accelerated the identification and resolution of browser-specific bottlenecks. Importantly, addressing these challenges has ultimately enhanced the application's robustness and provided invaluable lessons for future beamline GUI development, emphasizing the need for modern infrastructure, proactive performance optimization, and resilient communication patterns in user experimental measurements at synchrotrons.
In conclusion, BSxCuBE-Web has substantially enhanced experimental efficiency and user accessibility at the ESRF BM29 beamline by providing an intuitive, web-based platform for automated and simplified bioSAXS data collection. Its integration of automation, real-time feedback, modular experimental control and compatibility with various control frameworks, such as BLISS and EPICS, supports both routine and advanced workflows for expert and non-expert users alike.
Looking forward, several enhancements and further automation are anticipated on BM29; notably, the integration of new sample exposure setups, including microfluidic devices, some of which have already been demonstrated but that still require extensive testing to ensure robust functionality. These new experimental capabilities will likely require the addition of new experimental control features to BSxCuBE-Web, such as temperature-controlled cells for more adaptive and advanced technologies. Furthermore, improvements to visualization tools (e.g. Scan Viewer, Result Display) and long-term application resilience are also foreseen, ensuring BSxCuBE-Web will remain performant with a reliable, efficient, and state-of-the-art data collection platform.
Another key area for future development in BSxCuBE-Web is the enhancement of metadata acquisition during bioSAXS experiments. By expanding the scope and precision of the metadata collected, such as experimental parameters, sample characteristics, beamline settings, and environmental conditions, BSxCuBE-Web will support comprehensive and well structured datasets. An enriched metadata will not only improve the traceability and reproducibility of experiments but will also serve as a foundation for implementing machine learning techniques in data processing pipelines, aimed at automated complex data processing tasks, optimized experimental workflows, and extraction of patterns or insights that would otherwise require extensive manual analysis.
Continued collaboration between ESRF and EMBL Grenoble will ensure that BSxCuBE-Web remains at the forefront of bioSAXS beamline control and data acquisition and will pave the way for more advanced, efficient, and automated bioSAXS experiments at synchrotron facilities.
Footnotes
‡Current affiliation: Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Acknowledgements
The authors especially acknowledge the contribution made by the ESRF Macromolecular Crystallography, Imaging, and Scattering Group technicians; ESRF Beamline Control Unit; ESRF Data Analysis Unit; ESRF/ILL PSCM facility staff; EMBL Grenoble Synchrotron Crystallography team; and EMBL Grenoble Robotics and process development for MX and Cryo-EM team. Lastly, we gratefully acknowledge our user community for their support and insightful feedback over the years, which have been invaluable in advancing our developments.
Conflict of interest
The authors declare that there are no conflicts of interest.
Data availability
BSxCuBE-Web is open-source, and its code is freely available at https://gitlab.esrf.fr/ui/bsx3.
Funding information
D. Moussaoui and A. Popov are supported by a fellowship from the career accelerator for research Infrastructure scientists (ARISE) program under HORIZON EUROPE Marie Skłodowska-Curie Actions grant number 945405.
References
Acerbo, A. S., Cook, M. J. & Gillilan, R. E. (2015). J. Synchrotron Rad. 22, 180–186. Web of Science CrossRef CAS IUCr Journals Google Scholar
Allan, D., Caswell, T., Campbell, S. & Rakitin, M. (2019). Synchrotron Radiat. News 32(3), 19–22. CrossRef Google Scholar
Blanchet, C. E., Soloviov, D., Galikeev, E., Sutinen, A., Wieland, F. D. C., Stepanic, O. M., DeBeer, S., Brennich, M., Pernot, P., Tully, M., Cohen-Aberdam, R. & Papp, G. (2025). J. Phys. Conf. Ser. 3010, 012145. CrossRef Google Scholar
Bocciarelli, A., Huder, L. & Vincent, T. (2022). silx-kit/h5web: v4.0.0, https://doi.org/10.5281/zenodo.6458453. Google Scholar
Brennich, M. E., Kieffer, J., Bonamis, G., De Maria Antolinos, A., Hutin, S., Pernot, P. & Round, A. (2016). J. Appl. Cryst. 49, 203–212. Web of Science CrossRef CAS IUCr Journals Google Scholar
Classen, S., Rodic, I., Holton, J., Hura, G. L., Hammel, M. & Tainer, J. A. (2010). J. Synchrotron Rad. 17, 774–781. Web of Science CrossRef CAS IUCr Journals Google Scholar
Cowieson, N. P., Edwards-Gayle, C. J. C., Inoue, K., Khunti, N. S., Doutch, J., Williams, E., Daniels, S., Preece, G., Krumpa, N. A., Sutter, J. P., Tully, M. D., Terrill, N. J. & Rambo, R. P. (2020). J. Synchrotron Rad. 27, 1438–1446. Web of Science CrossRef CAS IUCr Journals Google Scholar
De Maria, A., Bodin, M., Gaonach, M., Gaisné, G., Svensson, O., Bocciarelli, A. & Götz, A. (2024). Synchrotron Radiat News 37(6), 35–42. CrossRef Google Scholar
De Maria Antolinos, A., Pernot, P., Brennich, M. E., Kieffer, J., Bowler, M. W., Delageniere, S., Ohlsson, S., Malbet Monaco, S., Ashton, A., Franke, D., Svergun, D., McSweeney, S., Gordon, E. & Round, A. (2015). Acta Cryst. D71, 76–85. Web of Science CrossRef IUCr Journals Google Scholar
De Nolf, W., Huder, L., Svensson, O., Payno, H., Autran, P.-O., Broche, L., Koumoutsos, G. & Götz, A. (2024). Synchrotron Radiat. News 37(6), 9–15. CrossRef Google Scholar
Fisher, S., Oscarsson, M., De Nolf, W., Cotte, M. & Meyer, J. (2021). J. Synchrotron Rad. 28, 1996–2002. Web of Science CrossRef IUCr Journals Google Scholar
Guijarro, M., Felix, L., De Nolf, W., Meyer, J. & Götz, A. (2023). Synchrotron Radiat. News 36(6), 12–19. Google Scholar
Hajizadeh, N. R., Franke, D. & Svergun, D. I. (2018). J. Synchrotron Rad. 25, 906–914. Web of Science CrossRef CAS IUCr Journals Google Scholar
Kieffer, J., Brennich, M., Florial, J.-B., Oscarsson, M., De Maria Antolinos, A., Tully, M. & Pernot, P. (2022). J. Synchrotron Rad. 29, 1318–1328. Web of Science CrossRef IUCr Journals Google Scholar
Li, N., Li, X., Wang, Y., Liu, G., Zhou, P., Wu, H., Hong, C., Bian, F. & Zhang, R. (2016). J. Appl. Cryst. 49, 1428–1432. Web of Science CrossRef CAS IUCr Journals Google Scholar
McCarthy, A. A., Basu, S., Bernaudat, F., Blakeley, M. P., Bowler, M. W., Carpentier, P., Effantin, G., Engilberge, S., Flot, D., Gabel, F., Gajdos, L., Kamps, J. J. A. G., Kandiah, E., Linares, R., Martel, A., Melnikov, I., Mossou, E., Mueller-Dieckmann, C., Nanao, M., Nurizzo, D., Pernot, P., Popov, A., Royant, A., de Sanctis, D., Schoehn, G., Talon, R., Tully, M. D. & Soler-Lopez, M. (2025). J. Synchrotron Rad. 32, 577–594. Web of Science CrossRef IUCr Journals Google Scholar
McPhillips, T. M., McPhillips, S. E., Chiu, H.-J., Cohen, A. E., Deacon, A. M., Ellis, P. J., Garman, E., Gonzalez, A., Sauter, N. K., Phizackerley, R. P., Soltis, S. M. & Kuhn, P. (2002). J. Synchrotron Rad. 9, 401–406. Web of Science CrossRef CAS IUCr Journals Google Scholar
Mueller-Dieckmann, C., Bowler, M. W., Carpentier, P., Flot, D., McCarthy, A. A., Nanao, M. H., Nurizzo, D., Pernot, P., Popov, A., Round, A., Royant, A., de Sanctis, D., von Stetten, D. & Leonard, G. A. (2015). Eur. Phys. J. Plus 130, 70. Google Scholar
Oscarsson, M., Beteva, A., Flot, D., Gordon, E., Guijarro, M., Leonard, G., McSweeney, S., Monaco, S., Mueller-Dieckmann, C., Nanao, M., Nurizzo, D., Popov, A., von Stetten, D., Svensson, O., Rey-Bakaikoa, V., Chado, I., Chavas, L., Gadea, L., Gourhant, P., Isabet, T., Legrand, P., Savko, M., Sirigu, S., Shepard, W., Thompson, A., Mueller, U., Nan, J., Eguiraun, M., Bolmsten, F., Nardella, A., Milàn-Otero, A., Thunnissen, M., Hellmig, M., Kastner, A., Schmuckermaier, L., Gerlach, M., Feiler, C., Weiss, M. S., Bowler, M. W., Gobbo, A., Papp, G., Sinoir, J., McCarthy, A., Karpics, I., Nikolova, M., Bourenkov, G., Schneider, T., Andreu, J., Cuní, G., Juanhuix, J., Boer, R., Fogh, R., Keller, P., Flensburg, C., Paciorek, W., Vonrhein, C., Bricogne, G. & de Sanctis, D. (2019). J. Synchrotron Rad. 26, 393–405. Web of Science CrossRef IUCr Journals Google Scholar
Pérez, J., Thureau, A. & Vachette, P. (2022). Methods Enzymol. 677, 221–249. PubMed Google Scholar
Pernot, P., Round, A., Barrett, R., De Maria Antolinos, A., Gobbo, A., Gordon, E., Huet, J., Kieffer, J., Lentini, M., Mattenet, M., Morawe, C., Mueller-Dieckmann, C., Ohlsson, S., Schmid, W., Surr, J., Theveneau, P., Zerrad, L. & McSweeney, S. (2013). J. Synchrotron Rad. 20, 660–664. Web of Science CrossRef CAS IUCr Journals Google Scholar
Raimondi, P., Benabderrahmane, C., Berkvens, P., Biasci, J. C., Borowiec, P., Bouteille, J.-F., Brochard, T., Brookes, N. B., Carmignani, N., Carver, L. R., Chaize, J.-M., Chavanne, J., Checchia, S., Chushkin, Y., Cianciosi, F., Di Michiel, M., Dimper, R., D'Elia, A., Einfeld, D., Ewald, F., Farvacque, L., Goirand, L., Hardy, L., Jacob, J., Jolly, L., Krisch, M., Le Bec, G., Leconte, I., Liuzzo, S. M., Maccarrone, C., Marchial, T., Martin, D., Mezouar, M., Nevo, C., Perron, T., Plouviez, E., Reichert, H., Renaud, P., Revol, J.-L., Roche, B., Scheidt, K.-B., Serriere, V., Sette, F., Susini, J., Torino, L., Versteegen, R., White, S. & Zontone, F. (2023). Commun. Phys. 6, 82. Web of Science CrossRef PubMed Google Scholar
Rosenberg, D. J., Hura, G. L. & Hammel, M. (2022). Methods Enzymol. 677, 191–219. CrossRef CAS PubMed Google Scholar
Round, A., Brown, E., Marcellin, R., Kapp, U., Westfall, C. S., Jez, J. M. & Zubieta, C. (2013). Acta Cryst. D69, 2072–2080. Web of Science CrossRef CAS IUCr Journals Google Scholar
Round, A., Felisaz, F., Fodinger, L., Gobbo, A., Huet, J., Villard, C., Blanchet, C. E., Pernot, P., McSweeney, S., Roessle, M., Svergun, D. I. & Cipriani, F. (2015). Acta Cryst. D71, 67–75. Web of Science CrossRef IUCr Journals Google Scholar
Shih, O., Liao, K.-F., Yeh, Y.-Q., Su, C.-J., Wang, C.-A., Chang, J.-W., Wu, W.-R., Liang, C.-C., Lin, C.-Y., Lee, T.-H., Chang, C.-H., Chiang, L.-C., Chang, C.-F., Liu, D.-G., Lee, M.-H., Liu, C.-Y., Hsu, T.-W., Mansel, B., Ho, M.-C., Shu, C.-Y., Lee, F., Yen, E., Lin, T.-C. & Jeng, U. (2022). J. Appl. Cryst. 55, 340–352. Web of Science CrossRef CAS IUCr Journals Google Scholar
Shimizu, N., Mori, T., Nagatani, Y., Ohta, H., Saijo, S., Takagi, H., Takahashi, M., Yatabe, K., Kosuge, T. & Igarashi, N. (2019). Proceedings of the 13th International Conference on Synchrotron Radiation Instrumentation (SRI2018), 11–15 June 2018, Taipei, Taiwan, p. 060041. Google Scholar
Thureau, A., Roblin, P. & Pérez, J. (2021). J. Appl. Cryst. 54, 1698–1710. Web of Science CrossRef CAS IUCr Journals Google Scholar
Tully, M. D., Kieffer, J., Brennich, M. E., Cohen Aberdam, R., Florial, J. B., Hutin, S., Oscarsson, M., Beteva, A., Popov, A., Moussaoui, D., Theveneau, P., Papp, G., Gigmes, J., Cipriani, F., McCarthy, A., Zubieta, C., Mueller-Dieckmann, C., Leonard, G. & Pernot, P. (2023). J. Synchrotron Rad. 30, 258–266. Web of Science CrossRef IUCr Journals Google Scholar
This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.
| JOURNAL OF SYNCHROTRON RADIATION |
