MOTIVATION: Nanobodies are single domain antibodies derived from the unique heavy chain-only immunoglobulins of the camelid family, and have broad applications in biosciences, including as imaging and diagnostic agents. The generation of nanobodies is a multi-stage experimental process for which we have developed AntigenApp a laboratory data management system for nanobody generation and sequence analysis. RESULTS: AntigenApp is a web application and database built to capture data generated during all steps of the nanobody discovery pipeline and to provide built-in analysis capabilities of the experimental results. This includes enzyme-linked immunosorbent assay optical density measurements, sequencing results, nanobody domain labelling, and search/clustering capabilities using protein BLAST. The tool incorporates a standardized naming convention that is automatically applied to nanobodies stored in the database and we envisage the tool being of interest to the wider nanobody community for in-house use. AVAILABILITY AND IMPLEMENTATION: AntigenApp is available from https://github.com/rosalindfranklininstitute/antigen-app under the Apache 2.0 licence and published https://doi.org/10.5281/zenodo.17397055.

MOTIVATION: Computational systems biology analyses typically make use of multiple software and their dependencies, which are often run across heterogeneous compute environments. This can introduce differences in performance and reproducibility. Capturing metadata (e.g. package versions, GPU model) currently requires repetitious code and is difficult to store centrally for analysis. Even where virtual environments and containers are used, updates over time mean that versioning metadata should still be captured within analysis pipelines to guarantee reproducibility. RESULTS: Microbench is a simple and extensible Python package to automate metadata capture to a file or Redis database. Captured metadata can include execution time, software package versions, environment variables, hardware information, Python version and more, with plugins. We present three case studies demonstrating Microbench usage to benchmark code execution and examine environment metadata for reproducibility purposes. AVAILABILITY AND IMPLEMENTATION: Install from the Python Package Index using pip install microbench. Source code is available from https://github.com/alubbock/microbench. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modern analytics has produced wonders, but reproducing and verifying these wonders is difficult. Data provenance helps to solve this problem by collecting information on how data is created and accessed. Although provenance collection techniques have been used successfully on a smaller scale, tracking provenance in large-scale analytics environments is challenging due to the scale of provenance generated and the heterogeneous domains. Without provenance, analysts struggle to keep track of and reproduce their analyses. We demonstrate Ursprung, a provenance collection system specifically targeted at such environments. Ursprung transparently collects the minimal set of system-level provenance required to track the relationships between data and processes. To collect domain specific provenance, Usprung enables users to specify capture rules to curate application-specific logs, intermediate results etc. To reduce storage overhead and accelerate queries, it uses event hierarchies to synthesize raw provenance into compact summaries.

SUMMARY: A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework. For three example models of varying size, we show that for large numbers of simulations PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator. AVAILABILITY AND IMPLEMENTATION: The PySB/cupSODA interface has been integrated into the PySB modeling framework (version 1.4.0), which can be installed from the Python Package Index (PyPI) using a Python package manager such as pip. cupSODA source code and precompiled binaries (Linux, Mac OS/X, Windows) are available at github.com/aresio/cupSODA (requires an Nvidia GPU; developer.nvidia.com/cuda-gpus). Additional information about PySB is available at pysb.org. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.