Journal of Open Source Software

SignificanceComputational modeling and the use of simulation software tools are essential for biomedical optics research. Designing effective simulations often requires in-depth understanding of the underlying physical problems and proper configuration of the software settings, which often constitute key barriers for novice users including students. The rapid emergence of large language models (LLMs) offers new opportunities for natural-language-based interaction, but integrating them with technical software remains challenging because of their limited output reproducibility. Overcoming these limitations would allow more intuitive, efficient, and reproducible interaction between scientists and scientific software. AimWe investigate the use of LLMs in quantitative biophotonics simulation tools, with a goal of enabling novice users to build complex photon simulations using intuitive natural-language-based problem descriptions. ApproachWe have explored prompt engineering strategies that enable LLMs to bridge the gap between natural language descriptions and advanced simulation software by constraining LLM outputs using a data schema (i.e., format) and a modular component architecture, followed by deterministic validation to ensure correctness and reproducibility of the outputs. ResultsUsing Monte Carlo eXtreme (MCX) - a widely used photon transport simulator - as an example, we showcase the capability of the proposed framework to convert user descriptions to structured simulation inputs. Benchmarked using 33 diverse natural language simulation descriptions, our LLM interface, MCX-LLM, achieves 98% accuracy and 99% repeatability, with an average processing time of 8.96 seconds per prompt. The framework also successfully handles various linguistic styles and diverse simulation settings, achieving a 100% success rate on 20 unconstrained real-world prompts. With only minor adjustments, our LLM interface also produces valid inputs for a finite-element-based diffusion solver to demonstrate generality towards other optical simulators. ConclusionsBy combining LLMs capability for textual data comprehension with structured constraints, this work provides a pathway to making complex scientific tools accessible while ensuring the reliability and technical correctness required for rigorous scientific research. MCX-LLM has been integrated with MCX Cloud accessible at https://mcx.space/cloud.

Computational models are central to cognitive neuroscience, but their rigorous application to experimental datasets is often constrained to a narrow set of canonical models that afford tractable analytical computations. We introduce the HSSM (Hierarchical Sequential Sampling Model) ecosystem, a Python toolbox that democratizes access to a broad, extensible array of neurocognitive process models through hierarchical Bayesian inference. Naturally leveraging simulation-based inference via likelihood surrogates, HSSM enables fast parameter estimation for models lacking closed-form likelihoods. Built atop PyMC and Bambi, HSSM provides a user-friendly formula syntax for specifying hierarchical mixed-effects regressions on model parameters, incorporating trial-by-trial neural or physiological covariates. The ecosystem allows fast model simulation and training data generation, as well as the neural network training utilities to deploy surrogate likelihood networks via HuggingFace. Contributions are designed to benefit not only the single researcher working on a problem, but organically, the entire research community. Together, the tools in the HSSM ecosystem bridge the interests of computational theorists as well as experimentalists, accelerating the cycle from model development to rigorous empirical testing.

Neurophysiology datasets are becoming increasingly complex, combining behavioral measurements with high-dimensional neuronal activity recordings coming from optical and/or electrophysiological measurements. The Neurodata Without Borders (NWB) standard has emerged in the community as the format of record. While standardized and widely used preprocessing tools generating NWB files have been developed, extensible frameworks for scientific analysis downstream of the NWB ecosystem are still under-represented. We present CICADA, a Python framework dedicated to analysis of neurophysiological data in the standardized NWB format. The toolbox is built as three hierarchically-organized packages: cicada-nwb (NWB access layer), cicada-analysis (plugin-based analysis engine and tool library), and cicada-gui (PyQt5 desktop application at the head of the pipeline). Beyond this architectural separation, CICADA is built around a central design principle: supporting a continuum from turnkey use to full modularity. Researchers can use the complete GUI-driven cicada-gui workflow without writing code, programmatically use existing analysis plugins from cicada-analysis, contribute to new analysis plugins, reuse utilities from cicada-tools, or build entirely custom pipelines on top of the cicada-nwb access layer alone. The same analysis plugin runs identically in interactive GUI and parameter-configured headless modes, enabling reproducible multi-session, multi-animal group analyses. We illustrate the versatility of CICADA with example analyses of behavioral, calcium imaging (two-photon and widefield) and extracellular electrophysiology datasets from rodent laboratories. CICADA is open source, actively maintained, and designed so that any laboratory can contribute at any level of the stack without modifying the core framework.

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.

golgi is an open-source platform that takes a peripheral nerve from image to stimulated fiber population through a single graphical interface, with an equivalent scriptable Python API and command-line interface for batch and high-performance use. It integrates promptable image segmentation, automated multi-region tetrahedral meshing, anisotropic finite-element solution of the extracellular field with an explicit perineurium contact impedance, generation of realistic fiber populations and their three-dimensional trajectories, and biophysical activation thresholds through interchangeable backends-- NEURON (via PyFibers) and a GPU-accelerated surrogate (AxonML). Every study exports as an integrity-hashed bundle whose image-to-recruitment provenance is verifiable byte-for-byte. golgi lowers the barrier to in-silico peripheral nerve stimulation modeling for experimentalists and clinicians, using a fully open finite-element stack with no commercial dependencies.

We now exist in the era of massive datasets from genomics, large language models, and all the known knowledge of humanity right at our fingertips. Much of this data is becoming more accessible; however, processing such data remains an ongoing issue across systems including high performance computing (HPC) infrastructures. Massively parallel computing (MPP) has solved this using a divide and conquer approach by splitting workloads across independent nodes (i.e., central processing units (CPU) allowing for higher scaling of data). The main engine for this in python is Ray; however, it has many issues including a large code space, security issues, debugging opacity, and memory management issues. Here, we present HydraMPP, a lightweight, ease of use and utilization, with high auditability, and with SLURM ergonomics.

Reticulate evolutionary processes (events in which lineages merge, such as hybridization, recombination, and horizontal gene transfer) are widespread across nature but cannot be represented by phylogenetic trees alone. Phylogenetic networks have therefore become an important modelling tool, yet existing software is typically tied to specific inference paradigms and provides limited support for working with multiple network representations in a unified and programmable environment. PhyloZoo is an open-source Python framework that lowers the barrier to developing practical, easy-to-use software for phylogenetic network analysis. It provides data structures and algorithms covering the main representations used in the field, together with dedicated visualization tools and robust I/O for all major phylogenetic file formats. A particular emphasis lies on semi-directed phylogenetic networks, which explicitly represent root uncertainty and have so far received limited support in existing software. By offering a shared foundation for developing interoperable tools and a combinatorial layer that supports computational proofs and theoretical exploration, PhyloZoo enables reproducible workflows for applied, methodological, and theoretical studies of reticulate evolution. Availability and implementationPhyloZoo is implemented in Python and installable from PyPI, with source code, documentation, and examples available at https://github.com/nholtgrefe/phylozoo. Contactn.a.l.holtgrefe@tudelft.nl

Converting medical images into anatomically detailed, subject-specific finite element (FE) models is a long-standing bottleneck in brain computational modelling. These models are used to predict brain tissue deformation, e.g. in traumatic brain injury, particle diffusion in brain drug delivery, and other biophysical phenomena across neurological disorders. However, existing model creation workflows depend on manual image segmentation, proprietary meshing software, and labour-intensive repair of meningeal and interface structures, limiting reproducibility and cohort analysis. Here we present PARS, a fully automated, open-source pipeline that converts a T1-weighted MRI scan into a simulation-ready FE head model. PARS combines anatomical parcellation with tissue maps and uses iterative neighbourhood-based reclassification, yielding a gap-free whole-head label volume. The volume is directly converted into a hexahedral mesh, augmented with algorithmically reconstructed falx, tentorium, pia and dura mater, and refined by Laplacian smoothing under a node-locking scheme that controls element quality and the explicit-solver stable timestep. We evaluated PARS on 23 subjects spanning cranial volumes of 832 to 1,329 cubic centimeter, at 1.0, 1.5 and 2.0 mm MRI resolutions. At 1 mm, meshes achieved a median Scaled Jacobian of 0.976, and total intracranial volume error of ~0.54; quality remained high at 1.5 mm (SJ of 0.933) and 2 mm (SJ of 0.921). Model creation runtime ranged from 9 to 38 minutes per subject. Models generated by PARS have been validated against cadaveric brain displacement data and demonstrated utility across traumatic brain injury and normal pressure hydrocephalus research. PARS provides an open-access, reproducible resource that substantially lowers the barriers to subject-specific brain modelling.

The rapid expansion of high-quality, nearly complete eukaryotic genomes demands computational accelerations of existing bioinformatic infrastructure. TIR-Learner has been widely used for the de novo identification of Terminal Inverted Repeat (TIR) transposons, but suffers from slow runtime and a large memory footprint. Here we present TIR-Learner v4, a complete rewrite of TIR-Learner v3 and key supporting programs, which accelerates runtimes by two orders of magnitude and caps at a low RAM footprint irrespective of genome size. We demonstrate the scalability of TIR-Learner v4 by annotating TIRs in all 579 VGP Phase I genomes in around 4 hours.

Summary: fastQpick is a command-line tool and Python library for sampling FASTQ reads with replacement. Sampling with replacement turns a single FASTQ file into an arbitrary number of bootstrap replicates, which enables uncertainty quantification and statistical analysis at the level of raw reads. This process answers questions such as how much an abundance estimate would change if the library were resequenced, or whether a low-abundance call is robust to the particular reads that were sequenced. fastQpick works efficiently on large libraries by streaming files in two passes by default: first to count reads and create a hash-based counter, and then to write the sample. It generates a full-size bootstrap replicate of a 500-million-read library in under 30 minutes with 9.4 GB of peak memory, with a low-memory mode that reduces the peak to 1.4 GB. A single-pass mode draws samples in a single read through the file, using O(1) working memory and producing an output size that is exact in expectation but not fixed. In a real yeast RNA-seq experiment, bootstrap replicates generated by fastQpick recover the sampling uncertainty of transcript abundance estimates, matching the analytic multinomial standard errors to within a few percent. Availability and implementationfastQpick is open source and freely available under the MIT license on GitHub at https://github.com/pachterlab/fastQpick and on PyPI (pip install fastQpick).

The recount3 online resource provides tens of thousands of uniformly processed RNA-seq samples across human and mouse from major sequencing repositories like the Sequence Read Archive. While access to these datasets has traditionally been centered in the R/Bioconductor ecosystem, the growing prominence of Python in bioinformatics and machine learning necessitates native, efficient tooling for Python users. Therefore, we present the recount3 Python package with robust application programming interface (API) and command-line interface (CLI) for discovering, downloading, and materializing recount3 resources. The software orchestrates uniform resource locator (URL) resolution, persistent on-disk caching, and the automatic parsing of data into analysis-ready data structures, including Pandas DataFrames and BiocPy RangedSummarizedExperiment objects. The recount3 Python package drastically lowers the barrier to entry for large-scale utilization of RNA-seq data in Python-based computational pipelines, bridging the gap between massive public transcriptomic data and modern machine learning ecosystems.

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.

Summarybcftools is the standard toolkit for handling VCF and BCF variant files, but it processes records on a single core; its --threads option speeds up only compression of the output, not the work done on variant records. Processing large call sets is therefore slow, and users often divide the genome and reassemble the results by hand. We present pbcftools, a Perl wrapper that does this automatically: it splits the genome into chunks, runs an ordinary bcftools command on each in parallel, and reassembles the outputs by a method suited to the data type. Across Linux servers, Windows/WSL2 workstations and Apple laptops, with bcftools 1.21 to 1.24, parallel output was identical to serial output for every command tested. On 1000 Genomes Phase 3 data, operations writing compressed VCF ran 10.8 to 21.1 times faster with 32 cores and up to 35.4 times with 64, those writing text 3.7 to 12.8 times, and merging 100 VCF files 19.2 times. pbcftools also runs on LSF and Slurm clusters. Availability and implementationpbcftools is written in Perl (>= 5.16) and requires bcftools; local parallel execution also requires Perl module Parallel::ForkManager. It is released under the MIT license at https://github.com/zhangge-uc/pbcftools (DOI: 10.5281/zenodo.21780361).

Proteins and their associated complexes often adopt multiple conformations, with the transitions between these states playing a critical role in biological function. However, the resulting structural heterogeneity can be challenging to visualize and communicate, often requiring manual inspection and time-consuming annotation of biomolecular structures. To address this, we developed ResiRuler, a local, browser-based tool that uses inter-residue distance measurements to quickly quantify atomic displacements and map changes in internal geometry across ensembles of related protein structures. By converting structural differences into residue-pair distance changes, ResiRuler enables rapid identification of regions undergoing coordinated motion, local rearrangement, or large-scale conformational change. The resulting visualizations can be exported as scripts for PyMOL and ChimeraX, allowing users to explore conformational differences and generate publication-quality molecular figures in their preferred visualization environment. Using atomic models in Macromolecular Crystallographic Information File (mmCIF) file format, ResiRuler aligns multiple structures and measures structural variation across models facilitating visualization and presentation of these differences. This allows for rapid visualization of which regions of proteins change among ensembles of structures. The program is available for download at https://github.com/tbaker67/ResiRuler on macOS and Linux operating systems.

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.

SummaryAncient DNA studies are increasingly targeting samples that are beyond the limit of radiocarbon dating (>50 thousand years old) and are often difficult or impossible to date using other geochronological methods. In cases where complete mitochondrial genomes (mitogenomes) can be recovered from such samples, Bayesian molecular clock dating approaches are routinely used as an alternative method for estimating their age. However, molecular clock dating of ancient mitogenomes lacks a standardised, reproducible computational framework, and existing approaches rely heavily on graphical interfaces that limit automation and scalability. To address these gaps, we developed MitoDate, an automated Nextflow pipeline for reproducible molecular clock dating of ancient mitochondrial genomes. The workflow standardises Bayesian time-calibrated phylogenetic inference within a portable, containerised framework, reducing manual intervention and improving analytical consistency. Availability and implementationMitoDate is implemented in Nextflow and is freely available at https://github.com/CpgSthlm/MitoDate. The pipeline is distributed with containerised dependencies and detailed documentation, including example datasets and usage guidelines.

Structural variants (SVs) are large-scale genomic variants, which can disrupt important functional and regulatory elements, leading to genomic disorders in humans and playing important roles in domestication, disease resistance, and traits in plants. SVs are generated across populations of individuals and used for association studies, consisting of large datasets with thousands of genomic loci. Visualization of these SVs aids in understanding their genomic distribution, identifying patterns across affected or phenotypic groups, and assessing their proximity to other genomic regions of interest. A variety of tools exist for visualizing SVs, including linear genome browsers and graph-based methods; however, many do not offer intuitive or scalable representations of SVs across large populations. To address this, we present SVPopEx, an interactive tool for population-wide visualization and exploration of SVs. SVPopEx provides a unique and intuitive representation for insertions, deletions, inversions, duplications, and translocations in a linear genome-style browser. Novel features were developed to support comparisons across genomes within user-defined regions, including rendering SVs based on one or more samples and visualizing haplotypes. Use of the tool is demonstrated with SV datasets from Schistosoma mansoni and Lens culinaris. A task-based evaluation was conducted using SVPopEx and two other linear genome browsers, which demonstrated that SVPopEx excelled in (1) providing a clear representation of the SVs present and (2) supporting comparisons across genomes.

1.SummaryThe Clinical Omics System for Metadata and Outcome Storage (COSMOS) is an open-source, FAIR- and GCP aligned clinical trial unit (CTU) infrastructure designed to streamline the transition of academic clinical and multi-omic trial datasets into curated, analysis-ready repositories within Secure Data Environments (SDEs). By integrating an automated Data Quality and Data Validation (DQ&DV) "Trust Layer" with a relational Structured Query Language (SQL) schema, COSMOS enables programmatic and interactive data access via R Shiny applications. Dynamic integration of omics and clinical data is achieved through analytical data structures (e.g. ExpressionSet objects) linked via relational database identifiers. This lowers technical barriers for researchers and promotes governed data reuse and secondary discovery. Availability and ImplementationCOSMOS is freely available under the GPL-3.0 license at https://github.com/at2e19/SCTU_COSMOS_DQDV_Shiny Supplementary information: Supplementary data are available at Bioinformatics online.

Surface electromyography (sEMG) is the most practical non-invasive interface for myoelectric prostheses, exoskeletons, and rehabilitation systems, but power-line interference (PLI) contamination and excessive digital pipeline group delay still limit its clinical adoption. This paper proposes a co-designed analog-digital correction system combining a high-CMRR front-end with an exponentially-windowed RMS (EMRMS) envelope estimator and a recursive single-tone PLI canceller. We present a closed-form CMRR model capturing the electrode-skin imbalance, and provide a complete stability analysis of the LMS canceller. The EMRMS estimator reduces the computational overhead from[O] (L) to strictly[O] (1) in both time and space complexities. Featuring no data-dependent branching, the algorithm achieves deterministic algorithmic execution time (zero jitter under an RTOS environment) and is natively compatible with fixed-point arithmetic on microcontrollers lacking a hardware Floating-Point Unit (FPU). A reference implementation reaches an 8.2 {micro}s median per-sample latency, yielding an end-to-end delay of[~] 30 ms -- leaving a generous >90 ms budget for electromechanical actuation -- while requiring an active CPU duty cycle of merely 1.6%, enabling prolonged deep-sleep intervals. Validation on the public Ninapro DB2 dataset demonstrates a 13.9 dB mean SNR improvement (averaged across 12 channels; single-channel comparison: 9.7 dB, Table 3) and a 70.0 {micro}V envelope RMSE against a length-200 rectangular reference. Paired Wilcoxon signed-rank tests confirm statistical significance (p < 0.001) over static baselines, and Pearson correlation analysis ({rho} = 0.993 {+/-} 0.0002) confirms strict morphological fidelity. The full open-source codebase and benchmarks are publicly released. O_TBL View this table: org.highwire.dtl.DTLVardef@299dc5org.highwire.dtl.DTLVardef@3519a0org.highwire.dtl.DTLVardef@2586aborg.highwire.dtl.DTLVardef@1ac5610org.highwire.dtl.DTLVardef@1465c46_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 3:C_FLOATNO O_TABLECAPTIONQuantitative comparison on a common 60 s segment of Ninapro-like synthetic sEMG (single channel) with a 3 mV 50.3 Hz mains tone slightly drifted from the static notchs design centre at 50.0 Hz, stress-testing the adaptive corrector under a frequency mismatch. The Ninapro multi-channel aggregate (13.9 dB) reported in Section 3.4 uses mains exactly at 50 Hz (matched notch) and so achieves a higher {Delta} SNR. "MAC/sample" excludes the EMRMS square root and the pre-computed LMS sine/cosine. C_TABLECAPTION C_TBL

jaxon is an open-source, fully differentiable and GPU-native reimplementation of the canonical peripheral-nerve fiber models in JAX/Jaxley: the myelinated McIntyre-Richardson-Grill (MRG) and Sweeney axons and the un-myelinated Sundt and Rattay C-fibers. It reproduces NEURONs extracellular mechanism through a custom backward-Euler coupled intracellular/periaxonal double-cable solver, agreeing with PyFibers-wrapped NEURON on 99.6% of 943 activation-threshold configurations within 1% and matching conduction velocity to machine precision. Because the entire forward model is expressed in JAX, it is both vectorized--simulating whole fiber populations in parallel and reaching a geometric-mean [~]820x speedup at N = 100,000 fibers on a single GPU--and differentiable, so extracellular-stimulation parameters (per-contact amplitudes, waveform shape, and electrode position) can be optimized directly through the cable equation rather than grid-searched. jaxon slots into existing peripheral-nerve modeling pipelines as a gradient-enabled, population-scale replacement for the NEURON forward solver.