Journal of Open Source Software

Multiple sequence alignments (MSAs) are often treated as a solved problem. However, ambiguously or incorrectly aligned regions are the source of much error in phylogenetics and comparative genomics. CRAIC is a cross-platform desktop workbench that treats an alignment as a set of hypotheses to be stress-tested. CRAIC uses the posterior match probabilities of a three-state pair hidden Markov model (pair-HMM) to derive complementary views of alignment confidence. The five views are (i) a reliability overlay with live column and residue masking; (ii) a multi-aligner disagreement map, (iii) a local realignment sandbox, (iv) an interactive posterior explorer, and, where the true alignment is known, (v) a per-residue view of correctly predicted homologies. When present, MAFFT, MUSCLE, ProbCons, Clustal Omega, ClustalW and PRANK are integrated in the tool. In benchmarks the reliability score can locate misaligned columns, but while CRAIC can implement automatic filtering, it is primarily built for inspection and curation.

Motivation: Tree sets appear naturally when performing model based phylogenetic analyses. When tracking host-parasite co-evolution, testing sensitivity of different model assumptions, or even just checking convergence of individual runs of the same analysis, the question arises: are a given pair of tree sets the same? And if they are not the same, how do they differ? Results: We provide DensiTree 3, an interactive tool for inspecting pairs of sets of trees that allow visualising both tree sets using a single panel. Incorporation of CCDs provide better summary trees, taxon orderings and extra functionality like rogue detection, making it easier to interpret tree samples. Furthermore, a clade comparison graph allows inspection of differences in clade support and clade heights linked to the double-DensiTree panel. Together, these visualisations of tree sets make it easy to identify potential differences in the tree sets and interactively inspect these differences. Furthermore, recent advances in conditional clade distributions (CCDs) to represent tree distributions have been incorporated in DensiTree 3, and the most visible part of the user interface has been updated for a more modern look. Availability: DensiTree 3 is freely available from https://cs.auckland.ac.nz/~remco/DensiTree/. The program is licensed under GPL and source code is available via the link above.

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

Small RNA (sRNA) sequencing is widely used to study siRNA, miRNA and piRNA pathways and to profile sRNAs derived from viruses, transposons and host transcripts. However, many sRNA workflows rely on command-line mapping and separate software for downstream analysis and visualisation. We present sRNAMap, an HTML5/JavaScript application for mapping and analysing sRNA sequencing data. sRNAMap accepts either raw FASTA/FASTQ reads or pre-aligned BAM files. For raw reads, sRNAMap uses a k-mer-seeded alignment algorithm that runs directly in the browser using parallel Web Workers where supported. It generates strand-specific per-base coverage, 5'- and 3';-end profiles, read-length distributions and nucleotide-bias summaries, with optional library-size normalisation. It also quantifies characteristic read-overlap signatures, including 5';-to-5'; overlaps associated with ping-pong-like processing. Additional modules quantify phased sRNA production, non-templated 3'- and 5'-additions, and sequence diversity. Analyses can be saved and restored as JSON or exported as self-contained HTML reports containing figures and analysis metadata. Benchmarking against Bowtie 2 under approximately matched alignment settings using a representative virus-infected arthropod small-RNA dataset showed highly concordant mapping profiles. Per-position depth showed an R^2 of approximately 0.998, while 5';- and 3';-end counts showed R^2 values of approximately 0.97. sRNAMap is available at https://github.com/rhparry/sRNAMap under the MIT licence.

Abstract 1. Biomes are continental-scale vegetation units widely used in ecological and evolutionary research, often together with large-scale species-distribution data. Many conceptually distinct biome schemes exist, differing in the underlying definition, delineation methodology and spatial resolution, yet most studies using biomes default to a few readily available schemes rather than choosing the one best suited to the research question. 2. We present biomes, an R package distributing spatially explicit raster layers of 31 published global biome schemes in a harmonised format at a 10 x 10 km resolution, together with metadata and a concise description. To facilitate their use, biomes provides a user-friendly classification workflow to classify occurrence records into specific biomes and a data-driven ranking algorithm to help researchers to choose the most suitable biome scheme for their data and research question in a transparent, reproducible way. 3. The biomes workflow can be applied to any dataset of georeferenced terrestrial occurrences. It (1) accepts either user-provided occurrence records or records downloaded by the package, (2) ranks the schemes according to their fit to the occurrence data, (3) assigns the records to specific biomes, and (4) summarises the resulting taxon-biome patterns. We demonstrate this workflow using 17,030 occurrence records of 185 species of Bombacoideae (Malvaceae). The analysis identified the vegetation-based biome scheme of Ramankutty and Foley (1999) as the best fit and recovered the forest and savanna biome signal known in the group from the literature, with tropical evergreen woodland being the biome with the largest share of records (46.1%) and highest number of species (162 of 185 species; 87.6%). 4. biomes provides user-friendly access to technically standardised and spatially explicit layers of 31 biome schemes, and thereby enables the transparent, data-driven choice of biome schemes tailored to a specific research data and questions, improving the reproducibility and the specificity of biome use across ecological and evolutionary research.

Trans-arterial radioembolization using yttrium-90 (Y-90) microspheres treats unresectable liver cancer with radiation. The tumor-to-parenchyma dose ratio is mainly governed by the patient-specific hepatic arterial flow distribution, which transports the microspheres. Even though computational fluid dynamics (CFD) simulation can predict this flow distribution, its high computational cost and time limit intraprocedural application. To overcome this computational limitation, a physics-informed neural network (PINN) surrogate was developed to provide rapid predictions of flow distribution in a patient-specific hepatic artery model. The surrogate maps spatial coordinates to the velocity vector U with Cartesian components u, v, w and the static pressure p. Reference velocity and pressure fields came from a converged finite-element CFD solve of the same geometry at about 1.05 x 105 nodes; a controlled fraction was used to supervise the training while the remainder was held out for validation. Using the NVIDIA PhysicsNeMo framework, we compared four coordinate networks (MLP, Modified Fourier, Multi-Scale Fourier and SIREN) under soft and hard inlet enforcement across a supervised data sweep (data fraction df = 0 to 0.20) and repeated df = 0.20 at a matched 300,000-step budget. Predicted flow splits were propagated through an established Y-90 dose-kernel pipeline. Accuracy is governed primarily by the supervised-data fraction, not the architecture: at df [≥] 0.10 the backbones agree to within one to two R2 points, and R2(u) rises from {approx} 0 under pure physics (df = 0) to {approx} 0.95 by df = 0.01 for the best backbones, saturating near 0.97 to 0.99 by df = 0.20. At a matched 300,000-step budget the Multi-Scale Fourier network leads (R2(u) = 0.978) ahead of SIREN (R2(u) = 0.976) and the MLP (R2(u) = 0.968); the best value seen (R2(u) = 0.985, Modified Fourier) needed 1.8 times that budget. On the df = 0.20 checkpoint the per-outlet flow split matches CFD (r = 0.999, L1 = 4.4 percentage points) and the derived dosimetry agrees to within 2.4% for perfused volume, mean dose and every isodose volume. At df = 0 training collapses toward the trivial near-zero state, which satisfies continuity, momentum and the zero-pressure outlets exactly and is ruled out only by the inlet and data terms; this, not architecture choice, is the limiting failure mode. Once trained, the network evaluates the full 105,284-node field in about 2 s on a four-core CPU. The supervised-data fraction, with a practical floor between df = 0.05 and 0.10, is the primary design lever for such hepatic-arterial PINN surrogates.

BackgroundSecondary haplotigs retained in a primary assembly inflate genome size and duplicate gene content, and their removal remains a necessary curation step for heterozygous diploid genomes. HapSolo (Solares et al. 2021) treats this step as an optimization problem: it searches three pairwise alignment thresholds (percent identity, query coverage, and the ratio of aligned query to aligned reference length) to minimize a cost defined over single-copy, duplicated, and missing conserved orthologs. The original implementation depended on a separate BUSCO v3 run for every contig, on Python 2.7 and pandas in its inner loop, on a single randomized forward-walk optimizer, and on OrthoDB v9 lineage datasets that are no longer distributed. ResultsHere we report HapSolo2, a reimplementation that replaces the per-contig BUSCO step with miniprot-based ortholog classification against OrthoDB v9, v10, or v12.2 lineage datasets, adds steepest descent and simulated annealing to the original random forward walk, and evaluates all walkers in batched CuPy array operations on a single GPU. On an avocado assembly of 5,122 contigs scored against 1,440 orthologs, 100,000 iterations of random walk and steepest descent ran 5.6- and 7.6-fold faster on one NVIDIA RTX A6000 than on 10 CPU processes. Across 45 runs on four diploid assemblies (Anopheles funestus, Persea americana, Vitis vinifera, and Amblyraja radi-ata; 212 Mb to 3.2 Gb) and three OrthoDB releases, the three optimizers reached best costs within 1.8% of one another. Independent assessment with BUSCO v6.1 showed that HapSolo2 reduced duplicated orthologs from 52.0% to 5.6% in P. americana, from 17.9% to 10.4% in V. vinifera, and from 7.8% to 2.8% in A. funestus, with completeness decreasing by at most 1.3 percentage points. On the avocado assembly, HapSolo2 reduced the cost reached by the original HapSolo from 0.2200 to 0.1036 and raised contig N50 from 3,366 kb to 4,608 kb. ConclusionsHapSolo2 makes ortholog-guided haplotig removal practical in minutes on a single workstation, independent of a BUSCO installation, and reproducible across OrthoDB releases. It is distributed as an installable Python package with a unified command-line pipeline, an assembly statistics and plotting tool, an Apptainer container, and archived copies of the legacy ortholog datasets.

Metagenomic sequencing enables broad pathogen detection without requiring prior assumptions about the organisms present, but the memory and computational demands of classifying large sequencing datasets against reference databases often lead analysis pipelines to rely on desktop or server-grade computing platforms. This limits practical use in disasterstricken and other resource-constrained environments, where portable sequencing may be available but reliable access to cloud or laboratory infrastructure is not. We present KENT, a portable edge-computing framework for metagenomic pathogen screening based on a cluster of NVIDIA Jetson Nano modules. KENT adapts CuCLARK-l for low-memory embedded execution and extends it with a custom software stack for distributed read classification, abundance estimation, split-run abundance merging, and report generation. Using an MPI-based controller/responder architecture, KENT supports local, offline analysis with sample-level parallelism across multiple nodes. We evaluate KENT using long read wastewater metagenomic samples with a custom pathogen database and the CAMI2 Marine long read dataset. In the wastewater setting, KENT achieves an average speedup of 3.1x over the CPU-based CLARK-l baseline while maintaining close agreement in classified read abundance estimates. On CAMI2 Marine, KENT achieves an average speedup of 2.68x and similarly preserves CLARK-l abundance profiles. Evaluation against the CAMI2 long read taxonomic ground truth yields a mean species-level precision of 58.55%, recall of 93.36%, F1 score of 71.74%, and L1 abundance distance of 0.731, closely matching CLARK-l. Among ground truth species represented in the reference database, KENT detects 99.86% on average. A controlled database-size experiment further shows little change in classification throughput across the tested database configurations, while large sequencing inputs remain constrained by the Jetson Nanos unified memory capacity. Overall, KENT demonstrates that GPU-accelerated metagenomic classification can be adapted to low-memory embedded hardware while preserving the biological profiling behavior of the CLARK-l baseline, supporting portable and offline pathogen screening in resource-constrained settings. AvailabilityKENT is available at https://github.com/BilkentCompGen/KENT

Neuroimaging research focusing on subcortical brain regions showed that they play a role in a wide range of cognitive functions and associated their structural alterations to various psychiatric disorders. Methods to analyse such structures were developed to observe changes, not simply in terms of volumes, but in terms of surfaces, which are able to detect subtle and local changes in shape and geometrical configuration. Such advanced analyses have however been largely restricted to command line environments with relatively limited access to Python users or researchers with low technical expertise. We present SubCortexMesh as a user-friendly toolbox which covers automated surface estimation from popular subcortical volume segmentations (FreeSurfer and the Functional Magnetic Resonance Imaging of the Brain Software Library (FSL)), computes shape-related vertex-wise metrics (thickness, surface area and curvature) for whole cohorts and includes statistical analyses. This is all done inside Python, automatically computing all subjects of a given preprocessed directory, with the explicit intent to minimise steps and manual coding required from the users. SubCortexMesh includes statistical tools which allow conventional random field theory-based cluster analyses on native subject metrics, standardised within common surface templates, thus containing a whole workflow necessary to run an up-to-date shape-wise subcortical analyses in one package.

Biomedical knowledge graphs can connect information across genes, phenotypes, tissues, pathways, experiments, and clinical resources, but they are difficult to query correctly without detailed knowledge of the graph. Large language models can help write Cypher code for knowledge graphs, yet a query focused on a bioinformatics task that looks reasonable may still use the wrong identifier, relationship direction, source, intermediate node, or output unit if the user is not completely trained on the schema. We developed ddkg.skill, an Agent Skill that can be loaded into a compatible LLM session to support querying of the NIH Common Fund Data Ecosystem Data Distillery Knowledge Graph (DDKG), a biomedical property graph integrating more than 180 ontologies, over 40 genomics datasets, and dozens of additional cross-domain biomedical datasets. Rather than utilizing a simple markdown prompt, ddkg.skill contains a controller, a compositional library of DDKG-specific references and structured tables, primary documentation, validated query patterns, a routing table, and a Python script that checks the internal links among these materials. The skill build for the December 2025 DDKG release contains 38 bundled files and 239 routing relationships and is identified by checksum so users can state the exact build used to compose a query. The evaluation reported here was performed separately on an earlier build with 38 files and 227 routing entries. In that orthogonal nine-test evaluation against a live December 2025 DDKG instance, five of seven tests targeting sources absent from the worked examples produced correct executed results. The evaluation also exposed a failed query, a cross-source comparison that was not biologically well posed, and errors in the skills own reference material that informed later revisions. The skill guides an AI through entity resolution, graph inspection, query construction, and validation for biomedical and bioinformatics queries, while keeping the DDKG itself as the source of returned results. This design provides a portable and versioned method for giving general-purpose AI systems practical knowledge of a complex biomedical knowledge graph to empower complex bioinformatics data integration. Three executed biomedical use cases further demonstrate cross-species phenotype-to-expression querying, genomic overlap between 4DN chromatin-loop anchors and GTEx eQTLs, and integration of congenital heart phenotype associations with ClinVar and GTEx heart evidence.

Phenotype prediction from public gene-expression data is straightforward to implement but reliable prediction is harder. Leakage during feature selection can inflate performance estimates. Class imbalance can hide poor performance in minority classes. Confounds like tissue type, repeated sampling, and demographic differences can also create patterns that resemble phenotype-associated biology. Here, I present LOL (Latent Omics Learning), a Python package for phenotype prediction from bulk GEO RNA-seq and microarray data. LOL uses leakage-safe preprocessing, imbalance-aware evaluation, and group-aware validation where repeated samples are present. Each analysis produces two audit diagnostics. The leakage inflation index ({Delta}LII) measures the change in performance between naive and leakage-safe evaluation. PVCA-lite examines associations between the latent representation and available metadata covariates, including phenotype. I evaluated the workflow across six independent GEO cohorts. The datasets covered microarray and RNA-seq platforms, binary and multi-class phenotypes, and sample sizes ranging from 18 to 566. The analyses produced a varied set of outcomes. Across diverse GEO datasets, LOL can reveal phenotype-associated expression patterns while identifying factors distort their interpretation. These include information leakage, class imbalance, repeated sampling, tissue differences, and demographic or other metadata-associated confounding. The resulting diagnostics help distinguish robust predictions from results that require further scrutiny. LOL does not introduce a new representation-learning algorithm. Its contribution is a reproducible workflow that places validation and diagnostic checks alongside prediction. The resulting analysis can expose weaknesses in an apparently convincing result and make those limitations visible. LOL is freely available under the MIT license from https://github.com/ngangao/lol-omics.

Conventional short read DNA alignment algorithms, such as widely used BWA-MEM2, devote a substantial fraction of their execution time to seed generation. We present RosaSeed, a short read alignment algorithm, based on a configurable seeding framework, that significantly accelerates alignment while maintaining accuracy comparable to that of BWA-MEM2. RosaSeed replaces the BWA-MEM2 seeding kernel and directly supplies candidate seeds to the existing BWA-MEM2 chaining and alignment extension pipeline, producing standard SAM output suitable for downstream variant analysis. RosaSeed was evaluated on both simulated and real short read datasets, and its seeding time, fastq-to-SAM time, alignment accuracy, and memory footprint were compared against BWA-MEM2, Minimap2, Bowtie2, and the Enumerated Radix Tree (ERT) algorithm. We also compared against ERT2, a proposed simplified variant of ERT that sacrifices a small amount of alignment accuracy to significantly increase the alignment throughput. With single-threaded software execution, our recommended RosaSeed configuration achieves 13.06x and 3.85x speedups in seed processing and fastq-to-SAM time, respectively, over BWA-MEM2. Corresponding seed processing (and fastq-to-SAM) speedups are 3.96x (2.48x) over ERT, 1.2x (1.44x) over ERT2, 4.82x (3.31x) over Minimap2, and 33.24x (10.83x) over Bowtie2. RosaSeed requires only 49.61 GB of peak memory, approximately 25% less than ERT and ERT2, while maintaining alignment accuracy comparable to that of BWA-MEM2. Following the recent release of minibwa, a prefetch-optimized FM index aligner, we additionally benchmarked RosaSeed against it on an upgraded 24-core, 48-thread AMD Zen 3 workstation. Our speed-optimized miniRosaSeed configuration achieves a 2.12x fastq-to-SAM speedup over minibwa for single-threaded execution while producing 2.58% higher standard accuracy. Against Strobealign, miniRosaSeed reported a 2.18x fastq-to-SAM speedup and 3.1% higher standard accuracy. For multi-threaded execution, miniRosaSeed was both faster and more accurate than minibwa for up to 26 software threads on the Zen 3 workstation. RosaSeed provides multiple runtime-memory configurations, spanning memory-efficient and high-performance strategies, enabling users to select operating points appropriate for diverse computational environments while maintaining alignment quality comparable to that of BWA-MEM2.

Two-photon calcium imaging enables measurements of neural activity from individual neurons and their subcellular processes in awake animals. Axial brain motion can change measured fluorescence independently of neural activity and move small structures out of the imaging plane, confounding two-photon calcium measurements in behaving animals. We developed two complementary post-acquisition methods, selected according to whether the imaged structure remains detectable across the encountered depth range. When it does, z-stack correction estimates imaging depth from a separately acquired reference z-stack and uses an axial fluorescence profile specific to each neural structure to rescale fluorescence to a common reference depth. When a structure can leave a single imaging plane, volume-based z-registration reconstructs a common-depth movie from repeated acquisitions of closely spaced planes. In awake mice, both methods tracked experimentally imposed axial displacements within the sampled range, with depth-estimation root mean squared errors (RMSEs) below 1 m for z-stack correction and 0.69-1.03 m for volume-based z-registration. Correction changed fluorescence substantially for a subset of somata, with larger effects in axonal boutons. Both methods are implemented in suite2p-in-depth, a Python package that integrates with Suite2p and provides diagnostics for depth estimation, correction quality and data reliability. Together, these methods enable axial-motion artifacts to be assessed and corrected within established Suite2p workflows.

Phylogenetic profiling infers gene function from the presence and absence of genes across genomes, but it loses resolution in eukaryotes, where many protein families contain several functionally distinct paralogs. Existing resources address parts of this problem separately: web services build trees for one family at a time, orthology databases provide precomputed orthologous groups for predefined species sets, and tree-profiling tools require users to supply their own trees and groupings. Here, we present KEDROS, a web platform that builds gene trees for any protein family across UniProt reference proteomes, splits each tree into subfamilies at inferred gene duplications, and profiles each subfamily separately. The organisms, the families and the taxonomic depth at which trees are split are all chosen by the user, so the same workflow serves any question that can be posed as a contrast between two sets of organisms: a pathogen and its host, a pest and non-target species, or lineages with and without a trait. KEDROS answers such contrasts at two levels. Across families and subfamilies, it ranks those present in one set and absent from the other, pointing to lineage-specific genes and candidate targets. Within a subfamily shared by both sets, it identifies amino acid positions that differ consistently between them and may underlie functional divergence, and displays them on AlphaFold structures. All analyses run in a web browser, with no installation or programming. Each of these steps exists in some form elsewhere, but to our knowledge no other resource runs them end to end, at the scale of whole proteomes, on organism sets defined by the user. We illustrate KEDROS with two cases. In the trypanothione reductase family of trypanosomatids, it recovers parasite-specific subfamilies and the substrate-binding substitutions that separate the parasite enzyme from human glutathione reductase. In the bacterial phylum Bacillota, it recovers the sporulation machinery from phenotype labels alone across 1,218 proteomes, and ranks an uncharacterized family among the strongest markers of the trait. KEDROS thus offers a general, code-free route from a comparative question to testable hypotheses about genes and residues. KEDROS is freely available at https://kedros.cgenomicslab.org.

Rule-based languages have proven effective for modeling systems of interacting structured entities as typically encountered in chemistry and molecular biology. We present PyKappa, a rule-based modeling package written in Python whose interpreted nature enables interactive simulation and analysis, including by agentic AI. The package seeks to broaden the base of developers by utilizing a widely known programming language and serves as an easy-to-deploy teaching tool. Using PyKappa, we conduct a case study of phase separation.

Breedbase ( is a comprehensive, web-based, open-source breeding management and analysis software for plant breeding that was introduced to address gaps with existing software for plant breeding data. Breedbase is continually improved and developed, with new features being added as required by the breeding community. Since its inception, the capabilities of Breedbase were improved to better serve the diverse data modeling needs of additional crops beyond our original ensemble of cassava, banana, yam, sweet potato, tomato, rice, and wheat. We describe here major updates that have been made in the past 4 years, including (1) improved handling of repetitive measurements; (2) a redesigned experimental treatments interface; (3) a more fine-grained user permission system; (4) improved trial quality control workflows; (5) intercropping support; (6) the addition of a decision meeting feature that helps decision makers make the best selection with the available data; (7) transgenics and tracking tools and finally, (8) an improved field layout tool which consolidates several trials in one field. These new features integrate seamlessly with the previous feature set of Breedbase - making it one of the most feature rich breeding management softwares available.

Shotgun sequencing of DNA mixtures has the potential to become an important tool in forensic genetics, particularly for degraded samples or complex mixtures. However, the analysis of shotgun-sequenced mixtures is challenged by several confounding factors, including sequencing errors, population structure, the potential presence of relatives, linkage, and linkage disequilibrium. Additionally, the statistical methods currently available for such data are limited. Here, we develop likelihood ratios that summarize the evidence for the presence of a suspects DNA in a mixture, assuming genotypes of the suspect and the victim are available, while accounting for DNA from unknown contributors modeled either as general population contamination or as a single contaminating individual. We show that these likelihood ratios are not robust to model assumptions and can falsely support the presence of the suspects DNA when the true contributor is even a distant relative. We therefore propose a combined evidence score, {Lambda}: the likelihood ratio minimized over contamination models, reported only when the evidence favors the suspect over a hypothesized sibling by at least a factor of C (default C = 10) under at least one contamination model, and otherwise set to 1 (log {Lambda} = 0). In simulations based on 1000 Genomes data, {Lambda} retains nearly all power to detect true contributors at mixture proportions as low as 5% while eliminating the false positives produced by model mis-specification, population structure, and by untested relatives. The methods are implemented in the open-source program DNAMIXTURE, providing the forensic and population genetics community a resource for robustly identifying small individual contributions to a DNA mixture.

Many studies using mouse models of human brain disorders rely on extended recordings of brain electrical activity through electroencephalogram, or EEG. The features that researchers measure in these recordings, such as seizures and epileptiform activity, are deeply influenced by whether the animal is awake, in non-rapid eye movement (NREM) sleep, or in rapid eye movement (REM) sleep. Because total sleep duration varies naturally across animals, rigorous experimental comparisons require classifying the sleep state of every few seconds of a recording. Manual annotation is highly labor-intensive, often requiring hours per subject. While automated scoring tools accelerate this process, they are typically trained exclusively on healthy cohorts and frequently fail when applied to disease models that exhibit atypical or fragmented sleep architecture. Somnus is an open-source Python package designed to automate rodent sleep staging using EEG, muscle activity (EMG), and optional video-tracking data. It includes a robust base model trained on diverse recordings from six independent laboratories, enabling out-of-the-box classification across various equipment setups without requiring prior manual labeling. A central feature of Somnus is its capacity for rapid adaptation to pathological sleep structure. The software provides a graphical interface that directs researchers to the specific epochs where the base model is least certain. Users can manually correct these targeted epochs in a built-in viewer by comparing the model's classification to the raw EEG signal and video, and then use their corrections to fine-tune the model. This workflow empowers researchers to generate accurate custom scorers for complex disease models or novel treatments with just a few minutes of manual correction, bypassing the need to hand-score entire experimental cohorts or develop their own models from scratch.

The Hierarchical Gaussian Filter (HGF) is a hierarchical Bayesian model of learning under uncertainty and volatility, with a widely used MATLAB implementation. We present HGFX 1.0.0, a Python/JAX toolbox whose primary objective is validated behavioral and numerical compatibility with a frozen HGF Toolbox 8.2.0 reference, while removing MATLAB as a user-runtime dependency. Reimplementation is treated as a validation problem rather than source translation: configuration semantics, parameter transforms, forward trajectories, observation likelihoods, objectives, fitting and statistical surfaces, simulation workflows, official demo behavior, model selection, and backend agreement are compared against the pinned oracle under predeclared tolerances. Two official MATLAB demo workflows reproduce the reference at frozen trajectory tolerances, including a regime in which classic HGF encounters negative posterior precision while eHGF completes successfully. In paired model-selection validation, all 36 BIC winners agree between MATLAB and HGFX. Physical NVIDIA GPU applicability was confirmed on two Tesla T4 devices, with a maximum CPU-versus-GPU final-objective difference of 1.42e-14 against a frozen 1e-7 criterion. Historical parameter recovery results that did not meet predeclared criteria, together with exact MATLAB/HGFX limitation matches, are preserved and are not reclassified as scientific success. A prospectively gated comparison with pyhgf 0.3.2 shows binary64-scale agreement on mapped perceptual trajectories in one authorized three-level binary-HGF cell, while participant-response negative log-likelihood is retained as not directly comparable. HGFX therefore provides a MATLAB-independent Python implementation with an explicit evidence model that separates direct parity, matched reference limitations, backend applicability, and future performance claims.

At-home sampling can address obstacles to participation in transcriptomics research. We previously developed the homeRNA and homeRNAmax platforms; these custom-designed tubes containing RNA stabilizer interface with a blood tube compatible with upper-arm blood collection devices, allowing for blood collection and RNA stabilization to occur from a participant's home. In this work we introduce homeRNAdrop, an insert for a commercially available blood tube (BD Microtainer) that divides the tube into two compartments: the top compartment holds 200 L blood and the bottom compartment holds ~590 L liquid stabilizer (RNAlater). By combining blood collection and RNA stabilization into the same tube, homeRNAdrop streamlines sample preparation for participants and allows for more compact sample storage for the lab. In a remote pilot study, n=25 participants from across the United States tested the homeRNAdrop kit. The n=23 samples successfully received by the lab show that samples collected using homeRNAdrop are of sufficient RNA quality (all RINs > 6, a common cutoff for analysis) and yield (mean yield=1.44 g) for downstream gene expression analysis. Almost all participants who successfully collected blood using the Tasso Mini device found the homeRNAdrop device easy to use (n=23/24). Overall, this work adds a simplified, user-friendly platform to the growing suite of tools for remote blood RNA stabilization.