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PubMed2026

WILDkCAT: extract, retrieve, and predict enzyme turnover numbers of constraint-based metabolic models.

SUMMARY: Accurate enzyme turnover numbers are essential for building enzyme-constrained genome-scale metabolic models. However, collecting and curating these parameters remains a major bottleneck. Indeed, kcat values are scattered across multiple databases, reported under varying experimental conditions, and often missing for many enzymes. To address this challenge, we present WILDkCAT, a Python-based pipeline that enables the retrieval of kcat values from wild-type enzyme measured under user-specified pH and temperature ranges for a given metabolic model. The application to Escherichia coli (iML1515) and Homo sapiens (Human-GEM) models demonstrated the ability of WILDkCAT to retrieve substantial kcat coverage and its applicability across diverse genome-scale models. AVAILABILITY AND IMPLEMENTATION: WILDkCAT is available at https://github.com/sysbiolux/WILDkCAT and from PyPI. WILDkCAT works on all major operating systems and computer architectures. The documentation is available at https://sysbiolux.github.io/WILDkCAT.

Software↗
PubMed2026

Making multi-axis Gaussian graphical models scalable to millions of cells.

MOTIVATION: Networks underlie the generation and interpretation of many biological datasets: gene networks shed light on the regulatory structure of the genome, and cell networks can capture structure of the tumor micro-environment. However, most methods that learn such networks make the faulty "independence assumption"; to learn the gene network, they assume that no cell network exists. "Multi-axis" methods, which do not make this assumption, fail to scale beyond a few thousand cells or genes. This limits their applicability to only the smallest datasets. RESULTS: We develop a multi-axis method, which learns conditional dependency networks, capable of processing million-cell datasets within minutes. This was previously impossible, and unlocks the use of such methods on modern scRNA-seq datasets, as well as more complex datasets. We apply the method to a new scRNA-seq dataset for neuronal cell development, and compare the result to an existing state of the art method, hdWGCNA. We demonstrate that the new method yields gene networks that have a more focused biological interpretation and that the simultaneously learned cell network has advantages over a conventional kNN-based clustering. Further, our method yields novel biological insights by identifying long non-coding RNAs that potentially have a role in neuronal development. AVAILABILITY AND IMPLEMENTATION: Our methodology is available as a Python package GmGM on PyPI (https://pypi.org/project/GmGM/0.5.3/). The code for all experiments performed in this article is available on GitHub (https://github.com/BaileyAndrew/GmGM-Bioinformatics) and Zenodo (10.5281/zenodo.20384566).

Gene Regulatory Networks↗
PubMed2026

Quantifying uncertainty of predictions from cancer progression models.

MOTIVATION: Cancer progresses through the accumulation of genomic events. Cancer progression models such as Mutual Hazard Networks (MHNs) describe this dynamic, enabling prediction of temporal event positions and patient-specific risks of acquiring mutations. However, current MHN analyses rely on single most likely models and do not quantify the uncertainty inherent to parameter estimation. Assessing forecast stability is essential before using them to anticipate treatment-relevant mutations, adapt targeted therapies, or prioritize monitoring of patients at elevated progression risk. RESULTS: We address a key prerequisite for the responsible clinical use of cancer progression models by making MHN-derived predictions uncertainty-aware. We present a Bayesian framework for MHN that uses Markov Chain Monte Carlo to sample from the posterior distributions of model parameters and derived predictions. For practical use we implemented the Random-Walk Metropolis, Metropolis-Adjusted Langevin Algorithm (MALA), and simplified manifold MALA samplers as part of the existing mhn Python package. Only MALA and smMALA were successful in sampling from MHN posteriors, with MALA performing best. While most MHN parameters and predictions showed low posterior variance, a small subset displayed greater variability across the posterior distribution. This differentiation cannot be obtained from a single most likely model, emphasizing the need for uncertainty quantification, especially in clinical contexts. As an illustrative example, posterior sampling identified a subgroup of STK11$-$, KRAS$+$ lung adenocarcinoma patients with a high predicted short-term risk-with low variance across posterior samples-to develop an STK11 mutation. This subgroup exhibited poorer survival under immunotherapy, resembling patterns observed in STK11+ patients. AVAILABILITY AND IMPLEMENTATION: Our implementation is part of version 1.2.0 of the mhn package (https://github.com/spang-lab/LearnMHN). All analyses including the code to produce all figures in this article can be found under https://github.com/huy29433/MCMC-sampling-for-MHN (https://doi.org/10.5281/zenodo.21160219).

Humans↗
PubMed2026

WxS-QC-a quality control pipeline for human germline short-variant Whole-Genome and Whole-Exome cohorts for population-scale analyses.

SUMMARY: Whole-exome (WES) and whole-genome (WGS) sequencing are rapidly becoming preferred methods for population-scale analysis of the human genetic landscape. However, there are currently no standardized quality control (QC) pipelines for human WES and WGS datasets. In this paper, we present WxS-QC, a powerful, scalable, and convenient pipeline for the QC of human germline short-variant WGS and WES cohorts for population-scale analyses. Our pipeline is suitable for both rare-variant discovery and common-variant association studies. It is based on deeply refactored gnomAD v3 and v4 quality control pipelines, contains several methods we have developed de novo, and is aligned with current best practices in WGS/WES germline cohort QC. We provide all methods in a single codebase, aligned to work together and controlled via a single YAML config, with automatic export of resulting graphs and summary tables, excellent performance and scalability, and comprehensive documentation. The pipeline can run in any UNIX-like environment and can efficiently process cohorts of up to 200 000 whole-exome samples, with the potential to handle bigger datasets. AVAILABILITY AND IMPLEMENTATION: The pipeline code is written in Python using the Hail library and is freely available under the BSD-3 license here: https://github.com/wtsi-hgi/wxs-qc. The detailed description of the pipeline is available in the pipeline documentation: https://github.com/wtsi-hgi/wxs-qc/blob/main/README.md. We also provide an open dataset with all required metadata, which is available at https://wxs-qc-data.cog.sanger.ac.uk/wxs-qc_public_dataset_v3.tar. An example of test dataset analysis is available in the supplementary materials.

Humans↗
PubMed2026

pLAST-a tool for rapid comparison and classification of bacterial plasmid sequences.

MOTIVATION: The increasing number of fully sequenced bacterial plasmids being annotated and catalogued has prompted the development of computational tools for comparing and classifying them. Existing approaches typically compare full-length DNA sequences (e.g. Mash, BLASTn, and ANI-based methods) or translated open reading frames (ORFs) (e.g. DIAMOND), with plasmid-level scores obtained by aggregating ORF-to-ORF similarities; however, they are either restricted to closely related plasmids or become computationally demanding in large-scale analyses. RESULTS: We describe pLAST (plasmid Language Analysis and Search Tool), a plasmid-search tool built using word2vec representations of protein-family content informed by local genomic context. Benchmarks indicate that pLAST outperforms nucleotide-based methods and performs comparably to DIAMOND in identifying functionally similar plasmids and compared with the widely used Mash, it achieves 26% and 24% improvements in detecting shared mating-pair formation system type and relaxase type, respectively. This performance scales to database searches across hundreds of thousands of sequences, as demonstrated using the precomputed PlasmidScope collection of ∼750 000 plasmids. Beyond global similarity, pLAST also returns per-ORF plasmid-plasmid alignments, enabling detection of shared functional modules. AVAILABILITY AND IMPLEMENTATION: pLAST is freely accessible as a web server at https://plast.lbs.cent.uw.edu.pl/ or https://plast.lbs.biol.uw.edu.pl/ and available as a Python module along with a precomputed database at https://github.com/labstructbioinf/pLAST for customized analysis.

Plasmids↗
PubMed2026

Zone equalisation normalisation for improved alignment of epigenetic signal.

MOTIVATION: High-throughput genomic technologies have transformed our understanding of biological systems, yet direct comparison and visualisation of these complex datasets remains challenging. Existing normalisation methods often fail to align genomic signal across samples due to sensitivity to sequencing depth differences and localised high-signal artefacts, leading to inconsistent replicate behaviour and increased downstream variability. RESULTS: We introduce Zone Equalisation Normalisation (ZEN), a novel approach designed to improve cross-sample signal alignment of genomic data. ZEN rescales genomic signal based on variance estimated within biologically enriched regions, reducing the influence of extreme outliers while preserving underlying biological structure. Using a diverse collection of data and our new genome-wide benchmarking approach, we reveal that ZEN improves biological and technical replicate alignment across the majority of tested conditions and experimental platforms. We further show that this improved signal comparability is associated with fewer differential accessibility calls between technical replicates and a more conservative set of biological differences. Together, these results demonstrate that ZEN provides a complementary framework to improve the accuracy and reliability of genomic data analysis and that normalisation choice can affect downstream analyses and biological interpretation. AVAILABILITY AND IMPLEMENTATION: ZEN is available as an open-source Python package via conda and PyPI. Source code, documentation, tutorials, and code to reproduce the analyses are available at https://github.com/Genome-Function-Initiative-Oxford/Zone-Equalisation-Normalisation and Zenodo (https://doi.org/10.5281/zenodo.21067751).

Epigenesis, Genetic↗
PubMed2026

Pesci: fast and user-friendly software to compare single-cell gene expression across species.

SUMMARY: Recent technological advances have propelled comparative functional genomics into the single-cell era, spurring a rapid development of methods to analyse these complex datasets. However, comparing single-cell gene expression across species to quantify expression similarity and ultimately identify homologous cell types remains an open problem. The ICC algorithm (Iterative Correlation of Coexpression) has been recently proposed as an attractive approach to tackle this challenge, but, to date, no software implementation is available. Here, we introduce Pesci (Pretty Easy Single-cell Comparisons using ICC), an efficient and user-friendly implementation of the ICC algorithm applied to pairwise comparisons of single-cell gene expression atlases across species. AVAILABILITY: Pesci is implemented in Python 3 (≥3.7). It is available for download on Linux, macOS and Windows via pip, conda and GitHub at https://github.com/eparey/pesci. The source code is permanently archived on Zenodo (https://doi.org/10.5281/zenodo.21477543).

Software↗
PubMed2025

Global maintenance of histone post-translational modifications during the transition into anoxia in embryos of the annual killifish Austrofundulus limnaeus.

Many organisms have adapted to survive anoxic or hypoxic environments, but the epigenetic responses involved in this successful stress response are not well described in most species. Embryos of the annual killifish Austrofundulus limnaeus have the greatest tolerance to anoxia of all vertebrates, making them a powerful model to study the cellular mechanisms necessary for anoxia tolerance. However, the global histone landscape of this species has never been quantified or explored in relation to stress tolerance. Liquid chromatography-mass spectrometry and a Python bioinformatics workflow were used to identify histones and their post-translational modifications. This pipeline resulted in the detection of 252 unique biologically relevant histone post-translational modifications (hPTMs) (unimod + residue). These PTMs represent 16 types of biologically relevant hPTMs present during both anoxia and normoxia in Wourms' stage 36 embryos. This hPTM library presents an exciting opportunity to study histone modifications across development and in response to environmental stressors. No significant changes in PTM or histone abundance were observed between anoxic and normoxic embryos, suggesting that 24 h of anoxia is not sufficient to induce epigenetic or histone isoform changes at the organismal level. This result is inconsistent with data presented for similar stresses in mammalian cells and thus stabilization of the hPTM landscape may be an adaptation that supports anoxia tolerance.

anoxia↗
PubMed2026

scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data.

Recent advances in single-cell epigenomic techniques have increased the demand for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis. One key analytical task is to determine cell type identity based on epigenetic data. Here, we introduce scATAnno, a Python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates reference atlases from publicly available datasets, enabling accurate cell type annotation by integrating query data with reference atlases without the use of single-cell RNA sequencing (scRNA-seq) data. To enhance annotation accuracy, we incorporated k-nearest neighbors (KNN)-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compared and benchmarked scATAnno against five other published cell annotation approaches, demonstrating its superior performance across multiple datasets and metrics. We further showcased the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cells (PBMCs), triple-negative breast cancer (TNBC), and basal cell carcinoma (BCC), and demonstrated that scATAnno accurately annotates cell types across diverse biological conditions. Overall, scATAnno is a useful tool for scATAC-seq reference atlas construction and cell type annotation and can facilitate the interpretation of new scATAC-seq datasets in complex biological systems. scATAnno is publicly available at https://scatanno-main.readthedocs.io/.

Single-Cell Analysis↗
PubMed2026

MACS3: A Peak-calling Platform for Bulk and Single-cell Regulatory Genomics.

Since the original publication of Model-based Analysis for ChIP-Seq (MACS), the software has been widely used to identify enriched genomic regions in ChIP-seq, ATAC-seq, CUT&RUN, DNase-seq, and related regulatory genomics assays. Over the years, MACS has evolved substantially, with MACS version 3 (MACS3) now serving as the actively maintained implementation. MACS3 preserves the core MACS framework for fragment pileup, dynamic local background noise, statistical enrichment testing, and peak refinement, while adding functionality needed for contemporary bulk and single-cell workflows. It supports conventional bulk peak calling, paired-end and fragment-based file formats, modular signal processing, direct analysis of single-cell ATAC-seq fragment files, barcode-restricted pseudobulk and cluster-level peak calling, specialized ATAC-seq and variant-calling modules, as well as command-line and programmatic interfaces. MACS3 is distributed through standard software channels and supported by continuous testing across operating systems, Python versions, and CPU architectures. Here we describe the architecture, current capabilities, and recommended use of MACS3, providing an updated reference for applying the MACS framework in contemporary bulk and single-cell regulatory genomics workflows. MACS3 is open-source software available at https://github.com/macs3-project/MACS.

Bioinformatics software↗
PubMed2025

CoDIAC: A comprehensive approach for interaction analysis reveals novel insights into SH2 domain function and regulation.

Protein domains are conserved structural and functional units that serve as building blocks of proteins. Through evolutionary expansion, domain families are represented by multiple members in diverse configurations with other domains, evolving new specificities for their interacting partners. Here, we develop a structure-based interface analysis to comprehensively map domain interfaces from experimental and predicted structures, including interfaces with macromolecules and intraprotein interfaces. We hypothesized that comprehensive contact mapping of domains could yield new insights into domain selectivity, conservation of domain-domain interfaces across proteins, and identify conserved post-translational modifications (PTMs), relative to interaction interfaces, allowing for the inference of specific effects due to PTMs or mutations. We applied this approach to the human SH2 domain family, a modular unit central to phosphotyrosine-mediated signaling, identifying a novel approach to understanding binding selectivity and evidence of coordinated regulation of SH2 domain binding interfaces by tyrosine and serine/threonine phosphorylation and acetylation. These findings suggest multiple signaling systems can regulate protein activity and SH2 domain interactions in a coordinated manner. We provide the extensive features of the human SH2 domain family and this modular approach as an open source Python package for COmprehensive Domain Interface Analysis of Contacts (CoDIAC).

SH2 domains↗
PubMed2025

Fast and flexible minimizer digestion with digest.

Minimizer digestion is an increasingly common component of bioinformatics tools, including tools for De Bruijn-Graph assembly and sequence classification. We describe a new open source tool and library to facilitate efficient digestion of genomic sequences. It can produce digests based on the related ideas of minimizers, modimizers or syncmers. Digest uses efficient data structures, scales well to many threads, and produces digests with expected spacings between digested elements. Digest is implemented in C++17 with a Python API, and is available open-source at https://github.com/VeryAmazed/digest.

digestion↗
PubMed2025

Inference and visualization of complex genotype-phenotype maps with gpmap-tools.

Understanding how biological sequences give rise to observable traits, that is, how genotype maps to phenotype, is a central goal in biology. Yet our knowledge of genotype-phenotype maps in natural systems is limited due to the high dimensionality of sequence space and the context-dependent effects of mutations. The emergence of Multiplex assays of variant effect (MAVEs), along with large collections of natural sequences, offer new opportunities to empirically characterize these maps at an unprecedented scale. However, tools for statistical and exploratory analysis of these high-dimensional data are still needed. To address this gap, we developed gpmap-tools (https://github.com/cmarti/gpmap-tools), a python library that integrates a series of models for inference, phenotypic imputation, and error estimation from MAVE data or collections of natural sequences in the presence of genetic interactions of every possible order. gpmap-tools also provides methods for summarizing patterns of epistasis and visualization of genotype-phenotype maps containing up to millions of genotypes. To demonstrate its utility, we used gpmap-tools to infer genotype-phenotype maps containing 262,144 variants of the Shine-Dalgarno sequence from both genomic 5'UTR sequences and experimental MAVE data. Visualization of the inferred landscapes consistently revealed high-fitness ridges that link core motifs at different distances from the start codon. In summary, gpmap-tools provides a flexible, interpretable framework for studying complex genotype-phenotype maps, opening new avenues for understanding the architecture of genetic interactions and their evolutionary consequences.

Gaussian process↗
PubMed2025

DeNoFo: a file format and toolkit for standardised, comparable de novo gene annotation.

MOTIVATION: De novo genes emerge from previously non-coding regions of the genome, challenging the traditional view that new genes primarily arise through duplication and adaptation of existing ones. Characterised by their rapid evolution and their novel structural properties or functional roles, de novo genes represent a young area of research. Therefore, the field currently lacks established standards and methodologies, leading to inconsistent terminology and challenges in comparing and reproducing results. RESULTS: This work presents a standardised annotation format to document the methodology of de novo gene datasets in a reproducible way. We developed DeNoFo, a toolkit to provide easy access to this format that simplifies annotation of datasets and facilitates comparison across studies. Unifying the different protocols and methods in one standardised format, while providing integration into established file formats, such as fasta or gff, ensures comparability of studies and advances new insights in this rapidly evolving field. AVAILABILITY AND IMPLEMENTATION: DeNoFo is available through the official Python Package Index (PyPI) and at https://github.com/EDohmen/denofo . All tools have a graphical user interface and a command line interface. The toolkit is implemented in Python3, available for all major platforms and installable with pip and uv.

Journal Article↗
PubMed2025

Mapping Allosteric Communication in the Nucleosome with Conditional Activity.

The nucleosome core particle (NCP) regulates genome accessibility through dynamic allosteric communication between histone proteins and DNA. Building on the concept of conditional activity introduced by Lin (2016), we use molecular dynamics simulations and develop an open-source Python library, CONDACT (CONDitional ACTivity), to quantify time-resolved kinetic correlations in nucleosome systems. We analyze long-time simulations of the nucleosome core particle, including two different DNA sequences, the Widom-601 and ASP (alpha-satellite palindromic) sequences. By tracking dihedral angle transitions, we identify residues with high dynamical memory and map inter-residue communication pathways across histone subunits and DNA. Our analysis reveals kinetically connected domains involving post-translational modification sites, oncogenic mutation sites, and DNA contact regions, with dynamic coupling observed over distances up to 7.5 nm. These findings offer new insight into the long-range allosteric behavior of the nucleosome and its potential role in regulating chromatin accessibility. Quantifying this allosteric behavior potentially identifies targetable residues and domains for therapeutic intervention.

Journal Article↗
PubMed2025

A Systematic Review of Spatial Epidemiological Modeling Approaches Applied During the COVID-19 Pandemic.

BACKGROUND: A wide range of epidemiological modeling approaches have been applied to the SARS-CoV-2 pandemic, which presents an opportunity to assess common approaches applied to specific research questions. Spatial models interrogate how heterogeneities and host movement dynamics influence local and regional patterns of disease, issues that were of great interest for understanding and controlling SARS-CoV-2. OBJECTIVE: Here we present a systematic review of spatial epidemiological modeling approaches of SARS-CoV-2. We describe common themes and highlight unique strategies, providing a foundation for researchers to devise spatial models most appropriate for future pathogens and epidemics. Our review also categorizes the research questions that were addressed with spatial models, highlights parameter estimation techniques, and describes the cyber infrastructure used for model development. METHODS: We conducted a systematic review using Web of Science and a standardized set of keywords, followed by thorough examination of abstracts and full texts to determine which studies met our inclusion criteria. To guide our description and comparisons of models, we developed a Geography, Population, Movement (GPM) framework that conceptualizes the interactions between three distinct subcomponents of any spatial model. The geographic model represents the physical arena in which the model is implemented, the intra-population model describes the transmission and disease processes that occur within distinct spatial units of the geography, and the movement model describes the algorithms that dictate how hosts move among spatial units within the geography. RESULTS: The search identified a total of 193 articles, of which 109 were included in our review. The most abundant intra-population modeling methods were agent-based (47.7%) and compartmental modeling (29.4%) approaches. Movement models ranged in complexity, with the most complex models implementing commuter movement among many points of interest in the geographic arena, which were sometimes parameterized by fine-scale mobility data. Geographic models ranged from describing microcosms, such as single classrooms, all the way up to multi-country models. Of the 63.3% of models studies that specified the programming language used, we detected ten different languages, with Matlab and Python being the most frequent, although only 30.6% of studies provided open-access code for their models. We also described eight specialized software systems that were used to construct agent-based or compartment models of COVID-19. CONCLUSIONS: Our review identified and characterized a variety of spatial modeling strategies and software that were usefully employed to address many relevant epidemiological questions for COVID-19. Future research is needed to quantitatively assess which modeling approaches are most appropriate in specific situations, to answer specific questions, or to apply to certain disease systems. Moreover, future cyberinfrastructure could help to modularize and standardize modeling approaches, which would increase transparency and reproducibility, and which would facilitate a detailed examination of which model attributes relate to model performance in a variety of contexts.

COVID-19↗
PubMed1990

Shed snake skin and hairless mouse skin as model membranes for human skin during permeation studies.

Difficulties in obtaining and using human skin have tempted many workers to employ animal membranes for percutaneous absorption studies. We have investigated the suitability of two species of snake (Elaphe obsoleta, Python molurus) for this purpose and compared our in vitro experimental results for human skin and for hairless mouse, a currently popular model. The effects of long-term hydration on the membranes were investigated over 8 d using tritiated water as a model permeant. The initial permeability coefficients of all the membranes were similar (0.74-2.2 X 10(-3) cm 2h-1). Although the human and squamate skins did not change significantly over the test period, the permeability of hairless mouse skin increased 37 times. The actions of typical enhancers on the permeabilities of the membranes to a model penetrant 5-fluorouracil (5-FU) were tested using 3% Azone in Tween 20/saline, propylene glycol (PG), 2% Azone in PG, and 5% oleic acid in PG. While the data from snake membranes tended to underestimate the enhancer effects, those from hairless mouse skin greatly overestimated the changes. None of the membranes was a completely reliable model for assessing human percutaneous absorption as modified by accelerants. Pretreatment with acetone did not significantly change the permeability of human or squamate skins to 5-FU, although that of hairless mouse increased twentyfold. An overall conclusion is that, wherever possible, human skin should be used in absorption studies and not hairless mouse or snake skin; otherwise, misleading results may be obtained.

Acetone↗
PubMed1975

Human infestation by Ophionyssus natricis snake mite.

A family presented with a papular vesiculo-bullous eruption of the skin, found to be caused by the snake mite, Ophionyssus natricis (Cervais, 1844). A pet python was the primary host. Treatment of the animal and its environment led to clearance of the human skin lesions.

Adult↗