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PubMed2026

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 (PDB ID: 3LZ0) and ASP (alpha-satellite palindromic) sequences (PDB ID: 1KX5). 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.

Nucleosomes↗

PubMed2026

7-day longitudinal proteomics of critically ill patients: a pilot study.

An adult's health, indicated by measurable parameters, is stable over time. With the exception of circadian rhythms, variability in these parameters typically does not exceed 20%. In this pilot study, we looked into the stability of proteome in intensive care unit (ICU) patients. This was a single-center, prospective, observational pilot study of blood plasma from adult ICU patients with statistically heterogeneous patterns of clinically observed parameters. Eight week-long batches from seven patients (one patient participated twice) were analyzed by means of bottom-up proteomics. The data were analyzed with MaxQuant software against reference proteome. The obtained intensities were further processed with in-house R and Python scripts. In total, 218 proteins were identified; however, only 68 proteins appeared in all samples from all patients. Most proteins remained stable within observation (within-patient variance was less than 30%). The random-effects model also confirmed high impact of within-patient variance on the protein levels. The effects of time on the protein level variances did not exceed 5%. Z-score-based hierarchical clustering analysis revealed that the daily data of each patient were clustered together indicating that the plasma proteome of ICU patients both bears individual traits and remains stable during short-term progression of the patients' condition. Therefore, in this pilot group of patients, the analysis over seven consecutive days fails to reveal proteome dynamics.

Humans↗

PubMed2026

Fedflow: cloud orchestration for federated learning with the FeatureCloud platform.

MOTIVATION: Federated learning (FL) enables collaborative model training on geographically distributed genomic and clinical datasets while complying with data privacy laws and regulatory constraints. FeatureCloud is an existing platform for FL that provides an accessible web-based interface and a large repository of implemented methods. However, due to its graphical interface, FeatureCloud requires manual interaction of all participants, limiting automation, iteration, and reproducibility. RESULTS: We introduce fedflow, a Python-based command-line tool for headless orchestration of FL tasks with FeatureCloud. This tool uses distributed computing resources such as virtual machines or cloud instances to automate such workflows. This allows for scalable federated computing either in local simulations or deployed in a trusted environment. Further, we demonstrate how fedflow can be used to integrate FeatureCloud in reproducible Snakemake workflows. For this, we reanalyse a metagenomic dataset with two federated algorithms and compare the results to the centralized approach with pooled data. Overall, fedflow enables automation of multi-client FL tasks, facilitates embedding of FeatureCloud in standard bioinformatics pipelines and thereby helps increase reproducibility. AVAILABILITY: Fedflow is open-source and available at https://github.com/W-L/fedflow.

Journal Article↗

PubMed2025

mettannotator: a comprehensive and scalable Nextflow annotation pipeline for prokaryotic assemblies.

SUMMARY: In recent years, there has been a surge in prokaryotic genome assemblies, coming from both isolated organisms and environmental samples. These assemblies often include novel species that are poorly represented in reference databases creating a need for a tool that can annotate both well-described and novel taxa, and can run at scale. Here, we present mettannotator-a comprehensive, scalable Nextflow pipeline for prokaryotic genome annotation that identifies coding and noncoding regions, predicts protein functions, including antimicrobial resistance, and delineates gene clusters. The pipeline summarizes these results in a GFF (General Feature Format) file that can be easily utilized in downstream analysis or visualized using common genome browsers. Here, we show how it works on 200 genomes from 29 prokaryotic phyla, including isolate genomes and known and novel metagenome-assembled genomes, and present metrics on its performance in comparison to other tools. AVAILABILITY AND IMPLEMENTATION: The pipeline is written in Nextflow and Python and published under an open source Apache 2.0 licence. Instructions and source code can be accessed at https://github.com/EBI-Metagenomics/mettannotator. The pipeline is also available on WorkflowHub: https://workflowhub.eu/workflows/1069.

Software↗

PubMed2025

APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19.

MOTIVATION: Computational analyses of bulk and single-cell omics provide translational insights into complex diseases, such as COVID-19, by revealing molecules, cellular phenotypes, and signalling patterns that contribute to unfavourable clinical outcomes. Current in silico approaches dovetail differential abundance, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking. RESULTS: We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically informed sparse deep learning model, to perform explainable predictions for COVID-19 severity. The APNet driver-pathway network ingests SJARACNe co-regulation and classification weights to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed in single-cell omics and highlighting under-explored biomarker circuitries in COVID-19. AVAILABILITY AND IMPLEMENTATION: APNet's R, Python scripts, and Cytoscape methodologies are available at https://github.com/BiodataAnalysisGroup/APNet.

COVID-19↗

PubMed2025

ESPClust: unsupervised identification of modifiers for the effect size profile in omics association studies.

MOTIVATION: High-throughput omics technologies have revolutionized the identification of associations between individual traits and underlying biological characteristics, but still use 'one effect-size fits all' approaches. While covariates are often used, their potential as effect modifiers often remains unexplored. RESULTS: We propose ESPClust, a novel unsupervised method designed to identify covariates that modify the effect size of associations between sets of omics variables and outcomes. By extending the concept of moderators to encompass multiple exposures, ESPClust analyses the effect size profile (ESP) to identify regions in covariate space with different ESP, enabling the discovery of subpopulations with distinct associations. Applying ESPClust to synthetic data, insulin resistance and COVID-19 symptom manifestation, we demonstrate its versatility and ability to uncover nuanced effect size modifications that traditional analyses may overlook. By integrating information from multiple exposures, ESPClust identifies effect size modifiers in datasets that are too small for traditional univariate stratified analyses. This method provides a robust framework for understanding complex omics data and holds promise for personalised medicine. AVAILABILITY AND IMPLEMENTATION: The source code ESPClust is available at https://github.com/fjpreche/ESPClust.git. It can be installed via Python package repositories as 'pip install ESPClust==1.1.0'.

Humans↗

PubMed2025

NMFProfiler: a multi-omics integration method for samples stratified in groups.

MOTIVATION: The development of high-throughput sequencing enabled the massive production of "omics" data for various applications in biology. By analyzing simultaneously paired datasets collected on the same samples, integrative statistical approaches allow researchers to get a global picture of such systems and to highlight existing relationships between various molecular types and levels. Here, we introduce NMFProfiler, an integrative supervised NMF that accounts for the stratification of samples into groups of biological interest. RESULTS: NMFProfiler was shown to successfully extract signatures characterizing groups with performances comparable to or better than state-of-the-art approaches. In particular, NMFProfiler was used in a clinical study on atopic dermatitis (AD) and to analyze a multi-omic cancer dataset. In the first case, it successfully identified signatures combining known AD protein biomarkers and novel transcriptomic biomarkers. In addition, it was also able to extract signatures significantly associated to cancer survival. AVAILABILITY AND IMPLEMENTATION: NMFProfiler is released as a Python package, NMFProfiler (v0.3.0), available on PyPI.

Humans↗

PubMed2025

HTSinfer: inferring metadata from bulk Illumina RNA-Seq libraries.

SUMMARY: The Sequencing Read Archive is one of the largest and fastest-growing repositories of sequencing data, containing tens of petabytes of sequenced reads. Its data is used by a wide scientific community, often beyond the primary study that generated them. Such analyses rely on accurate metadata concerning the type of experiment and library, as well as the organism from which the sequenced reads were derived. These metadata are typically entered manually by contributors in an error-prone process, and are frequently incomplete. In addition, easy-to-use computational tools that verify the consistency and completeness of metadata describing the libraries to facilitate data reuse, are largely unavailable. Here, we introduce HTSinfer, a Python-based tool to infer metadata directly and solely from bulk RNA-sequencing data generated on Illumina platforms. HTSinfer leverages genome sequence information and diagnostic genes to rapidly and accurately infer the library source and library type, as well as the relative read orientation, 3' adapter sequence and read length statistics. HTSinfer is written in a modular manner, published under a permissible free and open-source license and encourages contributions by the community, enabling easy addition of new functionalities, e.g. for the inference of additional metrics, or the support of different experiment types or sequencing platforms. AVAILABILITY AND IMPLEMENTATION: HTSinfer is released under the Apache License 2.0. Latest code is available via GitHub at https://github.com/zavolanlab/htsinfer, while releases are published on Bioconda. A snapshot of the HTSinfer version described in this article was deposited at Zenodo at 10.5281/zenodo.13985958.

Metadata↗

PubMed2025

MiNEApy: enhancing enrichment network analysis in metabolic networks.

MOTIVATION: Modeling genome-scale metabolic networks (GEMs) helps understand metabolic fluxes in cells at a specific state under defined environmental conditions or perturbations. Elementary flux modes (EFMs) are powerful tools for simplifying complex metabolic networks into smaller, more manageable pathways. However, the enumeration of all EFMs, especially within GEMs, poses significant challenges due to computational complexity. Additionally, traditional EFM approaches often fail to capture essential aspects of metabolism, such as co-factor balancing and by-product generation. The previously developed Minimum Network Enrichment Analysis (MiNEA) method addresses these limitations by enumerating alternative minimal networks for given biomass building blocks and metabolic tasks. MiNEA facilitates a deeper understanding of metabolic task flexibility and context-specific metabolic routes by integrating condition-specific transcriptomics, proteomics, and metabolomics data. This approach offers significant improvements in the analysis of metabolic pathways, providing more comprehensive insights into cellular metabolism. RESULTS: Here, I present MiNEApy, a Python package reimplementation of MiNEA, which computes minimal networks and performs enrichment analysis. I demonstrate the application of MiNEApy on both a small-scale and a genome-scale model of the bacterium Escherichia coli, showcasing its ability to conduct minimal network enrichment analysis using minimal networks and context-specific data. AVAILABILITY AND IMPLEMENTATION: MiNEApy can be accessed at: https://github.com/vpandey-om/mineapy.

Metabolic Networks and Pathways↗

PubMed2025

Tribus: semi-automated discovery of cell identities and phenotypes from multiplexed imaging and proteomic data.

MOTIVATION: Multiplexed imaging and single-cell analysis are increasingly applied to investigate the tissue spatial ecosystems in cancer and other complex diseases. Accurate single-cell phenotyping based on marker combinations is a critical but challenging task due to (i) low reproducibility across experiments with manual thresholding, and, (ii) labor-intensive ground-truth expert annotation required for learning-based methods. RESULTS: We developed Tribus, an interactive knowledge-based classifier for multiplexed images and proteomic datasets that avoids hard-set thresholds and manual labeling. We demonstrated that Tribus recovers fine-grained cell types, matching the gold standard annotations by human experts. Additionally, Tribus can target ambiguous populations and discover phenotypically distinct cell subtypes. Through benchmarking against three similar methods in four public datasets with ground truth labels, we show that Tribus outperforms other methods in accuracy and computational efficiency, reducing runtime by an order of magnitude. Finally, we demonstrate the performance of Tribus in rapid and precise cell phenotyping with two large in-house whole-slide imaging datasets. AVAILABILITY AND IMPLEMENTATION: Tribus is available at https://github.com/farkkilab/tribus as an open-source Python package.

Proteomics↗

PubMed2025

ORCO: Ollivier-Ricci Curvature-Omics-an unsupervised method for analyzing robustness in biological systems.

MOTIVATION: Although recent advanced sequencing technologies have improved the resolution of genomic and proteomic data to better characterize molecular phenotypes, efficient computational tools to analyze and interpret large-scale omic data are still needed. RESULTS: To address this, we have developed a network-based bioinformatic tool called Ollivier-Ricci curvature for omics (ORCO). ORCO incorporates omics data and a network describing biological relationships between the genes or proteins and computes Ollivier-Ricci curvature (ORC) values for individual interactions. ORC is an edge-based measure that assesses network robustness. It captures functional cooperation in gene signaling using a consistent information-passing measure, which can help investigators identify therapeutic targets and key regulatory modules in biological systems. ORC has identified novel insights in multiple cancer types using genomic data and in neurodevelopmental disorders using brain imaging data. This tool is applicable to any data that can be represented as a network. AVAILABILITY AND IMPLEMENTATION: ORCO is an open-source Python package and is publicly available on GitHub at https://github.com/aksimhal/ORC-Omics.

Software↗

PubMed2025

PathoSeq-QC: a decision support bioinformatics workflow for robust genomic surveillance.

MOTIVATION: Recommendations on the use of genomics for pathogens surveillance are evidence that high-throughput genomic sequencing plays a key role to fight global health threats. Coupled with bioinformatics and other data types (e.g., epidemiological information), genomics is used to obtain knowledge on health pathogenic threats and insights on their evolution, to monitor pathogens spread, and to evaluate the effectiveness of countermeasures. From a decision-making policy perspective, it is essential to ensure the entire process's quality before relying on analysis results as evidence. Available workflows usually offer quality assessment tools that are primarily focused on the quality of raw NGS reads but often struggle to keep pace with new technologies and threats, and fail to provide a robust consensus on results, necessitating manual evaluation of multiple tool outputs. RESULTS: We present PathoSeq-QC, a bioinformatics decision support workflow developed to improve the trustworthiness of genomic surveillance analyses and conclusions. Designed for SARS-CoV-2, it is suitable for any viral threat. In the specific case of SARS-CoV-2, PathoSeq-QC: (i) evaluates the quality of the raw data; (ii) assesses whether the analysed sample is composed by single or multiple lineages; (iii) produces robust variant calling results via multi-tool comparison; (iv) reports whether the produced data are in support of a recombinant virus, a novel or an already known lineage. The tool is modular, which will allow easy functionalities extension. AVAILABILITY AND IMPLEMENTATION: PathoSeq-QC is a command-line tool written in Python and R. The code is available at https://code.europa.eu/dighealth/pathoseq-qc.

Genomics↗

PubMed2025

MAFin: motif detection in multiple alignment files.

MOTIVATION: Whole Genome and Proteome Alignments, represented by the multiple alignment file format, have become a standard approach in comparative genomics and proteomics. These often require identifying conserved motifs, which is crucial for understanding functional and evolutionary relationships. However, current approaches lack a direct method for motif detection within MAF files. We present MAFin, a novel tool that enables efficient motif detection and conservation analysis in MAF files to address this gap, streamlining genomic and proteomic research. RESULTS: We developed MAFin, the first motif detection tool for Multiple Alignment Format files. MAFin enables the multithreaded search of conserved motifs using three approaches: (i) using user-specified k-mers to search the sequences. (ii) with regular expressions, in which case one or more patterns are searched, and (iii) with predefined Position Weight Matrices. Once the motif has been found, MAFin detects the motif instances and calculates the conservation across the aligned sequences. MAFin also calculates a conservation percentage, which provides information about the conservation levels of each motif across the aligned sequences, based on the number of matches relative to the length of the motif. A set of statistics enables the interpretation of each motif's conservation level, and the detected motifs are exported in JSON and CSV files for downstream analyses. AVAILABILITY AND IMPLEMENTATION: MAFin is offered as a Python package under the GPL license as a multi-platform application and is available at: https://github.com/Georgakopoulos-Soares-lab/MAFin.

Software↗

PubMed2025

CoverM: read alignment statistics for metagenomics.

SUMMARY: Genome-centric analysis of metagenomic samples is a powerful method for understanding the function of microbial communities. Calculating read coverage is a central part of analysis, enabling differential coverage binning for recovery of genomes and estimation of microbial community composition. Coverage is determined by processing read alignments to reference sequences of either contigs or genomes. Per-reference coverage is typically calculated in an ad-hoc manner, with each software package providing its own implementation and specific definition of coverage. Here we present a unified software package CoverM which calculates several coverage statistics for contigs and genomes in an ergonomic and flexible manner. It uses "Mosdepth arrays" for computational efficiency and avoids unnecessary I/O overhead by calculating coverage statistics from streamed read alignment results. AVAILABILITY AND IMPLEMENTATION: CoverM is free software available at https://github.com/wwood/coverm. CoverM is implemented in Rust, with Python (https://github.com/apcamargo/pycoverm) and Julia (https://github.com/JuliaBinaryWrappers/CoverM_jll.jl) interfaces.

Metabolomics↗

PubMed2025

argNorm: normalization of antibiotic resistance gene annotations to the Antibiotic Resistance Ontology (ARO).

SUMMARY: Currently available and frequently used tools for annotating antibiotic resistance genes (ARGs) in genomes and metagenomes provide results using inconsistent nomenclature. This makes the comparison of different ARG annotation outputs challenging. The comparability of ARG annotation outputs can be improved by mapping gene names and their categories to a common controlled vocabulary such as the Antibiotic Resistance Ontology (ARO). We developed argNorm, a command line tool and Python library, to normalize all detected genes across six ARG annotation tools (eight databases) to the ARO. argNorm also adds information to the outputs using the same ARG categorization so that they are comparable across tools. AVAILABILITY AND IMPLEMENTATION: argNorm is available as an open-source tool at: https://github.com/BigDataBiology/argNorm. It can also be downloaded as a PyPI package and is available on Bioconda and as an nf-core module.

Molecular Sequence Annotation↗

PubMed2025

Bayesian inference of fitness landscapes via tree-structured branching processes.

MOTIVATION: The complex dynamics of cancer evolution, driven by mutation and selection, underlies the molecular heterogeneity observed in tumors. The evolutionary histories of tumors of different patients can be encoded as mutation trees and reconstructed in high resolution from single-cell sequencing data, offering crucial insights for studying fitness effects of and epistasis among mutations. Existing models, however, either fail to separate mutation and selection or neglect the evolutionary histories encoded by the tumor phylogenetic trees. RESULTS: We introduce FiTree, a tree-structured multi-type branching process model with epistatic fitness parameterization and a Bayesian inference scheme to learn fitness landscapes from single-cell tumor mutation trees. Through simulations, we demonstrate that FiTree outperforms state-of-the-art methods in inferring the fitness landscape underlying tumor evolution. Applying FiTree to a single-cell acute myeloid leukemia dataset, we identify epistatic fitness effects consistent with known biological findings and quantify uncertainty in predicting future mutational events. The new model unifies probabilistic graphical models of cancer progression with population genetics, offering a principled framework for understanding tumor evolution and informing therapeutic strategies. AVAILABILITY AND IMPLEMENTATION: The Python package FiTree and the analysis workflows are available at https://github.com/cbg-ethz/FiTree.

Bayes Theorem↗

PubMed2025

Harnessing deep learning for proteome-scale detection of amyloid signaling motifs.

MOTIVATION: Amyloid signaling sequences adopt the cross-β fold that is capable of self-replication in the templating process. Propagation of the amyloid fold from the receptor to the effector protein is used for signal transduction in the immune response pathways in animals, fungi, and bacteria. So far, a dozen of families of amyloid signaling motifs (ASMs) have been classified. Unfortunately, due to the wide variety of ASMs it is difficult to identify them in large protein databases available, which limits the possibility of conducting experimental studies. To date, various deep learning (DL) models have been applied across a range of protein-related tasks, including domain family classification and the prediction of protein structure and protein-protein interactions. RESULTS: In this study, we develop tailor-made bidirectional LSTM and BERT-based architectures to model ASM, and compare their performance against a state-of-the-art machine learning grammatical model. Our research is focused on developing a discriminative model of generalized ASMs, capable of detecting ASMs in large datasets. The DL-based models are trained on a diverse set of motif families and a global negative set, and used to identify ASMs from remotely related families. We analyze how both models represent the data and demonstrate that the DL-based approaches effectively detect ASMs, including novel motifs, even at the genome scale. AVAILABILITY AND IMPLEMENTATION: The models are provided as a Python package, asmscan-bilstm, and a Docker image at https://github.com/chrispysz/asmscan-proteinbert-run. The source code can be accessed at https://github.com/jakub-galazka/asmscan-bilstm and https://github.com/chrispysz/asmscan-proteinbert. Data and results are at https://github.com/wdyrka-pwr/ASMscan.

Deep Learning↗

PubMed2025

KINAID: an orthology-based kinase-substrate prediction and analysis tool for phosphoproteomics.

SUMMARY: Proteome-wide datasets of phosphorylated peptides, either measured in a condition of interest or in response to perturbations, are increasingly becoming available for model organisms across the evolutionary spectrum. We introduce KINAID (KINase Activity and Inference Dashboard), an interactive and extensible tool written in Dash/Plotly, that predicts kinase-substrate interactions, uncovers and displays kinases whose substrates are enriched amongst phosphorylated peptides, interactively illustrates kinase-substrate interactions, and clusters phosphopeptides targeted by similar kinases. KINAID is the first tool of its kind that can analyze data from not only Homo sapiens but also 10 additional model organisms (including Mus musculus, Danio rerio, Drosophila melanogaster, Caenorhabditis elegans, and Saccharomyces cerevisiae). We demonstrate KINAID's utility by applying it to recently published S. cerevisiae phosphoproteomics data. AVAILABILITY AND IMPLEMENTATION: Webserver is available at https://kinaid.princeton.edu; open-source python library is available at https://github.com/Singh-Lab/kinaid; archive is available at https://doi.org/10.24433/CO.8460107.v1.

Proteomics↗