Latest bioRxiv papers

Category: bioinformatics — Showing 50 items

• Forecasting cell state futures from static snapshots

  Luo, E.; Gao, H.; BIAN, H.; Peng, S.; Li, Y.; Li, C.; Hao, M.; Chen, M.; She, Y.; Wei, L.; Liu, K.; Zhang, X.

  Abstract

  Predicting future cell state transitions from static transcriptomic snapshots is vital for understanding and steering cellular dynamics. Although existing trajectory and RNA velocity methods can infer local transitions within observed data, they are fundamentally limited in extrapolating long-range cellular dynamics. Here, we introduce CellTempo, an autoregressive generative framework that enables long-range forecasting of cell state trajectories directly from static transcriptomic snapshots. CellTempo is pretrained on scBaseTraj, a large-scale collection of multi-step cellular transition sequences constructed by integrating diverse experimental technologies and data sources. Across diverse biological systems, CellTempo accurately predicts long-range lineage progression and reconstructs unseen cellular potential landscapes. It further distinguishes transient perturbation responses from long-term cell-fate changes, enabling prioritization of candidate compounds for cell-fate engineering. These results demonstrate that long-range cellular dynamics are inferable from static observations, establishing a foundation for predictive and controllable modeling of cell fate.

  bioinformatics2026-07-31v2

• Cross-Attention Over RNA And Protein Sequences Enables Generalizable Interaction Prediction

  Catalano, M.; Pepe, G.; Appierdo, R.; Ausiello, G.; McWhite, C.; Gambosi, G.; Helmer-Citterich, M.; Gherardini, P. F.

  Abstract

  Computational predictions are essential to characterize the RNA-protein interaction landscape, yet a persistent gap between benchmark performance and practical utility suggests that current models have limited generalization capabilities. To address this issue, we present CORAL (Cross-attention for RNA-protein Association Learning), a deep learning framework for the prediction of RNA-protein interactions that integrates pretrained protein (ESM-2) and RNA (DNABERT2) language models through bidirectional cross-attention with Low-Rank Adaptation fine-tuning. We also introduce a benchmarking framework that rigorously addresses the problem of data redundancy between training and test sets, which greatly inflates model performances reported in the literature. To this end we adopt three partitioning strategies of increasing stringency: conventional random splits, pairwise non-redundant splits, and component-wise non-redundant splits. CORAL maintains an F1 score of 0.65 under the most stringent component-wise evaluation, compared to 0.47 for the next-best method retaining discriminative behavior. Interpretability analyses further reveal that the cross-attention mechanism captures biologically meaningful features of molecular recognition at two complementary structural scales: at atomic resolution, specific attention heads systematically attend to structurally defined contact positions, showing 26% elevated attention at interface residues across 309 experimentally resolved complexes (p < 0.001); at the domain level, protein-side attention localizes to annotated RNA-binding domains across 94% of 462 proteins examined (median within-protein Cohen's d {approx} 0.94). Together, these findings establish that current RPI prediction benchmarks substantially inflate performance estimates and demonstrate that cross-modal attention architectures yield improved generalization alongside mechanistically interpretable representations.

  bioinformatics2026-07-31v2

• DPCGS: a computational framework for linking GWAS to single-cell transcriptomics in complex traits and diseases

  Liu, C.; Yuan, B.; Shen, B.; Li, J.; Zhu, R.; Yang, P.; Wu, B.; Xuan, Y.; Yang, S.; Yang, N.; Ma, L.; Liu, Q.; Dai, S.; Zhang, Y.

  Abstract

  Complex traits and diseases arise from the interplay between genetic variation and cellular heterogeneity, making it essential to understand how genetic risk manifests at the cellular level. However, connecting genome-wide association studies (GWAS) to specific cell populations remains challenging due to cellular complexity and the prevalence of noncoding variants. Here, we present DPCGS, a computational framework that systematically integrates GWAS summary statistics with single-cell RNA-sequencing (scRNA-seq) data to identify trait-associated cell subpopulations, genes, and regulatory programs. Unlike existing approaches that primarily evaluate pathway enrichment or cell-type-level associations, DPCGS quantifies the enrichment of genetically prioritized genes within individual cells through a statistically calibrated gene-set scoring strategy, enabling high-resolution mapping of genetic risk to cellular states. Benchmarking across simulated and diverse human single-cell datasets demonstrates that DPCGS achieves superior accuracy, sensitivity, and robustness compared with existing methods, including scDRS and scPagwas. Applying DPCGS to Alzheimer's disease and asthma reveals disease-associated cellular populations and uncovers potential molecular drivers, including CD74, FOS, and AP-1 family regulatory programs, providing insights into disease-specific immune and cellular mechanisms. By bridging genetic discoveries from GWAS with functional interpretation at single-cell resolution, DPCGS establishes a generalizable framework for dissecting the cellular architecture of complex traits and diseases. This approach enables systematic discovery of disease-relevant cell subpopulations, regulatory networks, and potential therapeutic targets, offering broad applications in human genetics, single-cell biology, and precision medicine.

  bioinformatics2026-07-31v2

• ChemoCalib: multiblock PLS calibration of genome-scale metabolic models improves flux prediction over expression-only integration

  zhang, X.

  Abstract

  Constraint-based metabolic modeling (FBA) needs multi-omics integration to narrow flux solution spaces, yet existing expression-only methods (E-Flux, GIMME, iMAT, MOMENT, E-Flux2, SPOT) ignore cross-omics covariance and rely on heuristic thresholds. We present ChemoCalib, a chemometrics-calibrated constraint layer that projects shared latent structure from metabolomics, transcriptomics, and proteomics onto genome-scale reaction bounds via multiblock partial least squares (MB-PLS) with GPR-aware VIP aggregation. Unlike linear expression scaling, ChemoCalib derives continuous, interpretable bounds (soft/hard/adaptive modes) and couples them to an in-silico active-learning loop for virtual double-knockout selection. On E. coli iJO1366, using three public multi-omics datasets (Ishii 2007, Keio, Holm 2010) and 13C-MFA ground truth, ChemoCalib achieves Spearman {rho} = 0.461 and Pearson r = 0.493 against measured fluxes, significantly outperforming E-Flux, GIMME, iMAT, MOMENT, E-Flux2 and SPOT (Holm-corrected p < 0.05 on 3/5 baselines). On the full 20-condition E-Flux2/SPOT curated set (~430 flux measurements, Supplementary Table S3), the gain is retained ({rho} = 0.48, p < 0.01 vs SPOT). FVA solution space contracts 55% with 94.2% wild-type feasibility preserved. A Gaussian-process surrogate quantifies uncertainty (reliability diagram ECE = 0.032). Pentose phosphate path routing shift ({Delta}{rho} = +0.088) aligns with known G6PDH post-transcriptional regulation. ChemoCalib is open source (Zenodo DOI: 10.5281/zenodo.21645890), needs no wet experiment, and positions chemometrics as a calibration layer for virtual-cell metabolic modules.

  bioinformatics2026-07-31v1

• mock-fastq-generator: A synthetic FASTQ generator

  Florez Prada, A.; Hart, D. J.

  Abstract

  Validating bioinformatics pipelines and benchmarking sequence processing algorithms requires reliable test datasets. Existing read simulation tools rely on reference genomes and empirical error profiles, lacking fine-grained control over specific targeted DNA constructs and controlled error injection. mock-fastq-generator is an open-source software suite available both as an installable PyPI Python package and a standalone, client-side web application. It constructs synthetic FASTQ files by combining template constructs with customizable adapter sequences, selectable quality decay functions (Gaussian, Exponential, Sigmoidal), NovaSeq 4-state quality binning, and context-dependent error penalties. The software allows developers to benchmark sequence trimmers, test alignment sensitivity, and execute automated quality control pipelines in test suites without using proprietary or empirical biological data.

  bioinformatics2026-07-31v1

• MitoDate: a Nextflow pipeline for molecular clock dating and phylogenetic inference using ancient mitogenomes

  Li, W.; Sharif, B.; Heintzman, P. D.; Dalen, L.; Chacon-Duque, J. C.

  Abstract

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

  bioinformatics2026-07-31v1

• CyFj11: FlowJo v11 Workspace Import and Legacy Format Export for R-Based Flow Cytometry Analysis

  Jagla, B.; Culina, S.; Le-Guerroue, F.; Karkeni, E.; Hasan, M.

  Abstract

  High-dimensional flow cytometry measures immune cells at single-cell resolution, enabling systematic characterization of cell populations at scale. But harnessing this potential requires seamless interoperability between the interactive gating tools used by biologists and the statistical environments used for detailed downstream analysis. FlowJo, one of the most widely used commercial cytometry analysis software packages, now stores workspaces in a format that existing R tools cannot read, leaving researchers unable to import their gating strategies into R, or to return R-based results to FlowJo for visual review or collaborative sharing, without manual reconstruction. We present CyFj11, an R package that closes this gap, enabling import of FlowJo v11 gating hierarchies into R and export of R-defined gates back to FlowJo (throughout this paper, "import" refers to bringing a FlowJo v11 workspace into R, and "export" to writing an R-derived GatingSet back out to FlowJo). Using a combination of synthetic test scenarios and a real-world immunophenotyping dataset, we show that population counts in FlowJo 10 and 11 matched R-derived values with Pearson correlation coefficients exceeding 0.99. CyFj11 is platform-independent, requires no additional software infrastructure, and is freely available at https://github.com/C3BI-pasteur-fr/CyFj11.

  bioinformatics2026-07-31v1

• An integrated single-cell atlas of human lung across the lifespan

  Huang, L.; Huang, Z.; Feng, S.; Fang, K.; Wu, J.; Ao, Y.; Huang, L.; Zhang, J.; Sun, H.; Miao, Z.

  Abstract

  The healthy adult lung is largely quiescent but can respond to injury and replace damaged or lost cells. However, the identities and origins of regenerative cell states remain poorly understood. Here we present the Human Developmental Lung Cell Atlas (HDLCA), a curated single-cell reference atlas of the respiratory system across the lifespan, integrating 253 datasets from 225 studies, and comprising over 18 million cells from 3,198 samples and 2,460 individuals across 34 anatomical locations in both health and disease. The HDLCA defines 142 consensus lung cell types, including 13 rare and 8 previously undescribed ones. Notably, we identify a rare intermediate alveolar epithelial progenitor (Int AP) population in normal lungs, with transcriptional signatures shared by alveolar type 2 (AT2) and alveolar type 1 (AT1) cells. Trajectory inference indicates that Int AP cells arise from both AT2 and SCGB1A1+ secretory cells and give rise to AT1 cells, with Hippo-YAP/TAZ signalling governing lineage specification. Int AP cells are associated with genetic susceptibility to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease, and their dysfunction may contribute to abnormal alveolar repair and regenerative failure in both diseases. The HDLCA provides a unified atlas-level reference for mapping respiratory cellular diversity, uncovering reparative cell dynamics and informing regenerative and cell-based therapeutic strategies.

  bioinformatics2026-07-30v2

• Determining gene specificity from multivariate single-cell RNA sequencing data

  Swarna, N. P.; Booeshaghi, A. S.; Rebboah, E.; Gordon, M. G.; Kathail, P.; Li, T.; Alvarez, M.; Ye, C. J.; Wold, B. J.; Mortazavi, A.; Pachter, L.

  Abstract

  An important application of single-cell genomics experiments is to identify genes specific to biological categories or experimental conditions. Although numerous approaches have been proposed to identify such genes, we consider an axiomatic approach based on defining properties that a specificity measure should have. This leads us to develop ember (Entropy Metrics for Biological ExploRation), which we show is the only method satisfying four key desired properties for a specificity measure. Applying ember to eight tissues from eight founder mouse strains, we find that gene specificity is often unintuitive: canonical markers can be supplanted, housekeeping genes are context-dependent, and mouse strain can drive unexpected cell type switching. Unsupervised learning on entropy metrics uncovers shared genes specialized to male gonads and kidney, as well as genes specific to non-consecutive developmental stages in the kidney. To facilitate further exploration of gene specificity in mice, we have also developed a comprehensive specificity database, along with a web interface, API and MCP server. Extending ember to a human PBMC dataset collected from 255 diverse individuals, we find that variation in PBMCs is largely localized to classical monocytes. We also find genes with unique specificity by sex, age and ancestral background. Together, these applications establish ember as a powerful tool and provide a roadmap for elucidating the impact of human genetic variation using the murine model.

  bioinformatics2026-07-30v2

• AI4Life Open Calls and Public Challenges: why, how, and what we have learned.

  Galinova, V.; Seifi, M.; Serrano Solano, B.; Lidayova, K.; Dalle Nogare, D.; Corbat, A. A.; Talks, J.; Giacomello, E.; Gomez-de-Mariscal, E.; Ferreira, M. G.; Fuster-Barcelo, C.; Battagliotti, J. M.; Garcia-Lopez-de-Haro, C.; Salmon, B.; Croft, M.; Yie, S. Y.; Rey-Paniagua, G.; Hu, X.; Cho, S.; Sheth, A.; Porwal, C.; Li, X.; AI4Life Consortium, ; Henriques, R.; Li, X.; Krull, A.; Klemm, A.; Munoz Barrutia, A.; Kreshuk, A.; Ouyang, W.; Jug, F.; Deschamps, J.

  Abstract

  Within AI4Life, we ran three Open Calls and three Public Challenges (2023-2025), supporting 22 bioimage analysis projects from 151 applications and engaging 225 challenge participants, with the aim of applying FAIR deep learning in the life sciences. Our experience offers a view of the current state of bioimage analysis, the landscape of available tools, as well as the existing gaps between method developers, tool producers and potential users. It highlights that even after careful selection for AI-ready projects, most still require substantial effort to apply deep learning, and that the field still relies heavily on established, well-rounded methods to solve common problems. We come to the conclusion that for scientific AI in biology, the rate-limiting step is not methods and models but data, annotations, and shared infrastructure underneath them.

  bioinformatics2026-07-30v2

• General-purpose language models integrate structured biological evidence for explainable biological interaction prediction

  Zhang, Y.-z.; Xu, L.; Imoto, S.

  Abstract

  Biological interaction inference often requires integrating heterogeneous evidence that differs in biological meaning, provenance, specificity, and reliability. Existing computational approaches typically compress such evidence into numerical features, aggregate scores or latent representations before prediction, obscuring the contribution of individual evidence records and limiting interpretation when evidence is conflicting or incomplete. Phage--host interaction prediction provides a demanding test case for this problem, requiring the integration of genomic, functional, and reference-derived evidence with differing coverage, specificity, and reliability. Here we introduce a structured-evidence inference paradigm that retains heterogeneous biological observations as modular, named, and experimentally perturbable records rather than compressing them before prediction. We implement this paradigm in PHI-Reason, which constructs structured evidence profiles from genomic annotations, receptor-binding protein homology, nucleotide-neighbour relationships, alignment-free genomic similarity, and available CRISPR spacer information. A general-purpose large language model (LLM) performs inference directly over these structured profiles without task-specific training or manually engineered evidence-fusion rules. To validate this paradigm, we evaluated it across two distinct biological interaction domains: prokaryotic phage--host prediction and eukaryotic virus--host prediction. Across phage--host benchmarks, PHI-Reason achieved species-level top-1 accuracies of 63.6% and 53.2% on RefSeq-634 and VHDB-3150, respectively, and a multi-host accuracy of 0.571 on the Hi-C cohort, outperforming established numerical methods. Besides, systematic evidence perturbations quantified how individual evidence supported, complemented, or misled inference, identifying nucleotide-neighbour context as the dominant signal. Analyses of LLM intermediate representations using a target-conditioned local Jacobian readout, together with output-grounding analyses, further characterized evidence-dependent inference and quantified the extent to which generated rationales departed from the supplied evidence profiles. Applying the same framework to eukaryotic virus--host prediction using domain-appropriate evidence also preserved high predictive accuracy (66.6\%), supporting the generalizability of the proposed paradigm across biological prediction. These results demonstrate that LLMs can serve as an evidence-grounded inference interface, integrating heterogeneous biological evidence while making the boundaries of evidential support directly testable.

  bioinformatics2026-07-30v2

• How Bias Shapes the Leaderboard: Scoring Function Performance Under Scrutiny

  Graber, D.; Kopko, J.; Stockinger, P.; Nakandalage, R.; Kuhn, B.; Mishra, S.

  Abstract

  Structure-based scoring functions leveraging machine learning have recently demonstrated superior performance over classical scoring functions, particularly on virtual screening benchmarks. However, due to the fundamental differences between their underlying model principles and architectures, it remains unclear to what extent performance stems from an understanding of molecular binding or from exploitation of systemic biases. Thus, disentangling the factors underlying benchmark performance is essential for determining whether a scoring function will generalize to novel chemical space and succeed in prospective drug discovery. To address this need, we present a case study investigating the nature and impact of systemic biases on benchmark comparisons between different scoring function paradigms. By systematically analyzing the evaluation workflows of prominent models, we reveal pocket bias, a form of spatial coordinate frame leakage arising from static binding pocket extraction, which artificially inflates benchmark performance. To progressively eliminate these sources of bias, we benchmarked two selected graph neural network scoring functions against two minimalist machine learning models and a classical scoring function under four increasingly stringent evaluation levels, successively removing pocket bias, reducing structural data leakage, and finally evaluating on out-of-distribution (OOD) protein targets. Upon removal of pocket bias and structural data leakage, the performance of all machine learning models dropped substantially. When evaluated on out-of-distribution protein families, the classical baseline AutoDock Vina outperformed the machine learning models in five of seven virtual screening tasks and dominated the docking power evaluation. Our findings indicate that benchmark performance can be heavily shaped by evaluation design and dataset artifacts, potentially overshadowing algorithmic improvements. While the tested machine learning models remain heavily dependent on encountering familiar data distributions to achieve competitive results, AutoDock Vina demonstrated superior generalization capacity on OOD targets. This work underscores the critical need for rigorous, artifact-free benchmarking protocols to guide the development of truly prospective machine learning models for virtual screening.

  bioinformatics2026-07-30v1

• Quantitative assessment of cell fate commitment in single-cell transcriptomics using scCS

  Kriukov, E.; Ivleva, E.; Baranov, P.

  Abstract

  Cell fate trajectory inference is one of the key downstream methods in single-cell RNA-sequencing data analysis. Multiple existing tools allow studying such and help identify continuous cell fate dynamics, important genes along the trajectory, and reconstruct the transcriptional states a cell passes to its estimated final state. These methods have been essential to study various biological systems, yet cell fate by itself currently presents mostly qualitative analysis, and existing tools do not allow to directly quantify the fate-related parameters, including commitment, transition speed, fate affinity and entropy. Such quantifications may be performed through multiple parameters and result in better understanding and description of cell fates. We present scCS (single-cell Commitment Scoring), a scverse-friendly Python framework for this problem. scCS introduces Discounted Future-Fate Propagation (DFFP), which models the source transition graph as a geometrically stopped random walk that can reach endpoint anchors or stop unresolved, with a user-defined finite expected graph horizon. For each cell, the resulting probabilities are separated into total fate reach, relative affinity among reached fates, entropy-based fate specificity and reach-supported resolved commitment, while Signed Ordering Flux independently quantifies local progression. scCS also provides endpoint-anchor, graph-coverage and horizon-sensitivity diagnostics, an instantaneous local-direction mode, standardized visualizations, gene-level analyses and replicate-aware comparisons across experimental conditions. Applications to pancreatic endocrinogenesis and neural crest-Schwann-cell differentiation illustrate the general framework. scCS converts an explicit biological fate hypothesis into auditable cell-, population- and replicate-level quantities without redefining the source dynamics.

  bioinformatics2026-07-30v1

• RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data

  Seal, S.; Neelon, B.; Chakraborty, A.; Mattila, C.; Rubinstein, M.; Chung, D.; Angel, P.; Ghosh, D.

  Abstract

  High-throughput spatial omics technologies enable molecular profiling within intact tissue architecture, yet identifying concise, predictive, and biologically interpretable marker panels for cell types, tissue domains, and disease-associated tissue classes remains challenging. This limitation hinders the development of actionable panels for targeted validation and downstream translation. Existing pipelines rely largely on univariate differential-expression analyses, which ignore joint molecular structure and provide limited predictive insight. Multivariate machine-learning methods, including random forest, XGBoost, elastic net, and specialized single-cell panel-selection approaches, can capture predictive patterns but typically lack explicit spatial modeling and probabilistic feature selection, relying instead on model-specific importance scores or user-specified panel sizes. We develop rapid kernel machine regression (RKMR), a scalable framework for spatial-omics marker discovery that integrates nonlinear kernel modeling, spike-and-slab variable selection, and spatial dependence. RKMR uses automatic relevance determination (ARD) kernels and sparsity-inducing priors to capture nonlinear marker-outcome relationships and implicit feature interactions while producing approximate posterior inclusion probabilities (PIPs) that quantify model-based uncertainty in feature inclusion. To scale inference to large spatial datasets, RKMR combines low-rank kernel approximations with stochastic variational optimization. In simulations, RKMR consistently achieves higher AUPRC than competing methods across a range of molecular-signal and spatial-effect settings. Across spatial transcriptomics and scRNA-seq datasets, RKMR identifies parsimonious marker sets that recover reported cell-type signatures and reproducible tissue-layer markers. These results establish RKMR as a scalable and uncertainty-aware framework for translating high-dimensional spatial omics data into robust, experimentally actionable marker panels.

  bioinformatics2026-07-30v1

• Expanded unbiased population-genomic summary statistics in pixy

  Samuk, K.; Stone, M.; McAuley, E.; Bailey, N. P.; Lucas, G.; Plaza, M.

  Abstract

  Estimates of population-genetic summary statistics are often computed from variant-only VCFs, which commonly omit invariant sites (i.e. sites with only homozygous reference genotypes across all samples). However, these sites are critical for correctly estimating per site statistics such as nucleotide diversity ({pi}) and between population divergence (dxy). Our software pixy addressed this issue by providing support for computing statistics directly from "all-sites" VCFs that encode invariant positions explicitly (Korunes and Samuk 2021). pixy has since been widely adopted and used in a large variety of population genetic studies. Here we present a major update to pixy, which expands the original tool in four major areas. First, we provide implementations of new estimators of Watterson's {theta} and Tajima's D that are unbiased with respect to missing data. Secondly, we provide support for arbitrary ploidy and multiallelic sites. Third, we introduce a variety of optimizations, including multicore execution and a roughly order-of-magnitude reduction in per-worker memory footprint. Finally, we have broadly modernized our code base, test suite, and community contribution pathway. We validate these new features on simulated polyploid and multiallelic datasets, as well as two empirical datasets (one diploid and one autotetraploid), and benchmark the new multicore scaling. We position pixy relative to contemporary tools and discuss shared limitations of VCF-based estimators. By integrating support for diverse ploidies, allelic complexity, and genome-wide scale in one workflow, pixy provides the population genetics community with a straightforward tool for estimates of key summary statistics that are unbiased with respect to missing data. pixy remains completely open-source (MIT-licensed) and easily installable with the conda package management system.

  bioinformatics2026-07-30v1

• Rich structure alphabets enable highest accuracy protein search

  Edgar, R. C.

  Abstract

  Protein structure databases have grown from thousands of experimentally determined structures to hundreds of millions of AI-predicted models, creating an urgent need for search methods that combine high accuracy with practical scalability. Here, I present the third generation of Reseek, a protein structure search algorithm achieving the highest overall accuracy (median rank 1) according to diverse metrics among tested methods including DALI, Foldseek and TM-align. Improved accuracy is obtained by parallel sequence alignment of many discrete alphabets capturing primary, secondary and tertiary features, giving a combined space of ~10^22 possible states. Separate statistical models are optimized for family, superfamily and fold discrimination, respectively, revealing distinct combinations of features that characterize each level. Hundreds of query structures can be searched a against a multimillion-structure database on a server computer in minutes, making large-scale structure search at state-of-the-art accuracy practical on commodity hardware.

  bioinformatics2026-07-30v1

• Resolving Immune Lineage and Cell-State Heterogeneity in Human PBMCs via Mass Spectrometry-Based Single-Cell Proteomics

  O'Connor, S. A.; Gletten, R. B.; Sharma, R.; Jensen, Z. N.; Lovell, B.; Garcia-Mansfield, K.; Ghoda, L. Y.; Zhang, B.; Frankhouser, D. E.; Rockne, R. C.; Trent, J. M.; Marcucci, G.; Pirrotte, P.

  Abstract

  Single-cell proteomics (SCP) currently lacks validated benchmarking standards, and cell annotation often relies on transcriptomic proxies. Unsupervised clustering offers a proxy-free alternative, but its success depends on biological signal outweighing technical variation. In homogeneous samples this is achievable, but in heterogeneous populations, where closely related cell types differ only subtly, technical variation can dominate the clustering and obscure the biology needed for annotation. To address this, we developed an integrated experimental and computational pipeline for protein-level cell annotation and applied it to human PBMCs as an immune-cell test case. We isolated T cells, B cells, monocytes, and NK cells by negative-selection sorting to build a high-fidelity reference. In parallel, unsorted PBMCs from the same donor were processed on a cellenONE and acquired using label-free DIA on an Orbitrap Astral Zoom. Using the labeled reference, we systematically benchmarked normalization, imputation, and clustering methods to assess their effect on cell-type separation. Unsupervised analysis resolved functional subpopulations within each lineage, and a probabilistic SCP classifier trained on these annotations identified the corresponding cell types and states in the unsorted PBMC fraction, validating the pipeline on unenriched, heterogeneous samples. Together, this work delivers an analytically benchmarked SCP workflow that resolves immune lineage and cell-state heterogeneity in human PBMCs and provides a classifier-ready, protein-level reference for immune-cell assignment.

  bioinformatics2026-07-30v1

• Ontology-guided harmonization enables unified discovery of public metabolomics studies within and across repositories

  Banerjee, S.; Jalan, P.; Chinhara, R.; Kalle, C.; Wangikar, P.; Jadhav, K.

  Abstract

  Public metabolomics repositories contain thousands of studies, but differences in metadata structure, vocabulary, and repository-specific terms still limit reliable search, comparison, and reuse within and across databases. Here we present HARMONY, an ontology-based framework and web platform that harmonizes study-level metadata and metabolite information across Metabolomics Workbench and MetaboLights studies. HARMONY resolves eight biological and analytical metadata nodes, including species, sample source, disease, analytical technique, separation method, ion polarity, ionization source, and mass analyzer type, while preserving the original deposited terms as evidence. A ninth node, metabolite identity, maps metabolite entities to RefMet across both repositories. HARMONY uses a two-step workflow: Multi-source extraction retrieves records missed by single-field lookups, and ontology mapping then converts repository-specific labels into shared query terms, substantially closing the cross-repository retrieval gap relative to raw matching. Across the full corpus, HARMONY increased cross-repository retrievability from 75.5% to 89.6%, yielding thousands of study-node retrievals and reconnecting studies that raw-text search would have left unreachable within their own repositories. Approximately 91% of Metabolomics Workbench and 85% of MetaboLights studies had at least six of the eight nodes harmonized. The resulting platform, available at https://omicsinharmony.in, supports ontology-aware search, metadata filtering, within- and cross-repository study comparison, and metabolite-level querying, with retrieval backed by machine learning encoders that map study metadata into shared representations of biological and analytical context. HARMONY provides the metabolomics community with a shared, traceable search interface for study discovery and comparison within and across public repositories.

  bioinformatics2026-07-29v2

• Accurate ab initio gene prediction in eukaryotes with Tiberius in multiple clades

  Gabriel, L.; Bruna, T.; Kaur, A.; Krishnan, A.; Ortmann, F.; Salamov, A.; Talbot, S.; Becker, F.; Krieg, R.; Wheat, C. W.; Grigoriev, I. V.; Stanke, M.; Hoff, K. J.

  Abstract

  Background: Eukaryotic genome annotation is currently bottlenecked by limitations in the generality, scalability or accuracy of computational methods and fewer than 20% of the genomes available at NCBI Datasets have associated gene annotations. Evidence-based pipelines such as BRAKER3 achieve high accuracy but require substantial extrinsic evidence and compute. Deep learning approaches have recently achieved large improvements in ab initio gene prediction accuracy. The end-to-end deep learning gene predictor Tiberius approaches the accuracy of evidence-based annotation on mammalian genomes without using any extrinsic evidence, but its published models were trained on mammals only, limiting its applicability across eukaryotes. Results: We extend Tiberius beyond mammals by training lineage-specific models for Mesangiospermae, Fungi, Vertebrata, Insecta, Chlorophyta and Bacillariophyta, making the tool applicable to 92% of currently available eukaryotic assemblies. Across a benchmark of 33 species, Tiberius achieved higher exon-, transcript- and gene-level accuracy than the other evaluated ab initio methods, Helixer and ANNEVO, improving gene-level F1 score by 12-37 percentage points over Helixer and by 10-22 percentage points over ANNEVO, while also having the fastest runtimes overall. Compared with BRAKER3, which incorporates RNA-Seq and protein evidence, Tiberius approaches state-of-the-art accuracy in Mesangiospermae, Fungi, Bacillariophyta and Chlorophyta, while being on average 80 times faster when using a GPU. A reimplementation of the Tiberius backend reduced runtime by 31% compared to the previous implementation. Tiberius and its models are also available through a web server, which allows annotation of submitted assemblies without local resources. Furthermore, the Vertebrata model has been applied to annotate 2,948 vertebrate assemblies totalling nearly 6 trillion base pairs. Conclusions: Tiberius is transferable well beyond Mammalia and reaches accuracy close to evidence-based annotation in several clades at a small fraction of the compute cost. This makes it a practical choice for highly accurate large-scale genome annotation, particularly when extrinsic evidence is unavailable or annotation throughput is limiting. Availability and implementation: Code: https://github.com/Gaius-Augustus/Tiberius, web server: https://bioinf.uni-greifswald.de/tiberius.

  bioinformatics2026-07-29v2

• The limits of Bayesian estimates of divergence times in measurably evolving populations

  Ivanov, S.; Fosse, S.; dos reis, M.; Duchene, S.

  Abstract

  Bayesian inference of divergence times for extant species using molecular data is an unconventional statistical problem: Divergence times and molecular rates are confounded, and only their product, the molecular branch length, is statistically identifiable. This means we must use priors on times and rates to break the identifiability problem. As a consequence, there is a lower bound in the uncertainty that can be attained under infinite data for estimates of evolutionary timescales using the molecular clock. With infinite data (i.e., an infinite number of sites and loci in the alignment) uncertainty in ages of nodes in phylogenies increases proportionally with their mean age, such that older nodes have higher uncertainty than younger nodes. On the other hand, if extinct taxa are present in the phylogeny, and if their sampling times are known (i.e., `heterochronous' data), then times and rates are identifiable and uncertainties of inferred times and rates go to zero with infinite data. However, in real heterochronous datasets (such as viruses and bacteria), alignments tend to be small and how much uncertainty is present and how it can be reduced as a function of data size are questions that have not been explored. This is clearly important for our understanding of the tempo and mode of microbial evolution using the molecular clock. Here we conducted extensive simulation experiments and analyses of empirical data to develop the infinite-sites theory for heterochronous data. Contrary to expectations, we find that uncertainty in ages of internal nodes scales positively with the distance to their closest tip with known age (i.e., calibration age), not their absolute age. Our results also demonstrate that estimation uncertainty decreases with calibration age more slowly in datasets with more, rather than fewer site patterns, although overall uncertainty is lower in the former. Our statistical framework establishes the minimum uncertainty that can be attained with perfect calibrations and sequence data that are effectively infinitely informative. Finally, we discuss the implications for viral sequence datasets. In a vast majority of cases viral data from outbreaks is not sufficiently informative to display infinite-sites behaviour and thus all estimates of evolutionary timescales will be associated with a degree of uncertainty that will depend on the size of the dataset, its information content, and the complexity of the model. We anticipate that our framework is useful to determine such theoretical limits in empirical analyses of microbial outbreaks.

  bioinformatics2026-07-29v2

• WITHDRAWN: Metabolic Rewiring in Triple-Negative Breast Cancer: Systems Analysis of TCGA-BRCA Transcriptome Reveals Prognostic Hub Genes

  Chandrasekar, S.

  Abstract

  The author has withdrawn this manuscript because of methodological issues. Therefore, the author does not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

  bioinformatics2026-07-29v2

• Single-cell foundation modeling with species-nativeprotein tokens links regenerative competence across frog and mouse

  Cang, H.; Sun, S.

  Abstract

  Appendage regenerative capacity varies dramatically across developmental stages and anatomical sites, yet comparing these functional transitions across species remains difficult because cell types and gene vocabularies diverge. Universal cell embedding (UCE) provides a shared coordinate space across diverse organisms. Here we demonstrate that an experimentally anchored Xenopus laevis tail-organizer contrast identifies a developmental limb transition from which we define a frozen cross-species competence ruler. Applied without refitting, this ruler distinguished regenerative and fibrotic library groups in adult mouse digit repair and resolved a repeatable aligned component within state-balanced macrophage repair populations. Universal cell embeddings thus convert source population contrasts into portable vector rulers, enabling zero-shot functional comparisons across species boundaries.

  bioinformatics2026-07-29v1

• seqproc: An efficient, flexible, and concise tool for sequence geometry description and transformation

  Cape, N.; Fisher, E.; Liu, D.; Patro, R.

  Abstract

  Motivation: Novel sequencing technologies (for example, different single-cell chemistries and protocols) produce complex data. Often, the sequenced reads themselves encode critical technical information, such as the cell or molecule of origin. For effective pre-processing of this data and subsequent downstream analysis, it is required to efficiently and accurately identify, extract, and potentially normalize this information. Results: We introduce seqproc, a general-purpose sequence pre-processing tool based on a concise descriptive grammar to specify sequence matching and transformations. seqproc compiles a user-provided sequence geometry and transformation description into an execution graph, executed by the ANTISEQUENCE library. We demonstrate that \seqproc is faster on most chemistries, substantially more memory efficient, and at least as accurate as alternative tools that provide similar functionality, while having a more concise description syntax. Availability: seqproc is written in Rust and can be executed as a binary program or used as a Rust crate. It is licensed under the BSD 3-clause license and the source code is available at <a href="https://github.com/COMBINE-lab/seqproc">https://github.com/COMBINE-lab/seqproc</a>.

  bioinformatics2026-07-29v1

• UDIST: unsupervised disentanglement of shape and texture for multi-scale phenotypic profiling in 2D microscopy

  Bosch, B. M.; Terpstra, M. L.; Smith, M. B.; van der Steen, K. H.; Jonker, C. T. H.; Ovcinnikovs, V.; Wesselink, T. H.; Janssen, A. F. J.; Winkel, L.; Huigen, E. M. A.; Lefferts, J. W.; Mastrobattista, E.; Elstak, E. D.; van den Berg, C. A. T.; Beekman, J. M.; van Beuningen, S. F. B.

  Abstract

  Microscopy-based phenotypic profiling relies increasingly on autonomous, unsupervised feature extraction, yet no existing method explicitly separates shape from texture into dedicated and independent latent subspaces by architectural design. Therefore texture, encoding critical biological information such as protein distribution and intracellular organisation, remains inaccessible as an independent feature domain in standard unsupervised approaches. This represents a fundamental limitation that prevents unbiased phenotypic analysis across biological scales. Here we introduce UDIST (Unsupervised Disentanglement of Shape and Texture), a sequential dual variational autoencoder (VAE) framework that tackles this fundamental limitation by explicitly decoupling shape from texture into independent, non-overlapping latent subspaces at the single-object level. By training two VICReg-regularised VAEs on principal-axis-aligned objects, UDIST separates binary shape from continuous texture information into rotation-invariant feature spaces, enabling separate downstream analysis of both domains. We validated UDIST across biological scales, from nuclei and single cells to patient-derived intestinal organoids, using both fluorescence and brightfield imaging, revealing phenotypic differences previously hidden by morphological variation and enabling the independent analysis of shape and texture in downstream analyses including clustering and similarity measurements. UDIST provides a versatile, label-free, and unsupervised tool for multi-scale phenotypic profiling in high-content microscopy and screening.

  bioinformatics2026-07-29v1

• Manifold Agentic Reasoning: Extending Agentic POMDPs and Post-Training Reasoning to Riemannian State and Reasoning Spaces

  Xu, T.; Hu, Z.; Sun, X.; Jin, L.; Xiong, M.

  Abstract

  Agentic reasoning systems increasingly interact with environments whose states are only partially observed, dynamically evolving, and constrained by physical, biological, or logical structure. Existing agentic reasoning frameworks often model internal reasoning, tool use, and post-training adaptation using flat latent representations and struggle in curved manifold space environments. However, many scientific and embodied domains naturally lie on curved state spaces, including tissue geometry, developmental trajectories, protein conformations, robotic configuration spaces, and constrained physical systems. We introduce Manifold Agentic Reasoning, a geometric framework that extends agentic reasoning from Euclidean latent spaces to Riemannian manifolds. In the proposed framework, observations are encoded as manifold-valued states, memory is retrieved by geodesic similarity, candidate hypotheses are generated in tangent spaces, predicted transitions are projected by exponential maps, and decisions are admitted through verification-gated commitment or repaired by manifold self-correction. We further extend the framework to graph-agentic manifold reasoning, where node states live on manifolds and neighbor information is transported by logarithmic maps before attention-based aggregation. Manifold agent reasoning moves AI past brittle, prompt-chained templates to solve four critical production flaws: silent hallucinations and reasoning drift, brittle tool and context misuse, the black-box evaluation problem and stiff behavior profiles. To evaluate the framework, we introduce a Curved Tissue Manipulation and Recovery benchmark in which an agent must repair damaged tissue on a curved manifold. Simulated results show that the full manifold-agent substantially outperforms both a baseline reasoning agent and a full flat-agent reasoning system, achieving higher recovery success, lower geodesic shape error, lower pattern error, and fewer invalid transitions. Curvature and ablation studies indicate that the performance gain is driven by geometry-aware reasoning, verification, memory, and self-repair. These results suggest that manifold-aware agentic reasoning provides a principled foundation for reliable scientific and embodied AI systems operating in curved, constrained, or mechanistic domains.

  bioinformatics2026-07-29v1

• Systematic cross-study assessment of RNA-Seq experimental workflows for plasma cell-free transcriptome profiling

  Tuni-Dominguez, C.; Asole, G.; Monteagudo-Mesas, P.; Rusu, E. C.; Cabus, L.; Gonzalez, L.; Sanchez, L.; Neto, B.; Sanders, P.; Weber, M.; Lagarde, J.

  Abstract

  Plasma cell-free RNA (cfRNA) is a promising source of non-invasive biomarkers, but its clinical translation is hindered by technical challenges and a lack of protocol standardization, which compromises reproducibility and comparability across studies. There is a need for a systematic evaluation of existing cfRNA-Seq workflows to understand the drivers of technical variability. Here, we address this gap by performing a comprehensive cross-study analysis of 2,166 cfRNA-Seq samples from 15 published studies and an in-house generated dataset, applying a uniform bioinformatics pipeline to enable a controlled comparison of experimental workflows. Our analysis reveals that the donor phenotype typically explains a negligible fraction of the transcriptomic variation, whose main determinants are technical -- principally protocol choice, genomic DNA contamination levels and library diversity. Remarkably, this technical noise is so profound that variation within plasma cfRNA samples exceeds that found across a wide range of human tissues. Finally, we demonstrate that critical pre-analytical factors are often confounded with patient phenotypes, jeopardizing the validity of biomarker discovery efforts. Our work serves as a comprehensive benchmark of current cfRNA-Seq methodologies and provides evidence-based guidelines to improve experimental design. By highlighting the dominance of controllable technical factors, we offer a path towards more robust and reproducible cfRNA research.

  bioinformatics2026-07-28v5

• Melody: Decoding the Sequence Determinants of Locus Specific DNA Methylation Across Human Tissues

  Jin, J.; Wang, D.; Qiao, J.; Gao, W.; Liu, Y.; Chen, S.; Zou, Q.; Wu, S.; Su, R.; Wei, L.

  Abstract

  DNA methylation is a fundamental epigenetic modification that plays crucial roles in transcriptional regulation, cellular differentiation, and genome stability. However, how locus-specific DNA methylation is determined by intrinsic DNA sequence remains poorly understood. Here, we introduce Melody, a deep learning framework that predicts DNA methylation from 10-kb genomic sequences, enabling the integration of both local and long-range sequence signals. Across 39 human tissues, Melody accurately predicts methylation profiles and consistently outperforms existing state-of-the-art methods in whole-chromosome, hypomethylated-region, and cell-type-specific benchmarks. Melody also generalizes to methylation quantitative trait locus (meQTL) effect prediction and identifies regulatory sequence motifs associated with methylation variability. To extend prediction beyond profiled tissues, we further develop Melody-G, which incorporates single-cell RNA-seq foundation model embeddings to infer methylation states in previously unseen cell types directly from transcriptomic data. Together, Melody provides a unified framework for linking genomic sequence and cellular state to DNA methylation and offers new insights into the regulatory logic governing the human methylome.

  bioinformatics2026-07-28v3

• Identifying spatially variable genes by projecting to morphologically relevant curves

  Nicol, P. B.; Ma, R.; Xu, R. J.; Moffitt, J. R.; Irizarry, R. A.

  Abstract

  Spatial transcriptomics enables high-resolution gene expression measurements while preserving the two-dimensional spatial organization of the biological sample. A common objective in spatial transcriptomics data analysis is to identify spatially variable genes within predefined cell types or regions within the tissue. However, these regions are often implicitly one-dimensional, making standard two-dimensional coordinate-based methods less effective as they overlook the underlying tissue organization. Here we introduce a methodology grounded in spectral graph theory to elucidate a one-dimensional curve that effectively approximates the spatial coordinates of the examined sample. This curve is then used to establish a new coordinate system that reflects tissue morphology. We then develop a generalized additive model (GAM) to estimate spatial patterns which permits the detection of genes with variable expression in the new morphologically relevant coordinate system. Our approach directly models gene counts, thereby eliminating the need for normalization or transformations to satisfy normality assumptions. A second important advantage over existing hypothesis-testing approaches is that our method not only improves performance but also accurately estimates gene expression patterns and precisely pinpoints spatial loci where deviations from constant expression occur. We validate our approach through extensive simulation and by analyzing experimental data from multiple platforms such as Slide-seq and MERFISH. As an example of its ability to enable biological discovery, we demonstrate how our methodology enables the identification of novel interferon-related subpopulations in the mouse mucosa, as well as markers of inflammation-associated fibroblasts in a multi-sample spatial transcriptomic dataset.

  bioinformatics2026-07-28v3

• ProNA3D: Distance-Based Analysis of Nucleic Acid-Containing Interfaces

  Genz, L. R.; Topf, M.

  Abstract

  Biomolecular interactions are central to many essential cellular processes, but RNA-containing complexes remain challenging to resolve structurally, even as experimental methods and AI-based prediction have expanded structural coverage. Tools for the integrated analysis of complex interfaces remain limited. We present ProNA3D, a tool that provides a unified platform for analyzing protein-nucleic acid and nucleic acid-only complexes, bridging the gap between structure prediction and functional interpretation. ProNA3D supports both experimental and computationally predicted structures, incorporating scoring metrics for AlphaFold3 predictions. It also offers interactive two-dimensional interface visualization and secondary-structure topology plots for RNA and DNA. An interface-based density zoning feature facilitates structure analysis in cryo-EM maps, allowing evaluation of dynamic complexes in the context of heterogeneous density. We demonstrate ProNA3D on diverse complexes solved by X-ray crystallography or cryo-EM, as well as on computational models. For example, in a trimeric complex of HIV-1 RNA and a human antibody, ProNA3D identified a high-connectivity nucleotide with potential functional relevance. Applying ProNA3D to the entire Protein Data Bank revealed distinct interface connectivity trends and interaction modes characteristic of specific complexes (e.g., in methyltransferase-DNA and CRISPR-associated) in nucleic acid-containing interfaces. The method is available as both a UCSF ChimeraX plug-in for visualization and a command-line tool at https://gitlab.com/topf-lab/ProNA3D. In addition, the repository contains the results of the large-scale analyses.

  bioinformatics2026-07-28v2

• ProtSyntax: a protein large language model for decoding post-translational modification syntax and function

  Lin, Y.; Wu, J.; You, Z.; Ni, X.; Chang, S.; Ding, J.; Wang, Y.; Gao, X.; Yang, S.

  Abstract

  Post-translational modifications (PTMs) expand protein function by encoding context-dependent regulatory states, and their dysregulation contributes to cancer, neurodegeneration and metabolic disease. However, existing methods treat PTMs as independent residue labels, limiting their ability to distinguish contextually permissible sites, model crosstalk and infer functional consequences. Here we introduce ProtSyntax, a PTM-aware foundation protein language model combining protein-aware positional encoding, bidirectional state-space propagation, geometry-constrained attention and adaptive multi-objective learning. This design integrates residue chemistry, motif order, long-range context and three-dimensional microenvironments while coupling PTM recognition to enzyme function. Across 40 PTM-site benchmarks, ProtSyntax exceeded the strongest baselines in mean MCC and AP by 12.66% and 10.67%. ProtSyntax also recovered masked PTM types and sites, rejected structural decoys, generalized to data-scarce modifications, reconstructed crosstalk and linked PTM perturbations to enzyme kinetics. Applications to pathogenic variants, biomolecular condensates and disease-associated PTM landscapes demonstrate its potential to decode the regulatory language of the modified proteome.

  bioinformatics2026-07-28v2

• TAMIPAMI: Software and methods for PAM/TAM identification in CRISPR and OMEGA gene editing systems

  Orosco, C.; Jain, P. K.; Rivers, A. R.

  Abstract

  Protospacer adjacent motifs (PAMs) and target-adjacent motifs (TAMs) are essential for target recognition by CRISPR-Cas and TnpB nucleases. Here we present TAMIPAMI, an efficient experimental and computational framework for rapid PAM/TAM identification. TAMIPAMI requires only a single control library and Cas or TnpB-treated library, simplifying experimental design, reducing cost, and providing greater accessibility for users. The platform interprets sequencing data with interactive visualizations and introduces a novel algorithm that determines the minimal exact set of degenerate IUPAC sequences describing the observed PAM/TAM patterns. Using this approach, we accurately recovered canonical motifs for several nucleases, including SpCas9, LbCas12a, AsCas12a, BrCas12b, Cas12i1, and AmaTnpB. TAMIPAMI is available as both a web application and command-line tool, ultimately providing an accessible and efficient platform for PAM/TAM discovery and characterization across CRISPR and OMEGA systems.

  bioinformatics2026-07-28v2

• Ordered Gromov-Hausdorff Metric: A New Tool for Comparative Analysis of Protein Structures

  Timofeev, A.; Anufriev, A.

  Abstract

  Motivation: Classical protein structure comparison metrics such as RMSD and TM-score assess geometric similarity via rigid-body superposition and sequence-dependent alignment, but they do not explicitly incorporate the linear order of amino-acid residues into the metric itself. The Gromov-Hausdorff (GH) metric compares metric spaces by their intrinsic shape, yet it also ignores order. Consequently, proteins with rearranged domains can appear geometrically similar under GH even when their evolutionary relationship is ambiguous. We introduce the Ordered Gromov-Hausdorff (OGH) metric, defined on linearly ordered metric spaces via optimal order-preserving isometric embeddings into a common host space, to rigorously incorporate residue order into structural comparison. Results: The theoretical OGH distance satisfies all metric axioms for finite ordered spaces (non-negativity, symmetry, identity of indiscernibles, and the triangle inequality), proved by standard metric-gluing arguments. For computation, we introduce an efficient surrogate based on a greedy monotonic alignment with an exponential order-penalty; we prove that any monotone alignment produced by the surrogate induces a monotone correspondence whose distortion rigorously bounds the theoretical distance from above (Lemma 2.2.5). The algorithm runs in O(n*w), where w is the search window width. Analytical properties include invariance under rigid-body motions, upper boundedness, Lipschitz continuity under small coordinate perturbations, and concavity in the weight parameter . On the Viral Affinity Dataset (28 viral proteins from HIV-1, SARS-CoV-2 and MERS-CoV), the practical OGH score increases monotonically with residue shuffling (up to 0.363 at 100% shuffling) and correlates strongly with TM-score (r=0.706). In the task of separating homologs at fixed global structural similarity (TM-score {approx}0.5), OGH achieves "AUC"=0.800, whereas TM-score gives "AUC"=0.467, demonstrating that order conservation is a more reliable signal of evolutionary relatedness than global geometry alone in the twilight zone of homology. Availability: The Python source code for OGH is freely available at https://github.com/andytimoffilim/OGH. The VAD dataset (PDB IDs listed in the paper) is publicly accessible from the RCSB Protein Data Bank [1,2].

  bioinformatics2026-07-28v2

• PanTEon: a cross-kingdom framework to guide the design of transposable element classifiers

  Orozco-Arias, S.; Ferrer-Pomer, I.; Rodrigues de Goes, F.; Gaviria-Orrego, S.; Gomiz-Fernandez, J.; Llatser-Torres, J.; Paschoal, A. R.; Guyot, r.; Gabaldon, T.

  Abstract

  Transposable elements (TEs) are major drivers of genome evolution, yet their annotation and classification remain inconsistent and hard to reproduce across species. Fragmented repeats, lineage-specific innovations, and heterogeneous taxonomies across databases and tools complicate comparisons and slow progress in TE biology. To address this, we developed PanTEon, a cross-kingdom deep learning framework for reproducible TE classification that combines a harmonized database with an open, modular benchmarking platform. The PanTEon Database is an automatically curated, taxonomically broad TE repository spanning animals, plants, and fungi. The PanTEon platform standardizes training, evaluation, and inference across nine Machine Learning methods, while remaining extensible to user-defined architectures. Using this framework, we benchmark state-of-the-art Machine Learning-based TE classifiers across TE superfamilies and major eukaryotic lineages and find that performance varies markedly by kingdom and superfamily. Ensemble approaches and phylum-specific models improve predictive F1 scores, but cross-species generalization remains a major challenge. Together, PanTEon Database and PanTEon platform provide a reproducible, scalable, and extensible foundation for TE classification, enabling standardized evaluation of future AI methods and supporting community-driven annotation efforts.

  bioinformatics2026-07-28v2

• SQUARNA: stem maximization for accurate de novo RNA secondary structure prediction

  Serdakov, M. D.; Bohdan, D. R.; Nikolaev, G. I.; Bujnicki, J. M.; Baulin, E. F.

  Abstract

  Non-coding RNAs play diverse roles in a wide range of cellular processes, with their spatial structure being pivotal to their function. RNA secondary structure is a key determinant of its overall fold. Given the scarcity of experimentally determined RNA 3D structures, understanding secondary structure is vital for discerning RNA function. Currently, there is no universally effective solution for de novo RNA secondary structure prediction. Existing methods are becoming increasingly complex without marked improvements in accuracy and often overlook critical features such as pseudoknots and alternative folds. Here, we introduce SQUARNA, a new approach to de novo RNA secondary structure prediction that is suitable for both individual RNA analysis and large-scale structural searches. SQUARNA revisits the concept of base pair maximization and develops it into a stem maximization idea coupled with the widely used free energy minimization (MFE) framework. SQUARNA can predict alternative structures and handle pseudoknots of arbitrary complexity. Benchmarking shows that SQUARNA outperforms existing methods, including deep learning models, in both single-sequence and alignment-based RNA secondary structure prediction. SQUARNA seamlessly integrates sequence and alignment information with experimental data, such as residue reactivities obtained by chemical probing, as well as other structural restraints, including automated searches for Rfam database templates, G-quadruplex patterns, and protein-binding motifs. SQUARNA is available as a standalone tool at https://github.com/febos/SQUARNA and as a web server at https://larnal.imol.institute.

  bioinformatics2026-07-28v2

• Pinc: a simple probabilistic AlphaFold interaction score

  Badonyi, M.; Toth-Petroczy, A.

  Abstract

  AlphaFold has enabled large-scale prediction of protein-protein and protein-nucleic acid complexes, but ranking and assessing the quality of predicted models remain challenging. Existing confidence scores are often highly parametrised and provide limited interpretability. We introduce a simple geometric framework that converts AlphaFold predicted aligned error (PAE) into conditional contact probability. We show that these probabilities are well calibrated to the fraction of native contacts observed across experimentally determined structures. Motivated by this, we define the Pinc score (Probability of interface native contacts) as the mean contact probability between interacting chains. Because the probabilistic interpretation extends to individual residues, Pinc captures local structural constraint beyond interfacial burial, enabling residue-level prioritisation of hotspot positions for mutational studies. Depending solely on a single empirically fixed contact radius, Pinc offers an interpretable path from PAE to interface confidence, matching or exceeding the classification performance of more complex methods across five independent benchmark sets. We provide a portable, dependency-free C program and a Google Colab notebook for calculating Pinc scores for AlphaFold models at https://git.mpi-cbg.de/tothpetroczylab/Pinc.

  bioinformatics2026-07-28v2

• A Preparation-Free Mixture-of-Experts Framework for Protein-Ligand Affinity Prediction

  Bao, H.; Dong, S.

  Abstract

  Protein-ligand affinity (PLA) prediction is central to AI-driven drug discovery, but precise interaction-based methods require costly conformation preparation and data encoding, limiting their throughput. To reconcile accuracy with efficiency, we first investigate whether pre-trained molecular representation models can replace complex encoders. A unified and diverse assessment of sequence-, graph-, and image-based representations reveals both strong overall performance and family-wise variability, delivering the first practical guidance for encoder selection in PLA tasks. Next, to achieve high computational efficiency without sacrificing expressiveness, we adopt the mixture-of-experts (MoE) strategy from large language models. Systematic ablation studies uncover key design principles for deploying MoE in molecular prediction. The resulting model, HydrAffinity, is an interaction-free, dynamic sparse model that uses pre-trained encoders and MoE for parameter-efficient learning. It outperforms all interaction-free methods and matches state-of-the-art interaction-based methods on CASF-2016. Routing analysis confirms that MoE develops distinct, family-specific activation patterns, providing interpretable evidence of dynamic parameterization across protein classes. Zero-shot tests on DUDE-Z and LIT-PCBA further show strong EF5% performance, making HydrAffinity a practical, scalable solution acting as an effective early-stage pre-filter.

  bioinformatics2026-07-28v1

• Image-based Disease-wide Association Study via Self-supervised Learning links Abdominal MRI Features to 158 Diseases

  Mousavi, S.; Arora, J.; Agarwal, R.; Fischer, A.; Mueller, S.; Ding, Z.; de Jong, J.; Yuan, K.

  Abstract

  Medical images contain rich phenotypic information that is often not fully captured by manual clinical assessment. Here, we present a systematic framework for extracting such information and conducting image-based disease-wide association studies (iDWAS) using self-supervised learning (SSL). We applied this framework to abdominal MRI data from the UK Biobank and evaluated associations between MRI-derived features and diseases. Features were learned using a VICReg-based SSL model and tested for disease associations using logistic regression models. We identified 158 diseases that were significantly associated with the MRI-derived features, including the ones which are not directly linked to abdominal anatomy. Focusing on metabolic dysfunction-associated steatohepatitis (MASH), we showed that the MRI-derived features captured disease-relevant information and separated MASH cases from controls better than established biomarkers such as PDFF and Iron-cT1. These findings highlight the ability of SSL to uncover clinically meaningful signals from routine imaging data. The proposed framework is broadly applicable to other imaging datasets and modalities, enabling more systematic approaches to incidental and early disease detection. The trained model and analysis pipeline are publicly available at https://github.com/srm2022/iDWAS.

  bioinformatics2026-07-28v1

• A Curvature Guided Composite Kernel Framework for Differential Gene Selection in Cancer Transcriptomics

  Gupta, M.; Sarkar, A. P.

  Abstract

  Identification of differentially expressed genes is a crucial step for downstream tasks on gene data such as biomarker discovery, drug target identification. Traditional Methods assume negative binomial distribution on RNA-sequence data and models the DEGs using either generalized linear models or by estimating dispersion and assumption of mean-variance rate. The proposed method uses axiomatic approach by using quantum mechanics principles to project transcript data onto a Hilbert space using a composite kernel. Using the curvature generated by the transcripts on the latent manifold within the Hilbert space, a gravitational search inspired mechanism is used to identify the optimal number of differentially expressed genes by minimizing a representational loss function, and a reduced gene feature space is constructed as the potential differentially expressed genes. The proposed method has been compared with existing empirical methods for validation using proper statistical and biological benchmark analysis.

  bioinformatics2026-07-28v1

• scINTILLA: Single-Cell Integrated Inference, Labelling, and Landscape Analysis for Cell-Type Annotation Quality Assessment

  Kanannejad, S.; Bongiorni, N.; Nordera, E.; Redaelli, S.; Rusconi, I.; Zanin, R.; Giustacchini, A.; Chatterjee, S.

  Abstract

  Single-cell RNA sequencing has enabled the construction of comprehensive cell atlases, yet the quality and coherence of the cell-type annotations within these atlases remain largely unexamined. When a label is applied to a transcriptionally heterogeneous population, the downstream analyses that depend on it, and automated label transfer in particular, become unreliable. We present scINTILLA (Single-Cell Integrated Inference, Labelling, and Landscape Analysis), a computational framework that combines supervised and unsupervised machine learning to score the learnability and internal consistency of cell-type labels in single-cell datasets. The unsupervised arm benchmarks a broad panel of clustering algorithms and derives a neighbourhood confusion score for every cell, whilst the supervised arm trains up to twelve classifiers and extracts prediction agreement, entropy, and confidence. These signals are normalised and aggregated into a single composite score per cell type, where a low score flags label ambiguity or concealed heterogeneity. As a by-product, scINTILLA also reports which clustering and classification algorithms perform best on a given dataset, offering practical guidance for downstream label transfer. We applied it to five Human Cell Atlas datasets spanning the adult brain, lung, eye, and two organoid atlases, and recovered clear differences in the learnability and internal consistency of annotations across atlases that were not driven by the number of annotated cell types. Focused re-analysis of low-scoring populations in the lung and endoderm-organoid atlases resolved biologically coherent sub-populations, in some cases with context-specific enrichment, much of it recovered from cells that had been assigned broad or catch-all labels. scINTILLA is advisory rather than prescriptive, guiding principled, data-driven re-annotation at atlas scale.

  bioinformatics2026-07-28v1

• Bayesian Network Analysis Identifies PREX1 as a Master Regulator of Cytoskeletal Disruption in Inflammatory Bowel Disease

  Medvedeva, S.; Kulygina, J.; Morozova, K.; Popova, J.; Struchalin, M.; Osipenko, M.; Kozhevnikova, E.

  Abstract

  Background: Inflammatory bowel diseases are complex multifactorial and polygenic conditions with incompletely understood etiology. Transcriptomic and metabolomic studies reveal hundreds of genes and metabolites associated with the inflammatory dowel diseases (IBD), while about 240 genetic loci are associated with these diseases. IBD are accompanied by numerous epithelial lesions including epithelial cells damage and barrier dysfunction. Recent studies suggest that cytoskeleton dysregulation underlies these aspects of IBD. We have previously shown that the disruption of cortical actin filaments and microvilli degeneration are characteristic of Muc2 mouse IBD model. Methods: We used transmission electron microscopy to evaluate ultrastructural defects in the intestinal epithelium of IBD patients. A probabilistic graphical model (Bayesian network) was employed in order to identify the key gene network hierarchy that might define cytoskeletal architecture among other regulatory events in IBD. The Bayesian network was based on preselected key node genes from open access transcriptomic data obtained in patients with Crohn's disease. Results: Electron microscopy of IBD patients demonstrated disruption of the brush border ultrastructure, similar to the microvillar defects previously described in Muc2 colitis model mice. We highlighted a list of genes contributing to actin polymerization and bundling, of which PREX1 were further confirmed using the transcriptome data obtained from Muc2 mice. Conclusion: Our study underscores the utility of Bayesian network analysis in complex cellular phenotypes that helped to identify potential target genes responsible for cortical cytoskeleton and microvilli disruption upon chronic intestinal inflammation.

  bioinformatics2026-07-28v1

• Prioritizing Non-coding Variants in Rice GWAS Loci with a Chromatin-Informed DNA Language Model

  Shrestha, A. M. S.; Manlapaz, J. P.

  Abstract

  Numerous genome-wide association studies in rice have identified loci associated with diverse agronomic traits. However, interpreting the regulatory and functional significance of these loci remains challenging because each locus often contains multiple variants in linkage disequilibrium, many of which lie in non-coding regions. Here, we present a method for prioritizing non-coding variants within an associated locus by integrating chromatin feature information predicted by a pretrained DNA language model fine-tuned on rice ChIP-seq and ATAC-seq datasets. We demonstrate the utility of our method through three case studies. In a post-GWAS analysis of heat tolerance, prioritized variants overlapped promoters of candidate genes previously identified through integrated GWAS and transcriptomic analyses, providing independent support for their potential regulatory roles. For the high-yield gene DEP1, promoter variant prioritization combined with in silico saturation mutagenesis identified a localized regulatory region enriched for high-impact mutations overlapping predicted transcription factor binding sites. For the drought-associated gene OsHAK1, the highest-ranked variant was predicted to be associated with chromatin features in a manner consistent with the reported co-occurrence of H2Bub with H3K4 methylation marks in plants. Overall, these results demonstrate the utility of our method for functionally informative prioritization of non-coding variants, facilitating the interpretation of GWAS loci and the identification of candidate regulatory variants in rice.

  bioinformatics2026-07-28v1

• HPAI H5N1 risk in Australia: a model for the prediction of poultry outbreaks

  Zhang, P.; Lim, S.; Quigley, A.; MacIntyre, C. R.

  Abstract

  The panzootic highly pathogenic avian influenza (HPAI) H5N1 virus has now been detected on the Australian mainland, with incursions from the sub-Antarctic region posing an increasing threat to domestic wildlife and poultry populations. Our study aimed to predict the risk of HPAI H5N1 poultry outbreaks across Australia at the local government area (LGA) level using a range of influential risk factors. We first used a Maximum Entropy (MaxEnt) model to estimate the environmental suitability for HPAI H5N1 occurrence across Australia. The resulting suitability layer was then integrated with five additional predictor layers, including abundance data for two Southern Ocean wild birds, one of which has introduced HPAI H5N1 into Australia; abundance data for 28 native Australian wild birds; native bird flyways across Australia; Australian chicken density; and poultry farm density. The six layers were aggregated and averaged to generate an HPAI H5N1 risk map for poultry outbreaks across Australian LGAs. Although most incursions have occurred in Western Australia (WA) and South Australia (SA), we identified New South Wales (NSW) and Victoria (VIC) as having the highest predicted risk of HPAI H5N1 poultry outbreaks. Additional high-risk areas were identified in WA, SA, and Tasmania (TAS). In contrast, the Northern Territory (NT) and large parts of Queensland (QLD), WA, and SA were predicted to be at low risk. These findings provide a spatially explicit framework to support targeted surveillance, preparedness, and biosecurity measures aimed at mitigating the impact of future HPAI H5N1 outbreaks in Australian poultry.

  bioinformatics2026-07-28v1

• STRIDE: Signal Transfer and Aligned Ion-Peak Discrimination for Peptidoform Evidence for Consistent Quantification of Site-Localized Post-Translational Modifications in Large-Scale DIA-MS

  Sing, J. C.; Gupta, S.; Rost, H. L.

  Abstract

  Data-independent acquisition (DIA) mass spectrometry has emerged as a powerful method for comprehensive post-translational modification profiling in proteomics, but faces challenges in consistently assigning peptidoforms to the same chromatographic peaks across multiple samples. This study introduces STRIDE to address this challenge by implementing dynamic programming alignment and propagating unique ion signature signals across runs. We benchmarked STRIDE using a synthetic phosphopeptide dilution series dataset, demonstrating an 11.3-fold reduction in false-negative rate compared to DIA-NN, from 68% with DIA-NN to 6% with STRIDE at 5% false-positive rate when requiring both correct site-localization and correct peak assignment. STRIDE also improved over the base OpenSWATH (IPF), reducing the false-negative rate from 14% to 6% for correct peak assignment. When applied to a phospho-enriched U2OS nocodazole dataset, our approach reduced missing values from 38.6% to 15.2% in a representative quantification matrix, increased highly reproducible phosphopeptide detections from 7,698 to 12,510 in nocodazole-treated samples, and identified 5,431 differentially abundant phosphopeptides compared to 2,588 with DIA-NN. This increased the number of enriched pathways recovered from differential phosphopeptide analysis, including pathways related to mitotic arrest, microtubule organization, chromatin regulation, RNA processing and kinase signalling. Lastly, we analyzed acetyllysine and glycopeptide-enriched DIA datasets, where STRIDE improved complete replicate detection, reduced missing quantification values, and increased site-specific glycan reproducibility. Together, these results demonstrate that STRIDE improves peptidoform proteomics through reduced false-negative rates, consistent peak assignment, and more complete quantification across runs.

  bioinformatics2026-07-28v1

• Sex-Specific Modulation of Gene Expression by 17beta-Estradiol in Human Meniscal Cells: Pathways to Targeted Osteoarthritis Therapies

  Yu, Y.; Vergis, J.; Eby, H.; Markho, M.; Kopacz, J.; Cartwright, K.; Hershey, M.; Liu, J.; McCullumsmith, R.

  Abstract

  Osteoarthritis (OA) disproportionately affects women, and estrogen has been implicated in cartilage and joint homeostasis, yet its effects on meniscal fibrochondrocytes (MFCs), and whether those effects differ by sex, remain poorly defined. We reanalyzed a publicly available RNA-sequencing dataset (Gene Expression Omnibus, GSE199087) comprising human MFCs from a male and a female donor treated with 17{beta}-estradiol (E2) or vehicle. Differential expression, Gene Set Enrichment Analysis, Enrichr, and iLINCS were integrated to identify the transcriptional programs modulated by E2 in each sex. In female MFCs, E2 upregulated pathways governing DNA replication, cell-cycle progression, and genomic maintenance (e.g., MCM8, BRCA1, RAD51), while downregulating inflammatory and extracellular matrix-degrading genes, including MMP1, IL12A, CXCL12, and MYD88. In male MFCs, E2 instead upregulated chromatin remodeling and developmental signaling programs, led by SRCAP, ERCC6, and NOTCH1, and downregulated antigen presentation and mitochondrial genes. These divergent responses indicate that E2 engages distinct, sex-specific transcriptional programs in MFCs, with the female profile favoring proliferation and matrix preservation. Although derived from a limited sample and therefore hypothesis-generating, these findings nominate candidate mechanisms underlying sex differences in meniscal biology and OA susceptibility, and warrant validation in larger, sex-balanced cohorts.

  bioinformatics2026-07-28v1

• Sensitive Chromosomal Translocation Quantitation from Amplicon Sequencing Using Primer-Anchored Statistical Translocation Analysis (PASTA)

  Schmaljohn, E.; Usman, O.; Brommel, C.; Kinney, K. J.; White, N.; Turchiano, G.; Osborne, T.; Sanchez-Pena, A.; Sterrett, J.; Turk, R.; Rettig, G.; Jacobi, A. M.; Sturgeon, M.; Kurgan, G.

  Abstract

  Chromosomal translocations are rare structural rearrangement outcomes of genome editing, requiring analytical frameworks that combine high quantitative accuracy with performant sensitivity and specificity. Amplicon sequencing offers a scalable means to detect rare rearrangements with ultra-deep targeted sequencing, but existing methods often rely on heuristic thresholds or ad hoc normalization steps that limit reproducibility and have unknown analytical performance. Here, we present a computational tool we call PASTA (Primer-Anchored Statistical Translocation Analysis), using a count-based differential-event statistical framework to quantify and statistically confirm translocation junctions from targeted amplicon sequencing data. Comparison of this method to ddPCR demonstrates that quantitation is highly accurate, and outperforms other NGS-based methods even when randomized adapter chemistry is not present in amplicon sequencing structures. To measure analytical performance, we create a benchmarking dataset for measuring chromosomal translocation analysis performance with frequencies ranging from 1% to sub-0.01%, and demonstrate that the method can detect frequencies down to 0.01% with >75% sensitivity when sufficient read depth is present. Taken together, this work demonstrates using amplicon sequencing with PASTA as a bioinformatics analysis tool is a solution for translocation detection in amplicon sequencing genotoxicity assessments, enabling identification of rare genome rearrangements in both research and preclinical applications

  bioinformatics2026-07-28v1

• A geometric-to-neural cascade for cerebral microbleed detection in susceptibility-weighted MRI

  Bogdanov, S.; Rudravaram, G.; Saunders, A. M.; Kim, M. E.; LeFevre, J.; Charles, J.; Jain, S.; Schrag, M. S.; Landman, B. A.

  Abstract

  Cerebral microbleeds (CMBs) are established imaging biomarkers of cerebral small vessel disease and are a defining feature of cerebral amyloid angiopathy (CAA), yet their automated detection in susceptibility-weighted imaging (SWI) remains challenging due to high false-positive rates from vessel cross-sections, iron and calcium deposits, and other hypointense mimics. We present a fully automated, three-stage cascade pipeline that combines subject-adaptive unsupervised candidate generation with two successive lightweight 3D ResNet classifiers, trained with only human-in-the-loop quality-assurance (QA) labels (yes/no per candidate) rather than dense voxel-wise segmentation masks. The candidate generation stage is performed by fitting a Gaussian Mixture Model (GMM) to each subject's SWI intensity histogram to define an adaptive low-intensity threshold, followed by anatomical masking to exclude physiologically irrelevant regions (image edges, ventricles/CSF/choroid plexus, and cerebellum), and filters candidates by size and sphericity. The model was trained and evaluated across a nested 3x5-fold cross-validation on N = 30 subjects from a publicly available labeled microbleed dataset and a CAA cohort (11,424 CMB candidate lesions) with data augmentation during training. Stage A classifies all geometric candidates as CMB or non-CMB and Stage B refines the predicted positives to suppress false positives (cascade AUC = 0.9587, sensitivity = 0.712, specificity = 0.975, PPV = 0.676, F1 = 0.693). The cascade reduces Stage A false positives by 76.8% (888/1,157 false positives eliminated) while retaining competitive sensitivity. Inference was performed on 141 SWI scans, detecting a mean 40.3 CMBs per scan and being preferred for use in 85% of high CMB cases, as evaluated by a blinded neurologist. The inference pipeline outputs binary CMB segmentation NIfTI images and radiologist-ready QA visualizations.

  bioinformatics2026-07-28v1

• Baseline regulatory programs in larval and adult neural progenitors converge towards an injury-induced state after spinal cord injury

  Cosacak, M. I.; Severinov, D.; Westphal, M.; Heilemann, K.; Baerhold, D.; Bretschneider, A.; Reinhardt, S.; Roscito, J. G.; Becker, T.; Becker, C. G.; Poetsch, A. R.

  Abstract

  Regeneration after spinal cord injury requires progenitor cells to convert injury-associated signals into coordinated remodeling of gene regulatory programs. Mammalian spinal progenitors show limited neurogenic output after injury, whereas zebrafish regenerate spinal neurons and recover motor function. To investigate the regulatory changes that allow ependymo-radial glia (ERG) cells, the progenitor cells of the zebrafish spinal cord, to generate new neurons, we combined single-nucleus gene expression and chromatin accessibility profiling across embryonic, larval, and adult stages with topic-based gene regulatory network (GRN) inference. We found that larval and adult ERGs enter the injury response from distinct regulatory baselines: larval progenitors are characterized by a gliogenic program, whereas adult progenitors maintain a comparatively quiescent state. Following injury, both populations gradually change their baseline programs and shift towards a lesion-associated module marked by stress-responsive and chromatin-associated regulators, including jun, hmga1a, hmga2, ybx1, and foxj1a. The shift away from homeostatic states is supported by decreased expression of the Notch-associated regulators nuclear factor I A (nfia) and hey1 in larvae, while in adults, downregulation of the same nuclear factor and other TFs such as bhlhe41 is associated with quiescence exit. Pathway analysis showed stage-specific alterations after injury, characterized predominantly by extracellular signaling and cytoskeletal reorganization in larvae and by metabolic and translational remodeling in adults. Despite divergence from the homeostatic states, injury-induced larval and adult GRNs remain distinct from embryonic hERG regulatory programs. Thus, larval and adult progenitors follow different trajectories from their baselines towards a related lesion-reactive state, in which shared regeneration-associated features are acquired within respective contexts.

  bioinformatics2026-07-28v1

• PG-LLM: Benchmarking General-Purpose Language Models for Protein Variant Ranking

  Arora, R. K.; Chen, L. T.; Du, M.; Marks, D.; Church, G.

  Abstract

  General-purpose language models are being increasingly utilized in protein-design workflows, yet their ability to evaluate variant effects remains unclear. To answer this question, we introduce PG-LLM, a benchmark built on ProteinGym to evaluate general-purpose language models on 217 protein-variant prioritization tasks. Each task follows the same format: a language model receives a wild-type protein sequence, an assay description, and is tasked with ranking 50 mutant sequences by fitness without access to multiple-sequence alignments or protein structures. We evaluate thirteen language models and rescore 95 published protein predictors on the same candidate sets with the same evaluation metric. Claude Opus 5 leads the primary leaderboard with a Spearman correlation of {rho} = 0.406, narrowly ahead of GPT-5.6 Sol at 0.402. However, GPT-5.6 Sol scores higher than Opus 5 when the two models are compared only on assays scored by both. Opus 5 outperforms 49 of 95 published protein predictors, including 41 of 46 sequence-only methods, and approaches ESM2-650M at {rho} = 0.411, but remains below the leading predictor VenusREM at {rho} = 0.523. Variant-ranking performance improves with test-time compute across GPT, Claude, and Gemini models, but the gains taper before closing the gap to specialist protein predictors. Unlike sequence-only predictors, which perform better on proteins with deeper evolutionary alignments, LLM accuracy changes little across alignment-depth. PG-LLM shows that tool-free language models capture substantial protein-variant signal and already outperform many established sequence-based predictors. These results establish the emerging capability of language models as biomolecular reasoners while defining the remaining headroom for their reliable use in variant-prioritization workflows.

  bioinformatics2026-07-28v1

• The practical impact of numerical variability on structural MRI measures of Parkinson's disease

  Chatelain, Y. M. B.; Sokołowski, A.; Sharp, M.; Poline, J.-B.; Glatard, T.

  Abstract

  Numerical variability is rarely quantified in neuroimaging despite many measures relying on subtle morphometric differences across individuals. We instrumented FreeSurfer 7.3.1, a widely used neuroimaging pipeline, to simulate numerical differences across computational environments, and used it to measure numerical variability in MRI analyses of Parkinson's disease patients and controls. In multiple cortical and subcortical regions, numerical variation reached nearly one-third of the population variability, altering statistical conclusions about group differences and clinical associations. To assess the impact of numerical noise in existing studies, we developed a practical tool that estimates the Numerical-Population Variability Ratio (NPVR) in a study, and propagates the resulting numerical variability to common statistics and associated p-values. By applying this framework to thirteen previously published studies reporting MRI measures in Parkinson's disease, we quantified the probability of numerically induced false positives and false negatives in the literature, highlighting a substantial impact of numerical variability on MRI measures of Parkinson's disease with an average significance-flip probability of 5% for cross-sectional studies and 10% for longitudinal studies. These results underscore the importance of systematically evaluating numerical stability in neuroimaging and provide a practical framework to do so.

  bioinformatics2026-07-27v3

• PrimeKG-Plus: a refreshed and rare-disease-enriched precision medicine knowledge graph

  Nguyen, T. T. D.; Nguyen-Phuong, T.; Nguyen, Q.-H.; Abbasi, A. M.; Le Phan, H.-D.; Nguyen, L. B.-A.; Phan, N.-T.; Curabaz, N. N.; Hauser, A. S.; Tanoli, Z.; Nguyen, D. T.; Kooistra, A. J.

  Abstract

  Biomedical knowledge evolves rapidly, yet most disease-centered knowledge graphs remain unchanged after publication. We present PrimeKG-Plus, an extension of PrimeKG that updates all 20 original data resources to releases available as of Dec 2025 and incorporates additional resources, including OpenTargets, RepurposeDrugs, and nSIDES. The graph is further expanded with relations extracted from 637 PubMed abstracts and PMC full-text articles using a large-language-model-assisted curation workflow. Extracted relations were refined through normalization, UMLS-based synonym mapping, SapBERT embedding based similarity ranking, and human expert review to ensure data quality. This literature-driven expansion focuses on rare neurological disorders, including Canavan disease, Niemann-Pick disease type C, Tay-Sachs disease and Batten disease. Network topology analyses indicate that the expanded graph improves indirect drug-disease connectivity of 3-6 hops through the graph while adding 447,288 Drug-Protein-Disease paths linking drug-disease pairs that were previously unreachable. Temporal FDA validation captured 55 new molecular entities after the June 2021 PrimeKG data cut-off as drug nodes, 46 absent from the original PrimeKG. By integrating newly incorporated and updated resources with systematically curated literature-derived associations, PrimeKG-Plus provides an up-to-date knowledge graph for network-based drug repurposing and precision medicine applications. Data and code are publicly available.

  bioinformatics2026-07-27v3