Latest bioRxiv papers

Category: bioinformatics — Showing 50 items

• Combining transcriptomic resolutions and machine learning strategies uncovers new OXPHOS genes in Caenorhabditis elegans

  Zeballos - Goron, S.; Salinas, G.; Pazos Obregon, F.

  Abstract

  Assigning functions to genes remains a major bottleneck in biology, as many genes remain uncharacterized despite the availability of complete genome sequences. Oxidative phosphorylation (OXPHOS), the primary source of ATP in eukaryotes, exemplifies this gap. Although extensively studied in mammals, OXPHOS in other lineages has largely been inferred through sequence homology, an approach that may overlook lineage-specific components and propagate incorrect annotations. Here, we hypothesized that OXPHOS genes share characteristic transcriptional signatures that can be exploited for functional prediction. Using a curated set of 64 well-established OXPHOS genes, we combined supervised and unsupervised machine learning approaches to identify novel OXPHOS-associated genes in Caenorhabditis elegans. An ensemble of support vector machine, random forest, and k-nearest neighbors classifiers was trained on time-resolved bulk RNA-seq data using a novel informed bagging strategy and a two-round training scheme that incorporated genes annotated with limited evidence after an initial prediction round. In parallel, embryonic and adult single-cell RNA-seq datasets were used to infer co-expression networks and identify clusters enriched in known OXPHOS genes. Integrating both approaches yielded a high-confidence set of candidate genes supported by strong predictive performance on an independent test set. Several candidates lacked prior functional annotation. Functional validation of one top-ranked candidate, ril-1, showed that a ril-1 mutant displayed significantly reduced oxygen consumption, consistent with a role for ril-1 in mitochondrial respiration.Our results demonstrate that integrating complementary machine learning strategies with transcriptomic data across multiple biological resolutions enables systematic discovery of genes associated with complex cellular processes.

  bioinformatics2026-07-13v3

• SSUplex: fast, both-strand extraction and origin-sorting of small-subunit rRNA for environmental DNA metabarcoding

  O'Brien, A.; Vargas, J.; Acuna, I.; Restovic, F.; Martinez, P.; Parada, P.

  Abstract

  Ribosomal RNA metabarcoding sits at the centre of how we characterize microbial and eukaryotic communities in environmental samples, and long-read sequencing has made full-length small-subunit (SSU; 16S/18S) profiling routine. The broadly conserved primers that make rRNA such a convenient marker are also its liability: by design they co-amplify organellar (mitochondrial, chloroplast) and cross-domain SSU alongside the intended target. Left unsorted before taxonomic assignment, these passengers are systematically misclassified, and the error propagates straight into estimates of community composition and diversity. Reads must therefore be detected, extracted, and sorted by origin before they ever reach a classifier. We present SSUplex, an open-source tool that detects SSU rRNA, assigns each read to one of five origins (bacteria, archaea, eukaryota, mitochondria, chloroplast), and extracts the SSU region for downstream classification. SSUplex reimplements the extraction-and-origin logic of the widely used Metaxa2 in the Rust programming language, scans both strands, and ships as a single dependency-light binary suited to long-read (Oxford Nanopore, PacBio HiFi) and short-read data. Benchmarked against Metaxa2 on public data, SSUplex reproduces Metaxa2 origin calls on full-length reads (96.8% concordance) and matches its extraction speed on small inputs, then pulls away to run up to approximately 3.4-fold faster with approximately 35% lower peak memory at 200,000 reads, the per-sample scale a long-read amplicon run typically reaches. We characterize a genuine, measured trade-off in the origin-ranking statistic, and we identify the bacteria-versus-mitochondria boundary as the method's one intrinsically lower-confidence edge. For the now-common workflow in which origin-sorted reads are handed to a dedicated classifier rather than classified in place, SSUplex is a fast, reproducible, embeddable stand-in for Metaxa2's extraction role. Source code and a benchmark harness that regenerates every result from public data are available under the MIT license at https://github.com/ayobi/ssuplex.

  bioinformatics2026-07-13v2

• CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction

  Zhang, X.; Jeong, E.; Yan, C.; Feng, Y.; Lyu, L.; Guo, X.; Chen, Y.

  Abstract

  Graph foundation models have emerged as powerful tools for drug repurposing by enabling the prediction of novel drug-disease indications from large biomedical knowledge graphs. A representative example is TxGNN, which was previously developed and trained on PrimeKG, a comprehensive biomedical knowledge graph covering over 17,000 diseases. While TxGNN demonstrates strong performance, existing biomedical knowledge graphs largely lack fine-grained, cell-type-specific expression context. This limits their ability to capture disease mechanisms driven by dysregulated cellular programs, such as immune cell-specific pathways in autoimmune diseases. Moreover, prior evaluations typically test only randomly selected subsets of diseases, leaving many diseases unexamined and limiting conclusions about model performance across the full disease spectrum. To address these limitations, we first update PrimeKG to PrimeKG-U by incorporating expanded and curated biomedical knowledge and then develop TxGNN-U as a stronger graph-based baseline. Building on this foundation, we introduce CellAwareGNN, a graph foundation model that integrates single-cell genomics into PrimeKG-U. We construct a single-cell-enhanced knowledge graph, scPrimeKG, by incorporating cell-type-specific gene expression signatures from the OneK1K dataset, expanding PrimeKG from approximately 8.1 million edges and 129k nodes to over 14 million edges and 147k nodes. CellAwareGNN is pre-trained on all relation types in scPrimeKG and evaluated on drug indication prediction with explicit coverage of all diseases in the knowledge graph. CellAwareGNN consistently outperforms TxGNN and TxGNN-U. For drug indication prediction, CellAwareGNN achieves an AUPRC of 0.826, representing a 1.2% improvement over TxGNN-U (0.816) and a 3.4% improvement over TxGNN (0.799). Notably, for autoimmune diseases, CellAwareGNN attains an AUPRC of 0.864, improving by 2.0% over TxGNN-U (0.847) and 6.0% over TxGNN (0.815). Importantly, CellAwareGNN prioritizes promising repurposing candidates, including Ocrelizumab for Pemphigus via CD20-expressing B cells, Methotrexate for Pemphigus through DHFR and ATIC activity in T and B cells, and Rosiglitazone for Rheumatoid Arthritis through PPAR-{gamma} activation. These results demonstrate the value of iincorporating cell-type-specific expression context to improve both predictive performance and biological interpretability in graph-based drug repurposing.

  bioinformatics2026-07-13v2

• Language Model Embedding Classifiers Enable Identification of Multiple Sclerosis-Associated BCRs and Repertoires

  Peet, G. C.; Owens, G. P.; Bennett, J. L.; Krishnan, A.; Macklin, W. B.

  Abstract

  Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease. It affects over 2 million people worldwide but has historically been challenging to diagnose, categorize, and treat. MS has an autoimmune component that involves the production of B-cell receptors (BCRs) and immunoglobulins (Igs) that are associated with disease pathophysiology. We reanalyzed all publicly available RNA sequencing data from MS patients and extracted over 11 million BCR immunoglobulin heavy chain (IGH) sequences obtained from a variety of tissue sources. We developed a new decoder-only BCR DNA embedding model that outperforms state-of-the-art BCR and general-purpose protein language models on sequence embedding tasks. We then trained a language model classifier capable of identifying BCR sequences associated with MS. Using low-dimensional representations of whole repertoire embeddings combined with sequence-disease predictions, we can distinguish MS patient repertoires from healthy, infectious disease, or other autoimmune disease repertoires. Our models also successfully rank known MS-associated myelin-binding IgG sequences relative to controls. These findings provide a methodological foundation for BCR-based MS detection and could facilitate identification and study of disease-associated antibodies from blood.

  bioinformatics2026-07-13v1

• Biomarker Variability Limits Individualized Amyloid Time Estimation in Alzheimer Disease

  Wisch, J. K.; Jiao, Z.; Millar, P. R.; McKay, N. S.; Beric, A.; Lin, W.; Baker, B.; Stauber, J.; Preminger, S.; Jucker, M.; Barthelemy, N. R.; Chhatwal, J.; Ryan, N. S.; Schindler, S. E.; Cruchaga, C.; Benzinger, T. L. S.; Karch, C.; Bateman, R.; McDade, E.; Llibre-Guerra, J.; The Dominantly Inherited Alzheimer Network, ; The Alzheimer Disease Neuroimaging Initiative, ; Gordon, B. A.; Ances, B.; Ibanez, L.

  Abstract

  Objective: Disease progression modeling (DPM) or "amyloid time" is increasingly used to stage Alzheimer disease (AD). DPM performance depends on within-individual heterogeneity in rates of pathological accumulation as well as test-retest reliability of the biomarker. The relative contributions of these variabilities have not been systematically assessed. This would be particularly relevant if extrapolations from DPM were to be used to make individual-level predictions for research, clinical trials, or potentially future clinical practice. Methods: We conducted simulation studies incorporating empirically-derived noise properties from amyloid biomarkers to assess the contributions of inter- and intra-individual variability. Findings generalized in an autosomal dominant AD cohort with amyloid positron emission tomography (PET), cerebrospinal fluid (CSF), and plasma biomarkers and in a sporadic AD cohort with both amyloid PET and plasma biomarkers. We assessed group level DPM performance via mean average error (MAE) and root mean squared error (RMSE). At the individual level, we evaluated distinctness of distributions of biomarker levels associated with specific disease timings. Results: Inter-individual variability was the dominant source of error in temporal estimates. Intra-individual variability reduced estimate stability. Optimal performance occurred in biomarkers with positive average accumulation rates where a subset of individuals had exceptionally high levels of accumulation. In research study data, amyloid PET outperformed CSF and plasma biomarkers. Interpretation: DPM is fundamentally constrained by dynamic range, variability, and test-retest reliability of the biomarker of interest. Current DPM approaches are more robust at the group level, particularly when applied to biomarkers with more than 10-15% variability like fluid biomarkers. Funding: National Institute on Aging, Alzheimer's Association, German Center for Neurodegenerative Diseases, Raul Carrea Institute for Neurological Research, Japan Agency for Medical Research and Development, Korean Ministry of Health & Welfare and Ministry of Science and ICT, Spanish Institute of Health.

  bioinformatics2026-07-13v1

• Does ensembling improve feature attributions from sequence-to-activity models?

  Maslova, A.; Libbrecht, M.

  Abstract

  Sequence-to-activity models take as input DNA sequence and predict genomic activities such as transcription factor binding and gene expression. Applying explainable AI (xAI) methods such as DeepLIFT to these models has recently led to breakthroughs towards many genomic problems, including transcription factor binding grammar and predicting effects of genetic variants. However, there remains significant uncertainty about the reliability of sequence-to-activity interpretations.Thus, we need accurate probabilistic measures of confidence to distinguish reliable from unreliable interpretations. Towards this end, researchers have recently aimed to characterize variability acrossensembles of S2A models. However, previous work has focused on using model ensembles to improve the model predictions themselves. Here, we aim to evaluate whether model ensembles can also be used to improve feature attributions from post-hoc xAI methods. We find that ensembling attributions from multiple models improves downstream applications, including identifying transcription factor motifs and predicting regulatory genetic variants. We show that forming an ensemble using Monte Carlo Dropout (MCDropout) gets near to, but does not match, the performance of training multiple models, at much less train-time computational cost.

  bioinformatics2026-07-13v1

• Quantum Encoding Strategies for Drug Response Prediction: An Exhaustive Benchmark on a 20-Qubit Superconducting QPU

  Derouich, R.; Mathlouthi, N. E. H.

  Abstract

  We present the first systematic, hardware-executed benchmark of twelve distinct quantum data-encoding strategies for drug-response prediction on a real superconducting quantum processing unit (QPU). All experiments were conducted on the IQM Garnet 20-qubit QPU via the IQM Resonance cloud platform, using the Qrisp quantum-software framework (v 0.8.2). Each encoding was evaluated on n = 50 stratified samples drawn from the Genomics of Drug Sensitivity in Cancer dataset (GDSC2, 242 036 drug-cell-line pairs), targeting the natural-log IC50 response variable. Variational weights were optimised offline with the gradient-free COBYLA algorithm before hardware submission. Every circuit was executed with 1024 shots; the regression signal is the zero-qubit Pauli expectation value (Z0). Results show that the QAOA-inspired encoding achieves the best RMSE of 3.314 and is statistically superior (p < 0.05, Wilcoxon signed-rank test) to six of the remaining eleven encodings. Hardware-efficient entanglement structures-specifically alternating cost and mixer layers-provide a systematic advantage over purely rotational or diagonal encodings under realistic noise conditions. This work constitutes a reproducible baseline for noise-aware quantum machine learning on pharmaceutical data; all code, data, and raw QPU outputs are publicly released.

  bioinformatics2026-07-13v1

• A Bayesian Network-Based Framework for Causal Cancer Drug Target Discovery Integrating Patient and Cell Line Data

  Yoon, S. H.; Park, Y. R.; Kim, H. U.

  Abstract

  Current approaches to cancer drug target discovery face two key limitations: poor translation of cell line-derived targets to patient tumors, and the lack of causal explanation of the regulatory mechanisms underlying target prioritization. Here we present BayesTx (Bayesian Therapeutics target discovery), a Bayesian network framework that integrates patient transcriptomics data with cell line data to identify causal therapeutic targets in cancer. BayesTx projects both data domains into a shared biological space of pathway and transcription factor activities, learns domain-specific causal graphs, and merges them through weighted edge aggregation with bootstrap consensus filtering. Do-simulation on the consensus network quantifies the causal effect of each transcription factor on cancer cell viability. Applied to breast cancer using TCGA-BRCA (The Cancer Genome Atlas breast cancer cohort) and DepMap (Cancer Dependency Map) datasets, the framework ranked 47 transcription factors by predicted causal impact, with gene-level targets further derived through regulon-based propagation. Top-ranked transcription factor (TF) targets were independently supported by survival analysis in external cohort data and pharmacogenomic drug response associations. Overall, BayesTx demonstrates that cross-domain Bayesian network modeling can bridge patient and cell line data to systematically identify causal therapeutic targets in cancer.

  bioinformatics2026-07-13v1

• Learning proteomic disease trajectories with flow matching

  Hartman, E.; Karlsson, C.; Malmström, J.

  Abstract

  High-throughput proteomics has enabled detailed characterization of molecular states across health and disease. However, biological systems are inherently dynamic and methods for reconstructing continuous proteome changes remain limited. Here, we introduce proteome velocity, a framework for inferring continuous proteome trajectories from cross-sectional or sparsely sampled proteomics data using flow matching, in which a neural network learns velocity fields over proteome space. Proteome velocity estimates how rapidly and in which direction protein abundances change along a biological progression, such as disease. In mouse sepsis, covariate-conditioned velocity models resolved tissue- and pathogen-specific proteome trajectories and identified inflammatory proteins with distinct temporal activation patterns across infection routes and organ systems. In clinical COVID-19 plasma proteomes, inferred trajectories separated into distinct velocity programs associated with disease severity. These results show how generative trajectory models can transform cross-sectional or sparsely sampled proteomics into interpretable, protein-resolved representations of molecular progression.

  bioinformatics2026-07-13v1

• amR: an R package suite to predict antimicrobial resistance in bacterial pathogens

  Ghosh, A.; Brenner, E. P.; Boyer, E. A.; McKim, A. P.; Vang, C. K.; Wolfe, E. P.; Mayer, D. A.; Lesiyon, R. L.; Ravi, J.

  Abstract

  Motivation: Identifying bacterial antimicrobial resistance (AMR) is critical for diagnostics and treatment, but resistance is a complex trait arising from myriad mechanisms spanning multiple molecular scales. Existing computational approaches often function as black boxes and rarely explore cross-species or multi-drug patterns. We developed amR, an integrated R package suite that provides a complete framework from bacterial genome data curation to interpretable AMR predictions, enabling identification of resistance mechanisms across species and drugs. Results: The amR R package suite contains three modular packages. amRdata downloads genomes and paired antimicrobial susceptibility testing data from BV-BRC and processes them, constructs pangenomes, and extracts features at gene/protein cluster, protein domain, annotated Clusters of Orthologous Groups and ResFinder AMR-associated features, and structural variant scales; data are stored in memory-efficient formats (Parquet, DuckDB). amRml trains interpretable machine learning models per species-drug combination, calculates feature importance and performance metrics, and provides rich ground for hypothesis generation and mechanism discovery. amRviz provides an interactive Shiny dashboard to explore metadata distributions and model performance across species and drugs, visualize top predictive AMR features, and analyze cross-model patterns across geographic/temporal strata. We apply the suite to Shigella sonnei, achieving a median Matthews Correlation Coefficient of 0.89 across 23 drugs and drug classes. With thousands of genomes, multi-scale features, and interpretable models, amR provides an accessible, comprehensive framework for AMR research. The amR package suite is installable via GitHub (https://github.com/JRaviLab/amR; BSD-3-Clause license).

  bioinformatics2026-07-13v1

• Sex-Dimorphic Aging of Cardiovascular Disease Genes: A Network-Based Multi-Omics Analysis

  Defilippo, A.; Boccuto, F.; Guzzi, P. H.; Veltri, P.

  Abstract

  Sex differences influence the incidence, timing, clinical presentation, and outcomes of cardiovascular disease (CVD), yet the molecular programs through which aging interacts with biological sex remain insufficiently understood. To address this gap, we integrated basal gene expression profiles from multiomics data across 981 donors and 17 CVD relevant tissues with regulatory, genetic, network, disease-expression, and druggability information to characterize sex dimorphic aging patterns in 1,176 candidate CVD genes. Using a two-step expression analysis, we identified 4,404 genes with significant age-associated expression trends (BH-FDR < 0.05), including 2,718 male-specific, 202 female- specific, and 742 shared trends. Concordant evidence across complementary statistical approaches highlighted 35 high-confidence sex-dimorphic genes, including REN, APOE, GUCY1A2, and SRD5A2. Regulatory analysis showed that most CVD genes were influenced by nearby genetic variants, with 96.2 Network-based analyses further suggested that CVD genes are organized within hierarchical biological structures, with curated protein-interaction data showing stronger geometric organization than broader interaction resources. Integration with Open Targets identified 289 genes already linked to approved drugs and 48 of the top 50 biomarker candidates supported by GWAS-eQTL colocalisation evidence. A final composite ranking prioritized NTRK1, TUBB4A, PTGS2, IL6, and PDE5A, and identified 19 actionable biomarkers supported by convergent expression, regulatory, genetic, and therapeutic evidence. Among these, a dedicated sex-specific evidence score nominated GUCY1A2, CACNA1D, PGR, PDE5A, and LEPR as the strongest candidates for sex-stratified validation, with GUCY1A2 and PDE5A converging on a nitric oxide-cGMP signaling axis. This study provides an integrative framework for discovering sex-dependent molecular signatures of cardiovascular aging and for prioritizing biologically supported, potentially actionable targets for precision cardiovascular medicine.

  bioinformatics2026-07-13v1

• Optimizing automated classification for zooplankton in coastal conditions: the impact of model selection, imaging instruments, and colour information

  Hovenkamp, P. D. L.; van Walraven, L.; Ollevier, A.; van Oevelen, D.; van der Stappen, A. F.

  Abstract

  The advancement in deep learning techniques has made Convolutional Neural Networks (CNNs) a powerful tool for the fully automated classification of zooplankton images. In this study, we systematically investigate how network selection, colour information and differences in imaging instruments affect the classification of zooplankton images by comparing multiple state-of-the-art CNNs on images of zooplankton and marine snow from the in situ Continuous Particle Imaging and Classification Sensor (CPICS), Video Plankton Recorder (VPR), In Situ Ichtyoplankton Imaging System (ISIIS), and the on-board Plankton Imager (Pi-10). With differences between models of 7.8 to 19% in F1 score, we find that model selection strongly affects the classification performance, with EfficientNetV2S showing the most reliable overall performance. Moreover, differences between model architectures are largest for the least abundant classes (<100 labeled images), which implies that when these are present, careful model selection is most beneficial. The high image quality of the Pi-10 strongly increases the performance for the least abundant classes compared to the other instruments. In addition, we find a significant correlation (r = 0.597) between ImageNet the performance and F1-score on zooplankton images, which implies that more generally, a model that performs well on ImageNet will perform well for zooplankton classification. Colour information increases the F1-score of the best performing classifier with 2.8%, but provides a stronger benefit (25% F1-score) for classes with <100 images. The overall performance increase of colour information is less than expected and questions the advantage of recording colour information for zooplankton.

  bioinformatics2026-07-13v1

• Divergent transcriptomic transition programs in Alzheimer disease: immune priming and synaptic collapse in females versus organelle membrane bifurcation in males

  Choi, M.; Kim, D.-G.; Bauermeister, S.

  Abstract

  Alzheimer's disease (AD) disproportionately affects women, who account for approximately two-thirds of prevalent cases. Despite decades of research, the mechanistic basis for this profound sex disparity remains poorly resolved. Prior transcriptomic studies have predominantly used pooled or female-enriched cohorts, obscuring whether the transition from normal cognition (NCI) to AD follows a common molecular program across sexes or reflects fundamentally divergent biology. Here, we analyzed bulk RNA-sequencing data from the dorsolateral prefrontal cortex of 624 individuals (401 females, 223 males) in the ROSMAP cohort using a transition-aware framework that separates variance instability along the disease axis from mean expression changes. We demonstrate that female and male brains exhibit structurally distinct transcriptomic transition programs. Females display a sequential, multi-tier architecture: interferon and immune variance priming (628 genes) is detectable early at the NCI-to-mild cognitive impairment (MCI) interval, which structurally precedes a massive mean-level synaptic and neuropeptide loss (8,935 genes) in AD. The female NCI-to-MCI interval alone produces 1,249 variance bifurcation events entirely absent in males. Conversely, males exhibit no early immune priming or powered mean-level changes. Instead, they collapse into a single large variance bifurcation pool (8,237 genes) heavily enriched for system-wide organelle membrane fusion, post-translational modification, and intracellular trafficking. These findings reveal that the AD transcriptomic transition is not a unitary program quantitatively modified by sex, but two distinct biological trajectories. This fundamental divergence motivates sex-stratified mechanistic models and independent biomarker development for each sex, cautioning against analytical pooling in transition-stage cohorts.

  bioinformatics2026-07-13v1

• TOPAS: phosphoproteome data analysis and decision support platform for molecular tumor boards

  Jensen, C. B.; Sakhteman, A.; Hamood, F.; Schneider, A.; Woortman, J.; Teleanu, M.-V.; Horak, P.; Bayer, F. P.; Stange, C.; Huellein, J.; Hübschmann, D.; Henssen, A. G.; Fröhling, S.; Kuster, B.; The, M.

  Abstract

  The molecular tumor board (MTB) is central to precision oncology, providing personalized treatment recommendations based on molecular profiles of patient tumors. Genomics is instrumental for MTBs but often fails to identify clinically actionable targets, a gap that phosphoproteomics can fill. We present the tumor proteome activity status (TOPAS) platform, an end to end analysis pipeline that converts terabytes of phosphoproteomic data into patient-specific reports for MTB discussions, focusing on clinically relevant signaling linked to oncogenic mechanisms and therapeutic targets. Designed to scale with growing cohorts, the platform integrates data from 1,998 tumor samples to support patient- and cohort-level hypothesis generation. A web portal handles quality control, calculates TOPAS scores, identifies tumor antigens and immune checkpoints, and offers interactive analyses of differential protein abundance and outlier detection. The TOPAS platform is open source, addresses a critical unmet need and facilitates broader adoption of phosphoproteomics in precision oncology in the future.

  bioinformatics2026-07-13v1

• EcoXAI: Autonomous Agentic Ecosystem for Explainable Artificial Intelligence and Biomedical Discovery

  Matsumoto, N.; Choi, H.; Freda, P. J.; Hernandez, M. E.; Wang, Z. P.; Moore, J. H.

  Abstract

  Motivation: As biomedical datasets and knowledge graphs continue to grow in size, complexity, and heterogeneity, navigating and extracting actionable insights from them presents a major bottleneck for researchers. There is a clear need for autonomous analytical solutions that can utilize recent advancements in agentic AI such as agent harnessing and loop engineering without introducing hallucination or workflow fragmentation. Researchers, regardless of technical expertise, need tools that streamline complex data analysis and deliver meaningful, actionable insights grounded in both data and established biomedical knowledge. EcoXAI addresses this by introducing a modular, customizable, containerized multi-agent system that structures analysis into explicit pipeline execution stages, lowering the computational barrier for clinical and translational researchers. Result: EcoXAI replaces monolithic AI text interfaces with an autonomous execution-driven framework with specialized bioinformatics agents for delivering proactive, data-driven insights grounded in established biological knowledge. Unlike purely LLM-driven or less integrated AI solutions prone to hallucinations or biologically implausible outcomes, EcoXAI's multi-agent framework, which leverages modern agentic management and explicit knowledge graph integration, provides greater transparency and verifiability in its reasoning. In our use case in drug repurposing for Alzheimer's Disease, EcoXAI evaluated 103 drug candidates and identified 79 novel candidates whose predictive models exceeded a randomized baseline, including the CCR5 antagonist Maraviroc, whose generated hypothesis was subsequently supported by the literature. These results demonstrate the potential of knowledge graph-grounded AI agents to accelerate hypothesis-driven biomedical research.

  bioinformatics2026-07-13v1

• Tensor-based machine learning for multi-omics integrative clustering and feature discovery

  Zhang, Y.; Liu, L.; Liu, Z.; Liu, Q.; Ma, L.; Zhang, Z.

  Abstract

  Multi-omics integrative analysis is pivotal for elucidating complex molecular mechanisms and biological processes, yet remains challenging due to the high dimensionality and heterogeneity of multi-omics data. Here we present MIA, a machine learning framework for accurate sample clustering and feature discovery from multi-omics data. Unlike existing algorithms that primarily rely on two-dimensional representations, MIA models multi-omics data as a three-dimensional tensor and integrates tensor decomposition, fuzzy c-means, and an enhanced random forest model within a unified framework. Benchmarking on simulated and empirical datasets demonstrates that MIA consistently outperforms representative state-of-the-art algorithms in both clustering and feature identification. Application to multiple TCGA cancer types further shows its ability to stratify samples and identify molecular features associated with clinically relevant outcomes. Specifically, in glioblastoma, MIA reveals three previously uncharacterized subtypes with distinct prognostic profiles and uncovers feature genes strongly associated with subtype identity. These genes are further linked to therapeutic response and retain discriminative power across major glioblastoma cellular populations at single-cell resolution. Collectively, our results establish MIA as a generalizable computational framework for multi-omics integrative analysis, enabling systematic molecular stratification and interpretable feature discovery across diverse biological systems.

  bioinformatics2026-07-11v3

• Differential T cell receptor clonotype abundance analysis with similarity-based counts weighting

  Buytenhuijs, F.; Smits, T. C.; Ankan, A.; Textor, J.

  Abstract

  To better understand immune responses, comparing the abundance of T cell receptors (TCRs) between conditions can provide insights into which T cells have proliferated or were involved in immune activation. This requires methods that can accurately identify significant differences in TCR sequencing (TCR-seq). For conventional RNA-seq data, well-established differential gene expression (DGE) analysis tools such as DESeq2 and edgeR have been developed. However, applying these methods to data presents additional challenges. TCR-seq data is highly sparse, overdispersed, and contains many artificial zeros, which can lead to inflated false-positive rates when using traditional approaches. While non-parametric methods like the Wilcoxon test better control false positives, they may suffer from lower statistical power. To address these issues, we propose a novel pre-processing step for TCR-seq data using network-based local weighting based on TCR sequence similarity. This pre-processing step improves the sensitivity and reduces the false-positive rates of methods like DESeq2 and edgeR while enhancing the power of the Wilcoxon test. Through empirical analysis of both simulated and real datasets, we show that combining our pre-processing step with the Wilcoxon test achieves robust performance, outperforming traditional RNA-seq methods. This simple but powerful approach to TCR-seq analysis could help advance our understanding of adaptive immune responses.

  bioinformatics2026-07-11v2

• scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data

  Christidis, A.; Ghazi, A. R.; Chawla, S.; Turaga, N.; Gentleman, R.; Geistlinger, L.

  Abstract

  Although cell type annotation has become an integral part of single-cell analysis workflows, the assessment of computational annotations remains challenging. Many annotation tools transfer labels from an annotated reference dataset to a new query dataset of interest, but blindly transferring labels from one dataset to another has its own set of challenges. Often enough there is no perfect alignment between datasets, especially when transferring annotations from a healthy reference atlas for the discovery of disease states. We present scDiagnostics, a new open-source software package that facilitates the detection of complex or ambiguous annotation cases that may otherwise go unnoticed, thus addressing a critical unmet need in current single-cell analysis workflows. scDiagnostics is equipped with novel diagnostic methods that are compatible with all major cell type annotation tools. We demonstrate that scDiagnostics reliably detects complex or conflicting annotations using both carefully designed simulated datasets and diverse real-world single-cell datasets. Our evaluation demonstrates that scDiagnostics reliably identifies misleading annotations that systematically distort downstream analysis and interpretation and that would otherwise remain undetected. The scDiagnostics R package is available from Bioconductor (https://bioconductor.org/packages/scDiagnostics).

  bioinformatics2026-07-11v2

• DNA-MGC+: A versatile codec for reliable and resource-efficient data storage on synthetic DNA

  Khabbaz, R.; Mateos, J.; Antonini, M.; Kas Hanna, S.

  Abstract

  The biochemical processes underlying DNA data storage, including synthesis, amplification, and sequencing, are inherently noisy. Consequently, base-level insertion, deletion, and substitution (IDS) errors, as well as sequence-level dropouts, occur and pose major challenges for reliable data retrieval. Here we introduce DNA-MGC+, a DNA storage codec designed to enable reliable and resource-efficient data retrieval under diverse operating conditions. We evaluate DNA-MGC+ across a wide range of in silico and in vitro settings, including experiments with both Illumina and Nanopore sequencing, and show that it consistently outperforms several representative codecs from the literature. In particular, DNA-MGC+ achieves simultaneous gains in sequencing depth requirements, read cost, decoding time, storage density, and error-correction capability under explicit reliability constraints. These gains persist and become more pronounced for larger files, with DNA-MGC+ remaining reliable and efficient well beyond the practical scalability limits of the benchmarked codecs. Notable performance results include reliable decoding under IDS error rates of up to 24% in synthetic scenarios, and reliable retrieval at sequencing depths below 3x with read costs below 3.5 nts/bit under electrochemical synthesis for both Illumina and Nanopore sequencing.

  bioinformatics2026-07-11v2

• Tokenizing single-cell transcriptomes as a native language for large language models

  Xiao, C.; Ding, Y.; Bian, H.; Chen, Y.; Wei, L.; Zhang, X.

  Abstract

  Large language models (LLMs) can process diverse forms of information once they are represented as tokens in a shared sequence space. However, single-cell transcriptomes remain a foreign modality to LLMs because they are continuous, high-dimensional molecular profiles rather than discrete linguistic units. Here, we propose CellTok, a tokenized single-cell language modeling approach that converts transcriptomic profiles into compact cellular token sequences and incorporates them into the vocabulary of a pretrained LLM. By representing cells as native tokens, CellTok enables cellular measurements, textual instructions, biological context, and multi-cell populations to be jointly processed within the same autoregressive modeling framework. Across diverse tasks, CellTok enable LLMs to recognize individual cells, interpret homogeneous and heterogeneous cell populations, infer disease-associated cellular states, predict cell-cell communication, model developmental trajectories, and generate cellular states. Moreover, prompt-based experiments show that providing appropriate biological context improves performance, indicating that CellTok can leverage LLM knowledge and contextual reasoning to support cellular data interpretation. These results demonstrate that single-cell transcriptomes can be transformed from a foreign molecular modality into a native language for LLMs, establishing a unified interface for modeling cells, populations, and biological knowledge in a shared token space.

  bioinformatics2026-07-11v2

• Somatic mutation inference from single-cell transcriptomics: A survey in the esophagus

  Mendez-Alejandre, A.; Gonzalez-Menendez, D.; Vidal-Notari, S.; Skrupskelyte, G.; Rodriguez-Rodriguez, M.; Ajith, H.; Torralba, A. S.; Alcolea, M. P.; Piedrafita, G.

  Abstract

  Human somatic tissues accumulate mutations during normal aging. Some of these affect cancer-associated driver genes and confer mutant progenitor cells a competitive advantage that leads to clonal expansions. The human esophageal epithelium exemplifies this phenomenon, becoming a dense mosaic of competing mutant clones by adulthood. However, the phenotypic consequence of those mutations and their possible role in carcinogenesis remains unknown. Novel bioinformatic tools for de novo mutant detection from single-cell transcriptomics (scRNA-seq) could potentially leverage on the wealth of publicly available data to help draw mutant cell phenotypes in vivo. In this study we test SComatic algorithm's ability to identify somatic mutations in the normal, polyclonal esophageal epithelium. We analyze a public scRNA-seq dataset from a human cohort with multiple esophageal samples per donor, and an independent study in mice subjected to experimental mutagenesis where samples have been re-sequenced for validation. These unconventional experimental designs allow us to control unspecificity. We observe scRNA-seq variant calling output is heavily affected by undesired technical artifacts and germline variants, which we are able to reduce following a customized series of rational filters that enrich in somatic mutations. Final candidate mutations are then used to reconstitute clonal lineages and map them to differentiation trajectories in the UMAP embeddings. We find low read depth and sparse cellular sampling favor detection of passenger mutations and hinder driver mutant phenotypic inferences. Altogether, we showcase current limitations of scRNA-seq-derived mutation calling, while we offer methodological indications that should be considered for future studies aimed at investigating mutant clone behavior in normal polyclonal tissues from single-cell transcriptomics.

  bioinformatics2026-07-11v1

• Metagenomic contextualization of proteins with state space models

  Azbijari, N.; Wynne, J. H.; David, M.; Thurber, A. R.

  Abstract

  Since the early adoption of metagenomics (the culture-free sequencing of microbial community genomes) in 2011, sequence data has increased over 500-fold across ecosystems. This surge in data has outpaced reliable taxonomic and functional annotation, with over half of sequences lacking confident functional assignment. These unknown sequences limit our understanding of microbial processes central to planetary health and human health. Recent advances in genomic language modeling have made progress in the interpretation of metagenomics datasets. Most state-of-the-art models rely on transformer architectures, which limit the maximum sequence length and therefore capture only a fraction of assembled metagenomic sequences due to the quadratic scaling of attention. This prevents training and inference on sequences with broad context, including multiple coding and non-coding regions. To overcome this limitation, we propose leveraging new model architectures that scale linearly with sequence length, making them more suitable for modeling longer metagenomic sequences. Here, we introduce Nammu, a mixed-modality Mamba-based foundation model with 167M parameters trained on the OpenMetaGenomic (OMG) corpus. Nammu is a bidirectional encoder trained with a 20K context length using a curriculum strategy, first on 64M protein sequences and then on 32M mixed-modality metagenomic contigs. We compared Nammu to gLM2, a mixed-modality transformer also trained on OMG using 37% more tokens, using taxonomy inference on a marine dataset from the Critical Assessment of Metagenome Interpretation (CAMI). Nammu outperforms gLM2 at every taxonomic level. We further assessed function via KEGG Orthology prediction in deep-sea metagenome-assembled genomes, where Nammu outperforms gLM2 (150M). These results demonstrate improved performance.

  bioinformatics2026-07-11v1

• AGPI: An AI-Powered Genomic Pathogen Intelligence Platform for Integrated Classification, Visualization, and Therapeutic Targeting

  Goel, A.; Mishra, P.

  Abstract

  Rapid and accurate pathogen detection remains a major challenge in modern bioinformatics, as existing tools are often fragmented and require multiple specialized workflows. We present AGPI (AI-powered Genomic Pathogen Intelligence), an integrated platform that combines genomic sequence classification, biological enrichment, three-dimensional structural visualization, and AI-guided therapeutic prioritization within a single interpretable pipeline. AGPI employs a hybrid convolutional Bidirectional Gated Recurrent Unit (BiGRU) architecture trained on DNA sequences from 40 pathogen classes spanning viruses, bacteria, fungi, and protozoan pathogens. The model achieved 99.61% validation accuracy and 94.90% accuracy on an independent held-out evaluation of 600 pathogen sequences following iterative refinement. As a proof of concept, AGPI correctly classified a Zika virus genome with 96.14% confidence, retrieved curated biological context from 245 peer-reviewed studies, and identified Ribavirin as a leading therapeutic candidate against the Zika NS5 polymerase through AI-guided molecular docking. Multi-metric ligand similarity analysis further differentiated candidate compounds according to their structural and pharmacological properties. These results demonstrate that integrated AI-driven genomic pipelines can accelerate pathogen characterization and therapeutic hypothesis generation while providing an accessible and interpretable framework for infectious disease surveillance and computational drug repurposing.

  bioinformatics2026-07-11v1

• Structure-guided computational design and mechanistic understanding of the p95HER2-targeting NAZ-mAb antibody and its variants

  Rawat, P.; Kyte, J. A.; Greiff, V.; Dorraji, E.

  Abstract

  Human epidermal growth factor receptor 2 (HER2) is an oncogenic receptor tyrosine kinase in breast cancer and other malignancies. A subset of HER2-positive tumours expresses 611-CTF-p95HER2, a tumour-specific, hyperactive truncated isoform associated with metastasis and treatment resistance that lacks most of the extracellular domain targeted by conventional HER2-directed antibodies. We previously developed NAZ-mAb (formerly known as Oslo-2), a monoclonal antibody against 611-CTF-p95HER2. Here, we describe a computational antibody-engineering workflow for designing variants of NAZ-mAb. Starting from the sequence alone, we modeled the NAZ-mAb-611-CTF-p95HER2 complex, generated a combinatorial mutational landscape using FoldX 5.0, and prioritized candidate variants using predicted interaction energy and developability criteria. Two variants representing distinct design strategies were selected for validation: an aromatic double mutant, NAZ-mAb v1 (L:S31W/L:H107W), and a conservative single mutant, NAZ-mAb v2 (L:S31M). Both variants were successfully expressed as recombinant IgGs; NAZ-mAb v2 achieved a five-fold higher recombinant expression yield than parental NAZ-mAb, while both variants retained antigen binding with a higher apparent signal than the parental antibody in indirect ELISA. However, Biacore two-state kinetic analysis revealed weaker affinities than the parental antibody (KD NAZ-mAb v1: 32.6 nM, NAZ-mAb v2: 9.45 nM vs. parental NAZ-mAb: 5.33 nM). These findings show that the computational workflow can generate experimentally tractable, antigen-engaging NAZ-mAb variants, while also highlighting the limitations of fixed-backbone interaction-energy ranking as a predictor of binding affinity and yield. This study provides a practical framework for computationally driven, developability-aware antibody optimization in the absence of experimental structural data.

  bioinformatics2026-07-11v1

• Spatial mitochondrial lineage tracing uncovers a premetastatic niche and microenvironment programmed fate switching in osteosarcoma

  Xue, Y.; Su, Z.; Su, J.; Chu, A. S.; Wan, L.; Chen, M.; Cheung, J. P. Y.; Cheung, K. S. C.; Ho, J. W. K.

  Abstract

  Osteosarcoma progression involves complex spatiotemporal dynamics, yet the lineage relationships underlying tumor cell fate decisions remain poorly understood. Here we integrate single cell and spatial transcriptomics and mitochondrial variants-based lineage tracing to map the evolution of a late-stage, non-metastatic osteosarcoma. Single cell pseudotemporal reconstruction of malignant cells reveals a stepwise cascade of transcriptional programs that instructs bifurcation of COL3A1 progenitors into ALPL osteoblastic and THY1 mesenchymal lineages. Critically, we demonstrate that the metastatic THY1 mesenchymal cell fate is not governed by cell-autonomous transcriptional programs alone; this pro-tumorigenic state requires strict spatial licensing via direct co-localization with PLVAP dysfunctional endothelia to form a pro-metastatic niche (THY1-Endo) enriched for PTN/NOTCH signaling. To distinguish clonal ancestry from microenvironmental plasticity, we leveraged spatially resolved somatic mitochondrial variants as endogenous lineage tracers. A spatial autocorrelation filter confirmed significant clonal spatial coherence (Z = 10.07, p = 4 x10 -24). Phylogenetic reconstruction using a Wright-Fisher drift model coupled with a hidden Markov tree (WF-HMT) revealed a striking directional transition rate from COL3A1 to THY1 tumor cells (724.08) versus the reverse (4.09), a ~177-fold asymmetry, whereas the COL3A1 to ALPL transition rate was lower (0.47). Notably, drift--agnostic coarse VAF clustering failed to resolve ALPL versus THY1 bifurcation, underscoring that explicit modeling of stochastic mitochondrial drift is essential for uncovering directional fate locking. We propose a "time--space--lineage" relay model where sequential microenvironmental signals program tumor cell fate and pre-metastatic niche assembly through irreversible clonal commitment. This multi dimensional framework bridges single cell states, spatial organization, temporal dynamics, and clonal ancestry -underscoring the power of somatic mitochondrial variants to resolve clonal history and intercept cancer evolution.

  bioinformatics2026-07-11v1

• synpact: accurate, memory-light PacBio HiFi read mapping via a hierarchy of locally-consistent syncmer blocks

  Aydin, M. S.; Sahlin, K.

  Abstract

  Motivation: Mapping PacBio HiFi reads is a routine task and serves as a central step in many bioinformatics analyses. However, the most accurate long-read mappers have a high memory consumption and are slow. Some light-weight mappers have been proposed for faster runtime, but their accuracy is not comparable to state-of-the-art mappers. With the increasing number of available reference sequences, memory-efficient and fast methods for read mapping without the large accuracy drop are desired. A general trade-off with seed-chain-extend mappers is selecting a single, fixed seed size, which forces a compromise between sensitivity and specificity. Results: We present synpact, a long-read mapper that uses several seed sizes (a hierarchy) constructed with Locally Consistent Parsing (LCP) over syncmers. A read is mapped by querying for matches at different levels, followed by sliding window voting. By storing only the coarse upper levels rather than the full hierarchy, the index holds several times fewer entries, while still handling errors by falling back from coarser to finer stored levels at query time. We benchmark synpact against popular long-read mappers on four genomes and different read lengths. For simulated PacBio HiFi data, synpact matches or approaches minimap2 accuracy with higher precision in most cases, while using roughly 5-13 times less peak memory (e.g., about 0.8GB vs. 10.7GB on human) and mapping faster on large or repetitive genomes (e.g., about 10 to 13 times faster than minimap2 on rye). On real HiFi reads synpact has high concordance with minimap2 across the four genomes, as opposed to the other lightweight long-read mappers. Availability and Implementation: synpact is written in Rust and is available at https://github.com/mahmudsami/synpact

  bioinformatics2026-07-10v2

• GenCore: Genomic distance estimation using Locally Consistent Parsing

  Ashyralyyev, A.; Sirvan, E.; Malikic, S.; Batu, T.; Sahinalp, C.; Alkan, C.

  Abstract

  In the era of exponential data generation, a fast, consistent, and efficient string processing technique is necessary to represent extensive genomic data. One of the earliest string processing techniques, predating MinHash and minimizer-based sketching, is Locally Consistent Parsing (LCP). This technique partitions an input string and identifies short, exactly occurring substrings called cores, which collectively cover the input string while maintaining Partition and Labeling Consistency. The iterative application of LCP yields progressively longer cores in a compressed format, thereby substantially enhancing the efficiency of genomic sequence representation and subsequent downstream analysis. We have previously developed LCPtools as the first iterative implementation of LCP for the DNA alphabet and demonstrated its effectiveness in identifying cores with minimal collisions. Here, we introduce GenCore, a computational method that leverages LCP cores for the first time to sketch and estimate genomic distances for closely related large genomes, and successfully reconstruct simulated progression trees. GenCore also successfully recapitulates primate phylogeny using both telomere-to-telomere (T2T) assemblies and the PacBio HiFi reads for assembly-free comparisons. Availability: GenCore is available at https://github.com/BilkentCompGen/gencore

  bioinformatics2026-07-10v2

• Classpose drives the discovery of colorectal cancer phenotypes in clinical grade whole slide images

  Mandal, S.; de Almeida, J. G.; Bräutigam, K.; Papanikolaou, N.; Graham, T. A.

  Abstract

  Cell phenotyping in histopathology samples is essential for diagnostic and research workflows. However, human expert annotation requires significant time and expertise while being affected by inter-observer variability. Here, we present Classpose, an easily trainable framework for cell segmenting and phenotyping built on top of Cellpose-SAM with state-of-the-art performance across 6 distinct datasets, outperforming competing methods. We show that this requires fine-tuning the entire network, highlighting how instance segmentation is a poor objective for downstream cellular classification. We apply it to a large whole slide image (WSI) colorectal cancer (CRC) cohort (SurGen) and show that Classpose-derived cellular organisation and morphology features can be used to determine novel spatial morphological phenotypes for clinically relevant molecular conditions (MMR deficiency, BRAF mutations, KRAS mutations) and to predict these same molecular conditions. We make Classpose models available and provide a user-friendly QuPath extension for widespread use by the digital pathology community.

  bioinformatics2026-07-10v2

• Generative continuous time model reveals epistatic signatures in protein evolution

  Pagnani, A.; Barrat-Charlaix, P.

  Abstract

  Protein evolution is fundamentally shaped by epistasis, where the effect of a mutation depends on the sequence context. As standard phylogenetic methods assume independently evolving sites, there is a need for more complex models based on accurate estimations of the fitness landscape. Good candidates are modern generative models -- such as the Potts model -- which successfully capture epistatic effects. However, recent work on generative evolutionary models usually use discrete time, making them difficult to integrate with the standard frameworks in evolutionary biology. We introduce a continuous-time sequence evolution model using the Gillespie algorithm and parameterized by a generative Potts model. This approach enables us to simulate realistic, family-specific evolutionary trajectories and allows for direct comparison with independent-site models. Surprisingly, we find that while epistasis significantly slows down evolution, it does not change the average evolutionary rates at individual sites. This is explained by the rate heterogeneity caused by context-dependence: we show that the rate at some positions varies between null to high values depending on the context, while other positions are essentially independent from the context. Finally, we show that epistasis leads to a systematic underestimation bias in the inference of evolutionary distance between sequences. Overall, our work provides a new tool for simulating realistic protein evolution and offers novel insights into the complex interplay between epistasis and evolutionary dynamics.

  bioinformatics2026-07-10v2

• The role of space in explaining macroecological patterns of microbial abundance

  Gutierrez-Arroyo, A.; Lampo, A.; Cuesta, J. A.

  Abstract

  Understanding the origin of universal macroecological patterns in microbial communities remains a central open question in ecology. A key observation is that species abundance fluctuations across diverse biomes are well described by a gamma distribution, yet the mechanism responsible for this regularity is debated. Prevailing explanations invoke exogenous stochastic forcing, while endogenous interaction-based approaches -grounded in generalized Lotka-Volterra (gLV) dynamics- have so far failed to reproduce this pattern. Here we show that incorporating spatial structure resolves this discrepancy. In a gLV model with constant migration, local abundance dynamics remain inconsistent with a gamma distribution. However, when the community is embedded in a fragmented landscape as a metacommunity -with species dispersing among patches- aggregating abundances across patches yields distributions that closely match the empirically observed gamma form. We further demonstrate that this result can be reproduced by aggregating independent realizations of the constant-migration gLV model, entailing statistical aggregation rather than a specific biological mechanism as the origin of this pattern. Our findings highlight the fundamental role of spatial structure in shaping microbial macroecology, and suggest that observed abundance distributions may largely reflect the spatial coarse-graining inherent in metagenomic sampling.

  bioinformatics2026-07-10v2

• Combining transcriptomic resolutions and machine learning strategies uncovers new OXPHOS genes in Caenorhabditis elegans

  Zeballos - Goron, S.; Salinas, G.; Pazos Obregon, F.

  Abstract

  Assigning functions to genes remains a major challenge in biology, as many genes remain uncharacterized despite the availability of complete genome sequences. Oxidative phosphorylation (OXPHOS), the primary source of ATP in eukaryotes, exemplifies this gap. Although extensively studied in mammals, OXPHOS in other lineages has largely been inferred through sequence homology, an approach that may overlook lineage-specific components and propagate incorrect annotations. Here, we hypothesized that OXPHOS genes share characteristic transcriptional signatures that can be exploited for functional prediction. Using a curated set of 64 well-established OXPHOS genes, we combined supervised and unsupervised machine learning approaches to identify novel OXPHOS-associated genes in Caenorhabditis elegans. An ensemble of support vector machine, random forest, and k-nearest neighbors classifiers was trained on time-resolved bulk RNA-seq data using a novel informed bagging strategy and a two-round training scheme that incorporated weakly annotated genes after an initial prediction round. In parallel, embryonic and adult single-cell RNA-seq datasets were used to infer co-expression networks and identify clusters enriched in known OXPHOS genes. Integrating both approaches yielded a high-confidence set of candidate genes supported by strong predictive performance on an independent test set. Several candidates lacked prior functional annotation. Functional validation of one top-ranked candidate, ril-1, revealed significantly reduced oxygen consumption in mutant animals, supporting its involvement in mitochondrial respiration. Our results demonstrate that integrating complementary machine learning strategies with transcriptomic data across multiple biological resolutions enables systematic discovery of genes associated with complex cellular processes.

  bioinformatics2026-07-10v2

• LeafRank: A phylodynamic framework for inferring relative fitness from single-cell phylogenies in chromosomally unstable tumors

  Wu, C.; Leder, K.; Wang, Z.; Sun, R.

  Abstract

  Tumors contain cancer cells with diverse growth potentials that shape evolutionary trajectories, yet this fitness diversity remains difficult to quantify in cases of whole-genome duplication (WGD) and chromosomal instability. We present LeafRank, a mathematical framework that leverages single-cell DNA-seq phylogenies to infer the relative fitness of individual cells. Using a multi-type branching process model, LeafRank integrates full tree topology, including branch lengths and bifurcation patterns, to estimate marginal fitness probabilities under punctuated evolutionary regimes driven by rare driver events. To account for elevated aberration rates following WGD, we introduce a tree-rescaling strategy that adjusts for lineage-specific genomic instability. Unlike methods focused on predefined subclones, LeafRank ranks all sampled cells, enabling flexible assessment of growth heterogeneity. Simulations demonstrate high accuracy across spatial and non-spatial virtual tumors. Applied to ovarian cancer, LeafRank reveals directional and parallel selection in WGD tumors and identifies recurrent copy number events enriched in high-fitness lineages. WGD lineages do not show immediate growth advantages but acquire fitness through subsequent alterations.

  bioinformatics2026-07-10v2

• Reference-Based Library Construction Improves Performance in low-input Workflows

  Charkow, J.; Ghaznavi, M.; Seale, B.; Peng, J.; Gingras, A.-C.; Rost, H.

  Abstract

  In low input mass spectrometry-based proteomics, Data Independent Acquisition (DIA), is quickly becoming the method of choice for label free quantification. Whether using empirical or in silico spectral libraries, performance is dependent on the library; however, the optimal library construction strategy for low input proteomics remains an open question. To address this, we examine and develop library construction approaches that are compatible with both spectrum-centric and peptide-centric analysis workflows. These approaches leverage a closely related, high-quality sample to improve library quality. First, we validated our approach in bulk sample amounts where we observed that the effects of gas-phase fractionation based library construction is dependent on the software framework, with improvements more pronounced in OpenSWATH compared to DIA-NN. In OpenSWATH, our peptide-centric library reconstruction workflow consistently outperforms a transfer learning strategy, an emerging alternative approach. In DIA-NN, trends are dependent on library source highlighting OpenSWATH's stronger dependence on the search space. In low-input applications, such as single-cell-equivalent injection amounts (100 pg) of HeLa cell digest on a timsTOF SCP, our library construction approach provided more pronounced improvements across both software tools compared to bulk samples. Using a peptide-centric reconstruction approach with the OpenSWATH analysis framework, we detected over 15,000 peptide precursors (2480 protein groups), a 90% improvement over the original library. Furthermore, using a spectrum-centric construction approach, peptide precursor identification rates improved over 6-fold ( ~1000 to ~6000). Our strategy provides a practical solution for generating high-quality libraries in low-input applications.

  bioinformatics2026-07-10v2

• Coordinate- and Sequence-Based Features for a new Combined Annotation-Dependent Depletion Framework of Structural Variants (CADD-SV v2.0)

  Catona, O.; Kircher, M.

  Abstract

  Structural variants are a major source of genomic variation and contribute to human disease and evolution through diverse mechanisms, yet their functional interpretation remains challenging. We present CADD-SV v2.0, an improved machine learning framework for scoring SV deleteriousness that expands on the original CADD-SV implementation. This version introduces a unified Random Forest model trained on an expanded set of proxy-neutral and proxy-deleterious variants drawn from human and non-human primate genomes. The model integrates updated genomic annotations, including constraint metrics, regulatory elements, and chromatin architecture features. It scores Deletions, Insertions, Duplications and Inversions based on a single scoring framework that uses both the variant and its flanking regions. To complement this framework, we also explore sequence-based annotations derived from SegmentNT, a deep learning model that provides functional predictions from DNA sequence at nucleotide resolution. Our analysis evaluated whether sequence-derived functional signals can provide additional information for SV prioritization and whether additional models with these features alone or in combination with previous coordinate-based annotations can be used. CADD-SV v2.0 outperforms its previous version and other tools in prioritizing deleterious variants across major SV types, including some previously unsupported, and substantially improves the computational workflow, increasing predictive power for genome-wide SV interpretation.

  bioinformatics2026-07-10v1

• MKMC enables reference-free transcriptomic analysis using k-mer representations

  Mboning, L.; Dlugosz, M.; Kokot, M.; Chen, J.; Costa, E. K.; Wu, M.-R.; Wang, S.; Bouchard, L.-S.; Deorowicz, S.; Pellegrini, M.

  Abstract

  Traditional RNA-seq analysis depends heavily on genome alignment and gene annotation, limiting its utility in non-model organisms and introducing biases that can obscure regulatory complexity. We present MKMC (Multi-sample Kmer Counter), a scalable, reference-free toolkit for RNA-seq analysis that leverages k-mer-based statistics to detect biological variation without requiring alignment. MKMC integrates fast k-mer counting, abundance matrix generation, normalization, dimensionality reduction, and differential analysis into a unified workflow. Across diverse datasets, MKMC recapitulates key biological signals, including sex differences in killifish liver, and matches alignment-based pipelines in differential expression analysis and transcriptomic age prediction. Notably, MKMC detects isoform-specific events missed by traditional methods, one of which we validated using in situ hybridization. These results reveal previously hidden isoform-level regulatory events that contribute to sex- and age-associated transcriptional programs. MKMC offers a robust, extensible alternative to alignment-based approaches, enabling transcriptomic discovery across both model and non-model systems. While we focus here on RNA-seq as a primary application, MKMC is broadly applicable to any k-mer-based analysis of next-generation sequencing data.

  bioinformatics2026-07-10v1

• Adaptive 2.5D base-pairing subgraph search detects RNA small-molecule binding sites

  Nitchi, D.; Waldispuhl, J.; Oliver, C.

  Abstract

  Ribo-LENS is a geometric deep-learning framework for detecting small-molecule binding sites in RNA structures. It is designed to exploit two properties of RNA base-pairing networks: their robustness to conformational fluctuation and the functional signatures they encode. By reasoning directly in the space of base-pairing subgraphs, Ribo-LENS assembles coherent binding sites, in contrast to methods that score residues independently. In extensive experiments, Ribo-LENS is competitive with, and often outperforms, large all-atom co-folding models (AlphaFold3, Chai-1), fine-tuned language models (GerNA-Bind), and structure-based tools (RNAsite), raising mean MCC to 0.380 (versus 0.321 for the state-of-the-art GerNA-Bind). It is strongly robust to apo/holo rearrangement, with its accuracy tracking the base-pairing graph (Spearman rho = 0.82 with binding-site graph edit distance) rather than backbone displacement (rho = -0.15 with RMSD), and depends far less on sequence homology than competing predictors. In an end-to-end, sequence-based virtual screen of the ROBIN assay (approximately 25,000 compounds), Ribo-LENS guides docking to a small predicted pocket, matching a blind all-atom cavity search (enrichment factors up to 6.1) at a fraction of the search cost; on two SARS-CoV-2 targets its predicted sites align with NMR chemical-shift perturbations. Ribo-LENS turns coarse base-pairing structure into a practical entry point for screening the vast, largely unexplored RNA target space.

  bioinformatics2026-07-10v1

• Deconvolving the spatiotemporal chromatin landscape through the cell cycle

  Tran, T. Q.; Li, Y.; MacAlpine, D. M.; Hartemink, A. J.

  Abstract

  Profiling genomic processes during the cell cycle is challenging because synchronized populations gradually lose synchrony as individual cells progress through the cycle at different rates and divide asymmetrically. Researchers have addressed this challenge by modeling the loss of synchrony and applying sophisticated branching process deconvolution methods to mitigate the effects of imperfect synchrony. Such methods have been used to deconvolve cell cycle transcription, but despite the central role of the chromatin landscape in orchestrating eukaryotic transcription and replication, comparable approaches to deconvolving cell cycle chromatin occupancy have not been developed, because the data are orders of magnitude larger and because DNA replication introduces non-uniform copy number effects across the genome during S phase. We present CyCLOPS, a computational framework that overcomes these technical challenges, enabling deconvolution of the genome-wide chromatin landscape throughout the cell cycle at high spatiotemporal resolution. We apply CyCLOPS to MNase-seq data collected from synchronized yeast populations at 10-minute intervals to produce the first dynamic atlas of genome-wide chromatin occupancy through the cell cycle, profiled at sub-minute resolution. We identify functional groups of cell cycle genes through chromatin-based clustering and uncover chromatin regulatory dynamics, including at non-genic loci. Our atlas reveals that chromatin occupancy and transcription fluctuate largely independently.

  bioinformatics2026-07-10v1

• Extended t-cores for the de novo identification of transposable elements and other inexact repeats from short read RNAseq data

  Darmon, S.; Mary, A.; Lacroix, V.

  Abstract

  Transcribed repeats represent a major challenge in the de novo assembly of transcriptomes from short RNA-seq reads. Young transposable elements (TEs) and other inexact repeats create dense and ambiguous regions in the assembly graph, preventing the correct assembly of transcripts. In this paper, we introduce a fully de novo method based on the discovery of dense regions in the compacted De Bruijn graph (DBG) to identify such repeats directly from short reads RNA-seq data, without requiring a reference genome or repeat database. Our approach defines the extended t-cores, subgraphs of the DBG that capture the complex topology induced by highly expressed inexact repeats appearing in RNA-seq reads. Independently of its interest for transcriptome assembly, the proposed method appears to be effective for the de novo identification of repeats in transcriptomes. After classifying cores using sequence-based motifs to distinguish simple repeats from potential TEs, we demonstrate its potential for the de novo discovery of transposable elements. We validate the approach on a Mus musculus dataset using expressed TE consensus sequences, showing that extended t-cores correspond to known expressed TE families. We also illustrate its de novo discovery potential on a non-model species, Canis lupus familiaris, where the method was also able to recover known transposable elements.

  bioinformatics2026-07-10v1

• pylimma: a faithful, AnnData-native Python port of R limma for differential expression analysis

  Mulvey, J.

  Abstract

  pylimma is a faithful Python port of limma, intended to bring one of the most widely used tools for differential expression analysis to the developing Python ecosystem for transcriptomics and proteomics. We validated pylimma against the existing R implementation through 227 function-level comparisons and across six real world datasets spanning microarray, RNAseq, proteomics and single-cell transcriptomics. pylimma reproduces limma's numerical output to a median agreement of 13 significant figures and calls identical sets of differentially expressed features and gene sets. This supports its use as a drop-in replacement for the R implementation.

  bioinformatics2026-07-10v1

• Spatial Autocorrelation Aware Resampling Improves Cell-Cell Interaction Inference in Spatial Transcriptomics Data

  Khatri, P. H.; Newton, M. A.; Kendziorski, C. A.; Dinh, H. Q.

  Abstract

  Spatial transcriptomics has enabled finer-grained analyses of cell-cell interactions through the co-expression of ligands and their cognate receptors, thereby accounting for the spatial constraints of signaling. However, existing methods employ random permutations or analytic calculations to assess statistical significance, neither of which accounts for spatial autocorrelation, a common property of spatially resolved data. Here, we introduce SOAAR (Spatial Omics Autocorrelation-Aware Resampling), a statistical method for testing gene-gene correlations in spatial data that maintains spatial gene-level autocorrelation in resampled datasets used to generate null distributions. SOAAR uses spatial map patterns to decompose autocorrelation. The associations between gene expression and autocorrelation patterns are then randomized to construct resampled datasets used for evaluating significance testing. We showed that SOAAR maintains gene-level spatial autocorrelation and yields a lower false-positive rate than random permutations across varying degrees of gene-level spatial autocorrelation in simulation studies. In a 10X Visium dataset from 10 HNSCC patients treated with immunotherapy, SOAAR filters out low-confidence interactions that were present in only individual samples or in fewer than 3 samples. That led to the identification of a consistent signature of T-cell recruitment in Responder patients and a resistance signature driven by angiogenesis and tumor cell proliferation in Non-Responders. Similar trends were observed in a larger cohort of 23 patients profiled with single-cell spatial CosMX SMI data, revealing immune cell interactions in response to immunotherapy. Overall, SOAAR provides a more calibrated framework for testing spatial correlation, grounded in spatial statistics. Future developments will seek to link localized correlation patterns to downstream changes in biological pathways, thereby informing biomarker and therapeutic target discovery.

  bioinformatics2026-07-10v1

• CellPilot: an agentic framework that pilots small language models through autonomous single-cell annotation

  Jiang, S.; Qi, C.; Chen, Y.; Song, X.; Wei, Z.

  Abstract

  Large language models can annotate cell types from marker gene lists, but they typically operate after preprocessing and clustering are complete, treating annotation as a terminal labeling step rather than controlling the analytical decisions that produce the evidence for cell identity. We present CellPilot, an agentic framework that guides a locally deployable small language model through the full single-cell analysis workflow, from raw count matrices to cluster-level annotation. CellPilot combines standard single-cell analysis tools with structured workflow control and observation-guided reasoning, allowing the model to plan analyses, execute tools, inspect intermediate results and revise decisions within a traceable session. On GTEx, structured workflow orchestration raised the same 8B model from 0.39 in a prompt-only setting to 0.89, closing most of the gap to GPT-4o (0.92) within the same framework; the framework gain was substantially larger for the smaller backbone across datasets (+0.35 versus +0.19). Across GTEx, Tabula Sapi- ens, and Mouse Cell Atlas, CellPilot achieves cluster-level annotation accuracies of 0.891, 0.750, and 0.773, outperforming representative reference-based, marker-based, and LLM-based methods. CellPilot confidence scores were associated with annotation correctness and supported post hoc filtering, while complete execution traces were retained for each analysis. These results suggest that structured workflow orchestration can be a critical determinant of performance in multi-step single-cell analysis, enabling locally deployable small language models to approach larger proprietary models while preserving transparency and practical usability.

  bioinformatics2026-07-10v1

• Enhanced proteome relative quantification using refined quantotypic spectral libraries

  Barnes, B. A.; Alharbi, H.; Unwin, R.

  Abstract

  Plasma proteomics is used for a variety of applications including biomarker discovery, disease monitoring, and drug development. Data-independent acquisition (DIA) has vastly improved the breadth of proteins that are identified from samples; however, given challenges in reproducibility and translation, it is critical that the quantitative performance of these methods is reliable. Analysis of global proteomics data typically incorporates information from all detected peptides. However, some peptides do not reflect their parent protein amount, due to irreproducible digestion, modification, analytical interferences or instability. We hypothesise that including these peptides impacts protein relative quantification, and thus, a refined spectral library containing only quantitatively representative peptides provides superior protein quantification. By analysing a defined multi-species spike-in model, we show that refining a plasma spectral library by removing precursors that fail to meet quality control metrics (25.4% of all identified precursors) reduces noise and variability, improving precision, accuracy and differential abundance analysis by up to ~11%, with minimal identification losses and substantial reduction in computational demand. This demonstrates proof-of-concept that refining spectral libraries produces results that prioritize quantification quality over quantity. This approach could enable development of universal tissue-specific refined spectral libraries able to improve quantification quality with easy implementation and minimal processing time.

  bioinformatics2026-07-10v1

• Modelling the impacts of imports and non-native subspecies hybridisation in honeybees

  de Carlos, I.; Strachan, L.; McCormack, G. P.; Gorjanc, G.; Obsteter, J.

  Abstract

  Human-mediated movement of organisms for agriculture and ecosystem services often results in hybridisation and introgression between populations of native and non-native species. While introgression may increase genetic diversity, it can erode unique adaptations and reduce fitness, threatening the survival of native lineages. Honeybees offer a good model with extensive records, queen trade and migratory beekeeping facilitating genetic exchange among subspecies. To explore these dynamics, we used SIMplyBee to simulate hybridisation between populations of native Apis mellifera mellifera and non-native A. m. carnica. We adopted the parameters from the Irish honeybee population that maintains relatively low levels of import, but is threatened by commercial imports. The model included colony honey yield and fitness as complex polygenic traits. We simulated varying import rates, genetic correlations between fitness in native and non-native environments, and spatial distributions of introgression over 20 years, measuring levels and rate of introgression and genetic means for both traits. Increased imports accelerated introgression and induced a trade-off between higher honey yield and lower fitness, and decreasing genetic correlations between environments amplified fitness decline. Spatial simulations showed the spread of introgression across the entire simulated area. Halting imports reversed the trend, but purging of introgressed material was slow and varied among replicates. These findings highlight the trade-off between short-term production gains and long-term losses in fitness and adaptation. Our modelling framework provides a reference for exploring introgression in other systems, emphasising that sustainable management of introgression requires restricting imports and breeding locally adapted populations rather than relying on non-native imports.

  bioinformatics2026-07-09v3

• CircDiscoverer: A multispecies comprehensive resource for circRNA-protein interactions and RNA modification landscapes

  Srinivasan, S.; Kumar, S.; Chatterjee, S.; Chande, A.

  Abstract

  Circular RNAs (circRNAs) regulate various cellular processes by interacting with miRNAs, proteins, and by encoding peptides. Compared to circRNA-miRNA interactions, circRNA-protein interactions (CPIs) and circRNA modifications have been relatively less explored. Here, we developed a comprehensive user-friendly web resource, CircDiscoverer, to explore CPIs and modification landscapes across multiple species, including Homo sapiens, Mus musculus, Drosophila melanogaster, and Arabidopsis thaliana. CircDiscoverer integrates manually curated literature-supported CPIs with computationally predicted interactions. Furthermore, it offers comprehensive insights into protein-binding profiles and detailed information on interacting circRNAs, including qRT-PCR primer sequences, guide RNAs for experimental validation and potential RNA modifications, thereby providing an integrated platform for further research. CircDiscoverer is available at https://aclab.iiserb.ac.in.

  bioinformatics2026-07-09v2

• VicMAG, an open-source tool for visualizing circular metagenome-assembled genomes highlighting bacterial virulence and antimicrobial resistance

  Tsuda, Y.; Tanizawa, Y.; Vu, T. M. H.; Nishimura, Y.; Shintani, M.; Abe, H.; Hasebe, F.; Kasuga, I.; Nagao, M.; Suzuki, M.

  Abstract

  Bacterial pathogens spread in clinical and environmental settings, and mobile genetic elements (MGEs), such as plasmids and phages, mediate the transfer of virulence factor genes (VFGs) and antimicrobial resistance genes (ARGs) among bacterial communities. Metagenomic analysis of environmental and wastewater samples using highly accurate long-read sequencing technologies, such as PacBio HiFi sequencing, provides valuable insights into monitoring the regional spread of VFGs and ARGs, including dissemination mediated by MGEs. No visualization tool is currently available for the comprehensive display of numerous resulting circular metagenome-assembled genomes (cMAGs) with functional gene annotations. Here, we developed VicMAG, a visualization tool for highly complex cMAGs derived from long-read metagenome assemblies annotated using updated databases of VFGs, ARGs, and MGEs. Using 353 cMAGs from PacBio HiFi sequencing of a wastewater sample, we demonstrated the utility of VicMAG for metagenome visualization. VicMAG provides comprehensive, size-aware visualization of cMAGs representing bacterial chromosomes and plasmids, annotated with VFGs, ARGs, and phages. By simultaneously visualizing all cMAGs in a framework, VicMAG facilitates a holistic understanding of the distribution and genomic context of VFGs and ARGs across complex microbial communities. This tool supports integrated surveillance of bacteria associated with virulence and antimicrobial resistance across clinical, environmental, and One Health contexts.

  bioinformatics2026-07-09v2

• Emergence of Biological Structural Discovery in General-Purpose Language Models

  Wang, L.

  Abstract

  Large language models (LLMs) are evolving into engines for scientific discovery, yet the assumption that biological understanding requires domain-specific pre-training remains largely unchallenged. Here we report that general-purpose LLMs possess an emergent capability for biological structural discovery. Under strict, shortcut-controlled evaluation, a small-scale GPT-2 (124M) fine-tuned solely on English paraphrase discrimination detects protein homology zero-shot at ROC-AUC 0.79 on a shortcut-controlled benchmark. Controls establish that the ability is conferred by pre-training, not architecture: a randomly initialized GPT-2 is at chance (0.52). To exclude the possibility that public checkpoints were contaminated with biological data, we train our own GPT-2 from scratch on an English-only web corpus; it reproduces the transfer (0.76), proving the effect arises from linguistic pre-training alone. Network-based interpretability reveals a deep structural isomorphism: the discriminative signal localizes to deep layers (0.97 at layer 9), and attention analysis surfaces modality-agnostic "difference" operators. Scaling to massive instruction-tuned models further improves performance, including in the remote-homology "twilight zone", which we report as an exploratory upper bound because those models' training corpora are undisclosed. We formalize these tasks through the BioPAWS benchmark. Our controlled results, obtained entirely on models with known training data, establish that abstract logical structures distilled from human language constitute a genuine, if bounded, cognitive prior for decoding the syntax of biology.

  bioinformatics2026-07-09v2

• Expanding the Landscape of Disordered Flexible Linkers: A Structural and Computational Framework for DLD dataset assembly

  meng, d.; Glavina, J.; Garcia Alvarez, H. M.; Leonetti, C. O.; Pollastri, G.; Chemes, L. B.

  Abstract

  Disordered flexible linkers (DFLs) are functional elements found within intrinsically disordered regions that carry out key functions by connecting domains and/or short linear motifs. Understanding the features of DFLs is limited by the lack of comprehensive datasets and accurate predictive models. In this study, we propose a classification for DFLs that includes linkers joining two domains (DLD), a domain and a motif or two short linear motifs. We developed a workflow that allows the systematic identification of DLD-type linkers from protein structures and created a comprehensive dataset known as the DLD dataset. The DLD dataset includes 1640 independent domain linkers (IDLs) which expands currently available linker datasets and annotates related regions such as dependent-domain linkers, intra-domain loops, and termini. Our data collection process integrates missing residue completion and smoothing of short secondary structure stretches enabling to capture a higher number of longer IDLs. We assessed the features of IDLs using t-SNE analysis and protein language model embedding with a CNN-based classifier as well as PCA analysis. IDLs can be distinguished from other disordered and folded protein regions, and their features highly overlap with DisProt Linkers, considered the gold standard for linker annotation. The DLD dataset offers a valuable resource for researchers seeking to investigate the features of disordered flexible linkers and to improve the accuracy and generalizability of DFL predictive models. The DLD dataset is available via an interactive web server at https://dld.chemeslab.org/ where linkers are annotated with sequence and structural features and can be visualized using a structure viewer.

  bioinformatics2026-07-09v2

• Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis

  Su, Z.; Li, T.

  Abstract

  Cancer genomics and diagnostics is a rapidly evolving field in which identifying which topics attract early citation prominence can inform laboratory investment, clinical translation, and research strategy. We developed a bibliometric framework to identify and characterize the most influential recent publications in this domain across two consecutive annual cohorts. Using a mathematically exact threshold-expansion algorithm, we ranked over 10,000 OpenAlex-indexed research articles per cohort by 18-month post-publication citation count. Large language model (LLM)-based topical relevance filtering yielded 50 substantively on-topic papers per cohort (100 total). LLM-based concept extraction and a two-stage, embedding-guided normalization pipeline produced 1,853 canonical concepts organized into 103 parent themes, enabling structured cross-cohort comparison of paper-level concept prevalence. The most cited papers in both cohorts were large-scale genomic infrastructure resources rather than single-disease mechanistic studies. Between consecutive cohorts, normalized frequencies increased most for whole-genome sequencing, tumor microenvironment biology, molecular biomarkers, and cancer pharmacotherapy, while liquid biopsy-related themes showed the largest declines. These findings indicate that early citation impact in cancer genomics is shifting toward integrative, population-scale, and microenvironment-aware research, and demonstrate that LLM-augmented citation ranking provides a replicable, semantically enriched lens for monitoring thematic evolution in precision oncology. A web interface for exploring the results is available at https://pri.pepkio.com/.

  bioinformatics2026-07-09v1

• BertST: BERT-based Spatial Domain Identification in Patient Data

  Nnadi, G. O.

  Abstract

  Spatial transcriptomics enables the study of gene expression within its native tissue context, providing critical insights into cellular organization and microenvironment-driven biological processes. A key challenge in this field is spatial domain identification, which aims to partition tissue into coherent regions by jointly leveraging gene expression and spatial information. Existing approaches are predominantly based on Graph Neural Networks (GNNs), and approach based on Transformers particularly, Bidirectional Encoder Reppresentation Transformer (BERT) model for modelling both local and long-range dependencies remains largely unexplored. In this work, we propose BERT for Spatial Transcriptomics (BertST), a transformer-based framework that reformulates spatial transcriptomics as a graph-to-text representation learning problem. Building upon the BERTwalk paradigm, we construct a task-specific multi-graph representation integrating spatial adjacency, pruned gene-expression similarity, and a fully connected gene-expression graph. This design enables the modelling of both local spatial structure and global molecular relationships. Random walks over these graphs are treated as sequences, allowing a BERT model to learn contextualised node embeddings. To further enhance representation quality, we introduce a hierarchical multi-graph propagation strategy, where embedding refinement is performed sequentially: first on the fully connected graph to capture global structure, followed by the pruned graph to refine molecular relationships, and finally on the spatial graph to enforce local smoothness. This ordering ensures that global information is effectively distributed and progressively constrained by biologically meaningful neighbourhoods. We also improve computational efficiency by leveraging \textit{PecanPy}, a fast and scalable implementation of node2vec, enabling efficient random walk generation on dense graphs. Experimental results on multiple 10x Visium datasets, including DLPFC and Human Breast Cancer, demonstrate that BertST consistently outperforms or matches GNN-based methods such as ConST, CCST, and SpaceFlow in terms of Adjusted Rand Index (ARI) and Adjusted Mutual Information (AMI). Overall, BertST highlights the potential of transformer-based architectures for spatial omics analysis by effectively capturing both local and long-range spatial-molecular dependencies, offering a promising alternative to traditional graph-based methods.

  bioinformatics2026-07-09v1

• qg: Configuration-Driven, Multi-Vendor Acquisition Queue Generation with Reproducible Run-Order and QC Control for Mass Spectrometry

  Wolski, W. E.; Schwarz, L.; Trachsel, C.; Zanella, M.; Riedi, C.; Schlapbach, R.; Othman, A.; Tuerker, C.; Nanni, P.; Panse, C.

  Abstract

  Mass spectrometry laboratories must turn lists of submitted samples into acquisition queues. The run order and the placement of quality-control (QC) injections determine whether a design controls batch effects and signal drift, and whether those effects stay correctable afterward. Yet operators usually set them by hand in vendor worklist editors that neither randomize run order nor offer configurable, pattern-driven QC. We present *qg*, an open-source tool that builds acquisition queues with systematic run-order handling: four run-order modes (none, simple, blocked/randomized-complete-block, and group-uniform blocked), pattern-driven QC and standard injections, and sampler- and plate-aware positioning. Unlike plate-design tools that stop at a generic sample sheet, *qg* writes the native vendor acquisition file directly, for three instrument ecosystems (Thermo Fisher XCalibur, Axel Semrau Chronos, Bruker HyStar) across proteomics, metabolomics, and lipidomics. It separates a small, stateless generation pipeline from a declarative configuration layer, so a laboratory adapts instruments, QC patterns, layouts, and naming by editing version-controlled configuration through a validating editor rather than changing code. *qg* runs from a reactive web interface or a scripted command-line interface, integrated with a LIMS (B-Fabric) or standalone from uploaded tables; randomized runs record their seed and reproduce from exported parameters. On an unbalanced design, group-uniform blocked randomization spreads biological groups evenly across acquisition time, whereas textbook block randomization leaves a tail of the largest group and can track acquisition time worse than a plain shuffle. *qg* is released under the Apache-2.0 license.

  bioinformatics2026-07-09v1