Latest bioRxiv papers

Category: bioinformatics — Showing 50 items

• Automated generation of personalized trajectories of aging phenotypes with DyViA-GAN

  Pyne, S.; Ray, D.; Ray, M. S.

  Abstract

  With a general increase in human lifespan, the need for technological advances to develop strategies for healthy aging has assumed great importance. In the present study, our goal is to predict the progression of selected aging phenotypes in a given healthy individual as one continues aging past 65 years. Therefore, we developed a novel framework called Dynamic Views of Aging with conditional Generative Adversarial Networks (or DyViA-GAN) which is capable of predicting the plausible personalized trajectories of a selected aging phenotype conditioned on the available measurements of the phenotype at a few initial time instances, and additional covariates. Given the prevalence of osteoporosis in the aging population, we selected femoral neck Bone Mineral Density (BMD) of a healthy individual as the phenotype of interest, and baseline individual Body Mass Index (BMI) as covariate. We trained DyViA-GAN on a publicly available longitudinal dataset of a large cohort of mostly white women in the United States of age 65 years or above. Thus, it generated, for each individual, continuous phenotype trajectories, along with a corresponding region of acceptable predictions, for an age range of 66 to 98 years, for eight different combinations both with and without involving the covariate. The prediction results were subjected to rigorous quality-control and multiple comparative analyses. Our results clearly demonstrate the potential of generative deep learning frameworks in healthspan research.

  bioinformatics2026-04-27v5

• A Robust and Integrated Framework for Cross-platform Adaptation of Epigenetic Clocks in Cell-free DNA Sequencing

  Li, G.; Huang, W.; Zhao, X.; Wu, J.; Guo, Y.; Chen, L.; Cao, X.; Yang, Z.; Jiang, S.; Hu, B.; Wang, Y.; Tan, D.; Tong, V.; Tang, C.; Feng, X.; Hu, X.; Ouyang, C.; Zhou, G.

  Abstract

  Epigenetic clocks trained on methylation arrays generalize poorly to high-throughput sequencing (HTS) of cell-free DNA (cfDNA). Using paired array and HTS replicates, we systematically identified requirements to bridge this platform gap and developed a standardized, model-agnostic adaptation framework. Optimal performance requires maintaining at least 10 x mean target depth, utilizing L2-weighted regularization, implementing targeted beta-value imputation and transfer learning. A combined framework using these strategies significantly enhanced legacy clock performance across independent aging and disease cohorts, enabling robust, minimally invasive biological age assessment without compromising biological interpretability.

  bioinformatics2026-04-27v4

• Uniform pre-processing of bacterial single-cell RNA-seq

  Oakes, C. G.; Beilinson, V.; McFall-Ngai, M. J.; Pachter, L. G.

  Abstract

  Bacteria are highly heterogeneous, even under controlled conditions, making single-cell RNA sequencing (scRNA-seq) essential for studying microbial diversity and symbiosis. Since its first application in 2015, bacterial scRNA-seq has expanded, but different assays depend on distinct, custom, in-house preprocessing making it difficult to analyze data as part of a unified workflow. The kallisto-bustools suite of tools has enabled uniform pre-processing of eukaryotic scRNA-seq while also reducing time and resource demands for pre-processing, but is not optimized for bacterial scRNA-seq. We adapt kallisto-bustools to be suitable for reads generated from operons, as well as for a much shorter gene length distribution, and show that it can efficiently and accurately quantify bacterial scRNA-seq. Our work provides a scalable foundation for uniform pre-processing of microbial single-cell transcriptomics.

  bioinformatics2026-04-27v3

• Unified imputation of missing data modalities and features in multi-omic data via shared representation learning

  Nambiar, A.; Melendez, C.; Noble, W. S.

  Abstract

  Multi-omic studies promise a more comprehensive view of biological systems by jointly measuring multiple molecular layers. In practice, however, such datasets are rarely complete: entire molecular modalities may be missing for many samples, and observed modalities often contain substantial feature-level missingness. Existing imputation approaches typically address only one of these two problems, relying either on feature-level imputation within a single modality or on pairwise translation models that cannot accommodate arbitrary combinations of missing modalities. We present MIMIR, a deep learning framework for unified multi-omic imputation of bulk data that addresses both missing modalities and missing values through shared representation learning. MIMIR first learns modality-specific representations using masked autoencoders and then projects these representations into a common latent space, enabling reconstruction from any subset of observed modalities. Evaluated on pan-cancer multi-omic data from The Cancer Genome Atlas, MIMIR consistently outperforms baseline methods across a range of missing-modality and missing-value scenarios, including missing completely at random and missing not at random settings. Analysis of the learned shared space reveals structured cross-modal dependencies that explain modality-specific differences in imputation accuracy, with transcriptional and epigenetic modalities forming a strongly aligned core and copy number variation contributing more distinct signal. Together, these results demonstrate that shared representation learning provides an effective and flexible foundation for multi-omic imputation under heterogeneous patterns of missingness.

  bioinformatics2026-04-27v2

• Risk Based Prediction of Novel AMR Variants Using Protein Language Models

  Wood, J. J.; Portelli, S.; Ascher, D. B.; Furnham, N.

  Abstract

  Antimicrobial resistance (AMR) is among the most pressing global health threats of the 21st century, with the potential to thrust modern medicine back into a pre-antibiotic era. Resistance can arise through diverse mechanisms, including genomic mutations that prevent antibiotics from reaching or acting on their targets. To limit the spread of AMR, surveillance systems must detect both known and emerging resistance markers. Here we present AMRscope, a model trained on ESM2 protein language model embeddings of single mutations for prediction of resistance likelihood, combined with a rigorous evaluation framework. This tool is applied across antibiotic-interacting proteins of different bacterial species, including WHO priority pathogens, such as rifampicin-resistant M. tuberculosis and carbapenem-resistant P. Aeruginosa. Performance on random splits achieves a competitive accuracy, F1 and MCC of 0.88, 0.87 and 0.75, respectively, while additional splitting strategies demonstrate transfer of predictive power to unseen organisms or genes. Moreover, in silico deep mutational scanning and structural mapping across these targets reveals the tool can recover known resistance-associated regions and highlight new candidates. The risk-based outputs complement database matching and resistance element detection tools, providing clinicians and public health agencies with an interpretable and scalable system for AMR surveillance and proactive response.

  bioinformatics2026-04-27v2

• Combining AI structure prediction and integrative modelling for nanobody-antigen complexes

  Sanchez-Marin, M.; Giulini, M.; Bonvin, A.

  Abstract

  Nanobodies exhibit antigen binding a[ffi]nities of the same order as those of antibodies, which, along with their small size and unique structural characteristics, makes them well-suited for therapeutic and diagnostic applications. The lack of coevolutionary signals in nanobody-antigen complexes together with the broad complementary determining region 3 loop (CDR3) conformational space poses a challenge for predicting the 3D structure of those complexes with computational modelling and artificial intelligence-based methods. In this context, physics-based information-driven docking can provide an alternative solution. This study evaluates the state-of-the-art of machine learning-based methods for nanobody structure prediction and benchmarks various HADDOCK workflows to model their interaction with antigens using different input nanobody ensembles and information scenarios. We propose an ensemble docking pipeline that achieves high success rates starting from nanobody structural models predicted by AlphaFold2 and ImmuneBuilder. Our results highlight the e[ff]ectiveness of physics-based complex prediction of immune proteins when accurate input structures and su[ffi]cient information to guide the modelling are available.

  bioinformatics2026-04-27v2

• SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data

  An, S.; Keller, M.; Gehlenborg, N.; Hemberg, M.

  Abstract

  Spatially resolved single-cell technologies enable profiling of cells in situ, yet computational approaches that jointly discover multicellular spatial patterns and characterize their molecular programs remain limited. Here we introduce SpatialQuery, a framework that can both identify cellular motifs, i.e. recurrent multicellular co-localization patterns, and perform molecular analyses focused on the motifs. It uncovers genes modulated by spatial contexts through differential expression analysis, and detects coordinated expression changes through covariation analysis. SpatialQuery can identify functional tissue units, and goes beyond pairwise analyses to characterize multicellular interactions. Applications to both spatial transcriptomics and proteomics data uncover cross-germ-layer signaling in gut tube patterning, disease-specific fibrotic and immunosuppressive niches in kidney and colon, and regional determinants of motif-associated transcriptional programs in a mouse brain atlas. SpatialQuery is available as a Python package, and we demonstrate how its light computational footprint enables integration into web-based cell atlas portals for interactive visualization and exploration.

  bioinformatics2026-04-27v2

• Unraveling protein conformational plasticity with PROTEUS

  Caparelli Piochi, L. F.; Karami, Y.; Khakzad, H.

  Abstract

  Protein conformational plasticity underpins allosteric regulation, fold switching, and post-translational modification accessibility, yet no existing method can probe this property at the proteome scale without simulation. Here we show that SimpleFold, a flow-matching protein structure predictor, implicitly encodes conformational plasticity in its internal representations. By comparing per-residue embeddings between the sequence-only regime and the structure-converged regime of the denoising trajectory, we define a zero-shot conformational plasticity score, PROTEUS (PROtein TrajEctory Uncertainty Score), that requires no experimental dynamics data. PROTEUS correctly orders five independent protein classes spanning the full flexibility spectrum, from rigid de novo designed scaffolds to intrinsically disordered proteins that fold upon binding. Per-residue PROTEUS profiles correlate with atomic fluctuations from 1,290 independent molecular dynamics trajectories, and this signal persists after controlling for structure prediction confidence (pLDDT) and sequence-based disorder predictions. At the protein level, PROTEUS achieves AUROC = 0.77 for fold-switch detection, 0.81 for open/closed state discrimination, and 0.93 for identifying proteins with buried phosphorylation sites. Proteome-wide analysis of 4,188 Escherichia coli K-12 proteins reveals that fimbrial adhesins and the type II secretion machinery rank among the most conformationally plastic functional classes, consistent with the structural demands of chaperone-mediated secretion and receptor engagement, while ribosomal proteins score systematically lower. These results establish that PROTEUS provides unsupervised, proteome-scale probing of structural dynamics directly from a generative model.

  bioinformatics2026-04-27v1

• Integrative Bioinformatics Approach to Identify Prognostic Gene Signatures for Risk Stratification in Thyroid Carcinoma

  Malik, S.; Raghava, G. P. S.

  Abstract

  Thyroid cancer is a heterogeneous malignancy with variable outcomes, highlighting the need for reliable biomarkers and effective risk stratification. In this study, we implemented a multi-step integrative framework to identify distinct prognostic biomarker sets using transcriptomic data from 572 thyroid cancer patients. Correlation analysis followed by false discovery rate (FDR) correction revealed significant gene associations. Notably, MAFF (r = 0.25, p = 1.34e-9, FDR = 2.46e-7), NR4A3 (r = 0.24, p = 1.26e-8, FDR = 9.25e-7), and SRF showed strong positive correlations, whereas LOC728264 (r = -0.21, p = 7.39e-7, FDR = 6.36e-6) and VAMP1 (r = -0.20, p = 1.20e-6, FDR = 1.3e-4) exhibited negative correlations with OS. Univariate Cox regression identified several survival-associated genes, including TMEM90B (HR = 10.66, p = 2.88e-5) and PTH1R (HR = 9.88, p = 5.55e-5). LASSO regression further identified 31 key prognostic genes, including 13 potential drug targets predominantly functioning as inhibitors. Machine learning models based on seven independent 20-gene biomarker sets effectively predicted Class 0 (0-1 years), Class 1 (1-3 years), Class 2 (3-5 years), and Class 3 (>5 years), achieving AUC values of 0.91-0.94 and Kappa up to 0.76. An ensemble model further improved prediction (AUC = 0.95, Kappa = 0.72). Incorporating clinical variables (age, gender, stage) enhanced model performance (AUC = 0.96, Kappa = 0.80). Reduced 10- and 5-gene subsets demonstrated consistent yet slightly lower performance (AUC = 0.90 and 0.86, respectively). Collectively, the 20-gene set exhibited the strongest predictive and prognostic potential, highlighting the importance of integrating molecular and clinical features for risk stratification in thyroid cancer. All data and code are openly available (https://github.com/raghavagps/THCA_prognostic_biomarkers), supporting future research in thyroid cancer prediction.

  bioinformatics2026-04-27v1

• Semi supervised GAN for smart microscopy, fast and data efficient cell cycle classification

  Manick, R.; El Habouz, Y.; Guillout, M.; Martin, C.; Bonnet-gelebart, J.; Ruel, L.; Pastezeur, S.; Chanteux, O.; Bouchareb, O.; Tramier, M.; Pecreaux, J.

  Abstract

  Modern optical microscopes are fully motorised; however, transforming them into truly smart systems requires real-time adjustment of acquisition settings in response to detected objects and dynamic biological events. At the core are classification algorithms that commonly depend on customised softwares and are generally designed for narrowly-defined biological applications. In addition, they often require substantial annotated datasets for effective training. We introduce a semi-supervised generative adversarial network (SGAN) for robust cell-cycle stage classification under low-resource conditions, adaptable to diverse cellular structures. The framework combines unlabelled microscopy images with synthetically generated samples to mitigate limited annotation, while preserving stable performance even when the unlabelled subset is class-imbalanced. Tested on the Mitocheck dataset, which features five mitosis classes, the model achieved 93{+/-}2 % accuracy using only 80 labelled per class and 600 unlabelled images. The proposed algorithm is generic and can be readily adapted to new labelling schemes, classification targets, cell lines, or microscopy modalities through transfer learning. SGAN is well suited for integration into automated microscopes, enabling efficient and adaptable image analysis across diverse biological and microscopy applications.

  bioinformatics2026-04-27v1

• SynCom101: A web-based platform for the standardized design of functionally tailored synthetic microbial communities

  Jing, J.; Rockx, S.; Liu, A.; Melkonian, C.; Raaijmakers, J. M.; Garbeva, P.; Medema, M. H.

  Abstract

  Background Synthetic microbial communities (SynComs) are essential tools for dissecting the causal mechanisms in host-microbiota interactions. To date, however, SynCom design suffers from a lack of standardization, typically oscillating between arbitrary strain selection and computational pipelines that misalign with experimental design. As microbiome research transitions toward functionally defined community systems with reproducible experimental outcomes, there is a strong need for a user-friendly platform that integrates multi-dimensional genomic and/or biological data into a standardized and tailored SynComs design. Results Here, we present SynCom101, a web-based platform that democratizes the design of reproducible, hypothesis-driven SynComs. SynCom101 accommodates diverse input formats including genomic annotations and laboratory-obtained phenotypic traits, allowing users to customize their design criteria with high flexibility. The platform utilizes a parsimony algorithm to ensure computational scalability for large datasets, complemented by an optional correlation-aware mode to account for microbial compatibility and co-occurrence patterns when ecological interactions among strains are available. A core innovation of SynCom101 is its suite of trait-weighting modules, which empowers researchers to strategically guide the selection algorithm toward maximal functional trait coverage, the emulation of natural community architectures, or the enrichment of positively correlated microbial assemblages to enhance community stability. We showcase the functionalities of the platform by in silico design of communities from different datasets, demonstrating its capacity to generate concise, functionally prioritized SynComs aligned with targeted design objectives. Conclusion By providing a transparent, parameter-documented workflow, SynCom101 ensures that community design is no longer a "black box" but a reproducible scientific record. This platform establishes a necessary standard for in silico community assembly, facilitating the transition from descriptive microbiome studies toward high-throughput, predictive functional screening and cross-study comparability. Availability SynCom101 can be accessed via the web interface (https://syncom101.bioinformatics.nl/). The datasets used for case studies are available on Zenodo (https://doi.org/10.5281/zenodo.18310451). The source code is available at Git (https://git.wur.nl/jiayi.jing/syncom101).

  bioinformatics2026-04-27v1

• An Extended Clade Framework for Annotated Trees in the Context of Phylogeography and Transmission Tree Inference

  Berling, L.; Colijn, C.

  Abstract

  Bayesian phylogenetic inference produces large samples from a posterior distribution over phylogenetic trees that represents uncertainty in both tree topology and associated variables. Such a collection of trees is hard to interpret and it is common practice to summarize such samples into a single representative tree. Methods for constructing representative trees have largely been restricted to plain tree topologies, encoding only relationships among taxa. Inference with more sophisticated models produce annotated tree objects. These have additional information representing nodes' locations in the case of phylogeography, host information when inferring transmission trees, or sampled ancestor status when incorporating fossil information. Nevertheless, these annotated representations are reduced to a single representative tree, typically using methods developed for plain tree topologies and without accounting for the resulting methodological mismatch. Here, we introduce the concept of an extended clade and investigate an extension of the conditional clade distribution (CCD) model. Through motivating examples and case studies in discrete trait phylogeography and transmission tree reconstruction, we demonstrate limitations of standard summary tree approaches and show how these can be addressed using an extended CCD framework that explicitly incorporates the annotated tree structure.

  bioinformatics2026-04-27v1

• MycorrhizaTracer: A BIOINFORMATIC PIPELINE FOR FUNGI AND PLANT CLASSIFICATION OF SANGER DNA SEQUENCES

  Brekke, T. D.; Weeks, T.; Barber, R. A.; Thomson, I.; Gooda, R.; Gargiulo, R.; Delhaye, G.; Andrew, C.; Kowal, J.; Bidartondo, M.; Martinez-Suz, L.

  Abstract

  Processing Sanger DNA sequences remains a routine yet technically demanding step in many biodiversity and ecological studies, particularly when barcoding large numbers of environmental samples. Manual inspection and editing of trace files, DNA sequence alignment, and classification using taxonomic reference databases is time-consuming, inconsistent, and prone to error. These challenges are compounded in studies involving degraded samples, in-house DNA sequencing, under-described taxa, or when investigators have limited access to computational tools. We present MycorrhizaTracer, an open-source, fully automated pipeline for processing and taxonomically classifying large batches of Sanger sequencing chromatograms. We have optimized it for fungal and plant taxa, but it is adaptable across the tree of life. The pipeline performs quality trimming, consensus generation from bidirectional reads, taxonomic classification via BLAST, clustering, optional salvaging of low-quality sequences, and functional annotation of fungal taxa. Designed for scalability and ease of use, MycorrhizaTracer can process thousands of DNA chromatograms in a matter of hours without the need for an HPC. Accuracy and ecological relevance are ensured by features such as gene region-specific taxonomic filtering and sequence-based clustering of unclassified reads. By streamlining trace-to-taxon workflows, MycorrhizaTracer reduces the burden of manual curation, supports reproducibility, and enables efficient recovery of biodiversity data from Sanger sequences - particularly in field-based or resource-limited research contexts.

  bioinformatics2026-04-27v1

• MOSAIC: a longitudinal phenotypic clock to dissect organismal aging trajectories in C. elegans

  Vaudano, A. P.; Pierron, M.; Stojkovic, L.; Membrez, M.; Bourgeois, M.; Neal, C.; Chimen, M.; Verbakel, L.; Cornaglia, M.; Solari, F.; Mouchiroud, L.

  Abstract

  Interventions that extend lifespan do not necessarily preserve healthspan, the portion of life spent in good health. This disconnect has intensified interest in biological aging clocks as quantitative proxies of organismal health. However, most existing clocks rely on invasive or endpoint measurements, providing static estimates that capture biological age at a single time point and offer limited insight into aging trajectories - the dynamic patterns through which physiological resilience and functional capacity change within individuals over time. Here we combine standardized, high-frequency imaging of individual Caenorhabditis elegans across the lifespan with machine learning to develop MOSAIC (Modular Organismal Signature of Aging In C. elegans), a non-invasive phenotypic clock that estimates biological age longitudinally at single-organism resolution. Leveraging ~3'750 animals, ~230'000 observations and 29 phenotypic features, MOSAIC predicts biological age with high accuracy and resolves organism-wide aging trajectories at high temporal resolution. Beyond age prediction, MOSAIC decomposes biological age into contributions from distinct physiological modules, enabling mechanistic interpretation of organismal decline. Applying MOSAIC to natural lifespan variation, dietary restriction, longevity mutants and pharmacological interventions reveals that lifespan extension can emerge through distinct, time-dependent phenotypic trajectories rather than a uniform slowing of aging. Interventions with similar effects on longevity produce divergent biological-age trajectories and distinct combinations of younger and older traits, highlighting context-dependent physiological trade-offs. MOSAIC provides a scalable, non-invasive framework to repeatedly quantify biological age across the lifespan and to compare interventions based on how they reshape aging trajectories.

  bioinformatics2026-04-27v1

• Integrative Clinical-Molecular Modeling Identifies LRRN4CL as a Determinant of Structural and Functional Myocardial Improvement

  Johnson, E.; Visker, J. R.; Brintz, B. J.; Kyriakopoulos, C. P.; Jeong, J.; Zhang, Y.; Shankar, T. S.; Hillas, Y.; Taleb, I.; Badolia, R.; Amrute, J. M.; Stubben, C. J.; Cedeno-Rosario, L.; Kyriakoulis, I.; Sideris, K.; Ling, J.; Hamouche, R.; Tseliou, E.; Navankasattusas, S.; Ducker, G. S.; Rutter, J.; Holland, W. L.; Summers, S. A.; Hong, T.; Koenig, S. C.; Hanff, T. C.; Lavine, K. J.; Greene, T.; Bailey, S.; Alharethi, R.; Selzman, C. H.; Shah, P.; Guo, H.; Slaughter, M. S.; Kanwar, M. K.; Drakos, S. G.

  Abstract

  Background: Mechanical ventricular unloading and systemic circulatory support with left ventricular assist devices (LVADs) enable myocardial recovery in a subset of advanced heart failure (HF) patients, but predictors and mechanisms of recovery are not well understood. Integrating clinical and molecular data may improve identification of patients most likely to recover and uncover biologically relevant targets in HF. Methods: We collected and analyzed left ventricular apical myocardial tissue and clinical data from 208 patients undergoing LVAD implantation across five centers. Pre-implant transcriptomic profiles (22,373 mRNA transcripts) were integrated with 59 clinical variables using supervised machine learning with repeated cross-validation to identify and prioritize features associated with myocardial recovery, defined as a binary outcome based on improvement in left ventricular ejection fraction (LVEF [≥]40%) and left ventricular end-diastolic diameter (LVEDD [≤]5.9 cm). We also modeled functional (LVEF) and structural (LVEDD) improvement as a continuous outcome without any predefined LVEF and LVEDD pathological thresholds. Feature prioritization was followed by validation in human myocardial tissue and mechanistic interrogation in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). Results: Integrative models achieved modest discrimination for myocardial recovery as a binary categorical outcome (maximum mean cross-validated area under the curve 0.73{+/-}0.15), identifying clinical features such as HF duration, LVEDD, HF pharmacologic therapy, and device configuration. Leucine-rich repeat neuronal 4C-like (LRRN4CL), measured in human myocardium, consistently emerged as a top transcriptomic predictor across both binary and continuous metric models (functional and structural). Higher pre-LVAD LRRN4CL expression was associated with reduced likelihood of myocardial recovery and localized primarily to cardiomyocytes. In iPSC-CMs, LRRN4CL overexpression localized to the sarcoplasmic reticulum, induced transcriptional remodeling characterized by suppression of contractile pathways and activation of stress programs, impaired calcium handling, impaired contraction?relaxation kinetics, and diminished mitochondrial respiratory reserve capacity. Conclusions: Integration of clinical and myocardial transcriptomic data identifies LRRN4CL as a novel marker associated with impaired myocardial recovery following LVAD-mediated ventricular unloading and systemic circulatory support. These findings move beyond predictive modeling, linking integrative computational discovery to cardiomyocyte dysfunction and providing a translational framework for biologically informed risk stratification and therapeutic targeting for myocardial recovery.

  bioinformatics2026-04-26v1

• Are Current AI Virtual Cell Models Useful for Scientific Discovery?

  Bereket, M. D.; Leskovec, J.

  Abstract

  AI models are increasingly developed to predict the effect of perturbations on gene expression, but current benchmarks fail to reliably measure model performance. Here, we argue that new benchmarks that directly measure the value of model predictions for specific scientific discovery outcomes are needed to address this gap. We present PerturbHD, an evaluation framework for AI-enabled hit discovery, to demonstrate the benefits our proposed approach.

  bioinformatics2026-04-25v1

• AI-readiness criteria for biomedical data

  Clark, T.; Caufield, H.; Parker, J. A.; Al Manir, S.; Amorim, E.; Eddy, J.; Gim, N.; Gow, B.; Goar, W.; Hansen, J. N.; Harris, N.; Hermjakob, H.; Joachimiak, M.; Jordan, G.; Lee, I.-H.; McWeeney, S. K.; Nebeker, C.; Nikolov, M.; Reese, J.; Shaffer, J.; Sheffield, N.; Sheynkman, G.; Stevenson, J.; Chen, J. Y.; Mungall, C.; Wagner, A.; Kong, S. W.; Ghosh, S. S.; Patel, B.; Williams, A.; Munoz-Torres, M. C.

  Abstract

  Biomedical research is rapidly adopting artificial intelligence (AI). Yet the inherent complexity of biomedical data preparation requires implementing actionable, robust criteria for ethical and explainable AI (XAI) at the "pre-model" stage, encompassing data acquisition, detailed transformations, and ethical governance. Simple conformance to FAIR (Findable, Accessible, Interoperable, Reusable) Principles is insufficient. Here, we define criteria and practices for reliable AI-readiness of biomedical data, developed by the NIH Bridge to Artificial Intelligence (Bridge2AI) Standards Working Group across seven core dimensions of dataset AI-readiness: FAIRness, Provenance, Characterization, Ethics, Pre-model Explainability, Sustainability, and Computability. Conformance to these criteria provides a basis for pre-model scientific rigor and ethical integrity, mitigating downstream risks of bias and error before AI modeling. We apply and evaluate these standards across all four Bridge2AI flagship datasets, spanning functional genomics to clinical medicine, and encode them in machine-actionable metadata bound to the datasets. This framework sets a benchmark for preparing ethical, reusable datasets in biomedical AI and provides standardized methods for reliable pre-model data evaluation.

  bioinformatics2026-04-24v6

• Characterization of selective pressures acting on protein sites with Deep Learning

  Bergiron, E.; Nesterenko, L.; Barnier, J.; Veber, P.; Boussau, B.

  Abstract

  It is often useful, in the field of molecular evolution, to identify the selective pressures acting on a particular site of a protein to better understand its function. This is typically done with likelihood-based approaches applied to codon sequences in a phylogenetic context. However, these approaches are computationally costly. Here we adapt a linear transformer neural network architecture, which has been shown to be able to reconstruct accurate pairwise distances from sequence alignments, to identify selective pressures acting on individual amino acid sites. We design different versions of the architecture and train and test them on simulations. We compare the results of one of our best models to state-of-the-art likelihood-based methods and find that it outperforms it when it is applied to data that resemble its training data, but that it performs less well when applied to datasets that do not resemble the ones the model has been trained on. In all cases, our approach operates at a fraction of the computational cost of likelihood-based methods. These results suggest that such a neural network architecture can compare very favorably to state-of-the-art approaches to characterize selection pressures acting on coding sequences, but that it must be trained on datasets representative of empirical data.

  bioinformatics2026-04-24v4

• A De Novo Algorithm for Allele Reconstruction from Oxford Nanopore Amplicon Reads, with Application to CYP2D6

  Brown, S. D.; Dreolini, L.; Minor, A.; Mozel, M.; Wong, N.; Mar, S.; Lieu, A.; Khan, M.; Carlson, A.; Hrynchak, M.; Holt, R. A.; Missirlis, P. I.

  Abstract

  The Oxford Nanopore Technologies' sequencing platform offers a path towards bedside genomics, producing long reads that can completely cover a gene of interest, and thus detect any known or novel variant the gene contains. However, the analysis of these long reads to identify actionable genotypes remains challenging and typically requires customization depending on the target gene. Here, we describe a generic algorithm to accurately reconstruct allele sequences derived from long-reads of genomic-amplicon origin. Rather than calling variants directly from these long-reads, our method takes a "sequence-first" approach, performing an unbiased reconstruction of the underlying amplicon sequences to generate high-confidence reconstructed allele sequences. This is done without user input of the expected target gene, allowing for any source amplicon to be reconstructed. These high-confidence reconstructed allele sequences are then compared to the genomic reference sequence of the gene to infer the specific diplotype present in the sample. This approach is agnostic towards the number of genes and alleles present and readily detects novel variants. We demonstrate our approach using three independent data sets for CYP2D6, a diverse and complex gene with over 175 known alleles of clinical significance affecting drug dosing. We show how our approach can accurately recover validated CYP2D6 diplotypes from 20 Coriell samples sequenced using different primer sets, on different Oxford Nanopore Technologies flow cell versions, and to different depths. This includes inferring occurrences of copy number variation from relative abundances of each allele, a critical factor for ascribing functional effects to a diplotype. Further, we demonstrate our approach's utility for other genomic regions, including HLA.

  bioinformatics2026-04-24v3

• Adaptive prediction intervals for polygenic risk scores reveal individual variation in genetic predictability

  Wang, C.; Wang, F.; Bogdan, M.; Masala, M.; Fiorillo, E.; Devoto, M.; Cucca, F.; Belsky, D.; Ionita-Laza, I.

  Abstract

  Polygenic risk scores (PRS) are widely used in post-GWAS analyses to predict complex traits across humans, animals, and plants, yet the uncertainty of these predictions is rarely quantified at the individual level. Here, we introduce a framework for individualized uncertainty quantification based on quantile regression and conformal prediction, enabling the construction of prediction intervals with guaranteed coverage under minimal assumptions. Quantile regression enables adaptive, individual-specific prediction intervals that capture asymmetry and allow interval widths to vary substantially across individuals based on genetic information alone. Applying this framework to 62 traits in the UK Biobank and the ProgeNIA/SardiNIA studies, we show that these intervals maintain valid coverage and reduce uncertainty in risk stratification compared to existing methods, driven by their adaptive construction. Prediction interval width correlates positively with age and BMI, indicating reduced genetic predictability in subsets of the population where genetic effects interact with environmental factors. Our results demonstrate that incorporating uncertainty is essential for interpreting polygenic predictions and provide a principled approach to distinguish individuals whose phenotypes are well explained by genetic predictors from those in whom non-genetic influences dominate.

  bioinformatics2026-04-24v3

• MetaTree: an interactive web platform for aligned hierarchical data visualization and multi-group comparison

  Wu, Q.; Zhang, A.; Ning, Z.; Figeys, D.

  Abstract

  Background: Hierarchical quantitative profiles are widely used in microbiome studies and other domains. However, comparing multiple samples and experimental groups while preserving hierarchical structure remains challenging. Many existing workflows require extensive manual figure assembly or do not support aligned comparisons across conditions on a shared hierarchy. Results: We developed MetaTree, an open-source platform that runs in a web browser for interactive visualization and comparative analysis of hierarchical quantitative data. MetaTree anchors samples, groups, and contrasts between groups to a shared reference hierarchy, preserving one-to-one node correspondence so that the same clade is compared in the same position across views. In addition to visualization, MetaTree integrates statistical testing for comparisons between two groups with false discovery rate (FDR) control, enabling users to identify clades with consistent differences between conditions and interpret them in hierarchical context. MetaTree also provides user configurable controls for visual encoding, filtering thresholds, label density, and layout, allowing figures to be adapted to different datasets and reporting needs. The interface remains usable for large hierarchies through interactive navigation, adaptive label handling, and branch collapsing. Conclusions: MetaTree is an installation-free web platform (https://byemaxx.github.io/MetaTree) for topology-consistent visualization and comparison of hierarchical profiles, supporting coordinated multi-panel exploration and automated comparison matrices to enable rapid generation of publication-ready figures for microbiome and other hierarchical datasets.

  bioinformatics2026-04-24v3

• Modeling causal signal propagation in multi-omic factor space with COSMOS

  Dugourd, A.; Lafrenz, P.; Mananes, D.; Paton, V.; Fallegger, R.; Bai, Y.; Kroger, A.-C.; Turei, D.; Li, Y.; Trogdon, M.; Nager, D.; Deng, S.; Shen, C.; Lapek, J. D.; Shtylla, B.; Saez-Rodriguez, J.

  Abstract

  Understanding complex diseases requires approaches that jointly analyze omics data across multiple biological layers, including signaling, gene regulation, and metabolism. Existing data-driven multi-omics analysis methods, such as multi-omics factor analysis (MOFA), can identify associations between molecular features and phenotypes, but they are not designed to integrate existing mechanistic molecular knowledge, which can provide further actionable insights. We introduce an approach that connects data-driven analysis of multi-omics data with systematic integration of mechanistic prior knowledge using COSMOS+ (Causal Oriented Search of Multi-Omics Space). We show how factor analysis output can be used to estimate activities of transcription factors and kinases as well as ligand-receptor interactions, which in turn are integrated with network-level prior-knowledge to generate mechanistic hypotheses about paths connecting deregulated molecular features. We apply this approach on a novel multi-omics dataset of cell line models of breast cancer resistance to evaluate the ability of such mechanistic hypotheses to identify resistance drivers, as well as a breast cancer patient cohort. Our approach offers an interpretable framework to generate actionable insights from multi-omic data particularly suited for high dimensional datasets.

  bioinformatics2026-04-24v3

• Gene-First Identity Construction for Robust Cell Identification in Single-Cell Transcriptomics

  Yang, L.; Huang, Z.; Cai, J.; Xin, H.

  Abstract

  The precise delineation of cell types is fundamental to single-cell transcriptomics, yet current clustering pipelines often violate an axiomatic principle: hierarchical consistency. Existing methods measure cell-to-cell distances within a fixed global feature space, disregarding the fact that biological distinctions are inherently context-dependent lineage separation requires different gene programs than subtype resolution. Mathematically, this implies that the similarity metric itself should not be a static functional, but a pair-dependent energy functional evaluated within a specific Hilbert subspace determined by the biological comparison at hand. The challenge lies in the fact that allowing pair-dependent metrics typically destroys the global geometric consistency required for downstream analysis, unless the family of Hilbert subspaces is given strong biological structure. To resolve this geometric dilemma, we introduce GeCCo (Gene Co-expression Constructed identity), which constructs identities by projecting cells onto a rigorously derived hierarchy of gene programs. To construct this hierarchy, GeCCo first quantifies Boolean regulatory logic via the {varphi} coefficient, and subsequently employs a greedy topological inference to organize genes based on their synergistic and antagonistic relationships. Benchmarking on human immune atlases demonstrates that GeCCo achieves superior hierarchical consistency, ensuring that globally inferred cell identities rigorously match locally refined subtypes. Furthermore, in pancreatic endocrine progenitors, GeCCo resolves a hidden mitotic bridge state, suggesting a concentrated division phase prior to differentiation. Ultimately, GeCCo shifts the paradigm from ad hoc clustering to programmatic cell typing, offering a mathematically grounded framework for scalable atlases of cellular discovery.

  bioinformatics2026-04-24v2

• Ancestra: A lineage-explicit simulator for benchmarking B-cell receptor repertoire and lineage inference methods

  Hassanzadeh, R.; Abdollahi, N.; Kossida, S.; Giudicelli, V.; Eslahchi, C.

  Abstract

  High-throughput B-cell receptor sequencing has transformed the analysis of adaptive immunity, but benchmarking clonal grouping and lineage reconstruction methods remains limited by the absence of datasets with known evolutionary histories. Here we present Ancestra, a lineage-explicit simulator of B-cell receptor heavy-chain affinity maturation. Ancestra models stochastic V(D)J recombination, context-dependent somatic hypermutation, affinity-based selection and clonal expansion while recording complete parent-child relationships and mutation events. The framework generates BCR heavy-chain sequence datasets together with their corresponding ground-truth lineage trees, enabling direct benchmarking of lineage-aware analytical methods. Across simulations, Ancestra recapitulates key properties of human repertoires, including complementarity-determining region 3 length distributions, amino-acid usage patterns, junctional mutation patterns consistent with IMGT criteria and heterogeneous branching topologies. Simulated lineages also reveal multi-label lineage trees, in which identical nucleotide sequences can arise independently along distinct evolutionary paths. Ancestra provides a practical foundation for rigorous benchmarking of lineage-aware immune repertoire analysis.

  bioinformatics2026-04-24v2

• scConcept enables concept-level exploration of single-cell transcriptomic data

  Chen, H.; Li, Y.

  Abstract

  Interpreting high-dimensional single-cell transcriptomic data remains challenging, as existing methods rely on latent representations or prior knowledge that require extensive post hoc analysis to derive biologically meaningful insights. Topic models provide interpretable gene-level signals but often produce redundant and coarse-grained programs that are difficult to translate into coherent biological concepts. While recent foundation models and large language models (LLMs) show promise, they are not readily applicable to large-scale single-cell data or fail to provide structured, cell-level interpretations. Here we present scConcept, a framework that introduces concept-level representation by transforming gene-level topic representations into structured, human-interpretable biological concepts. By integrating neural topic modeling with LLMs, scConcept distills fragmented gene programs into semantically coherent concepts defined by a biological label, description, and gene set, and quantitatively maps them back to individual cells. Across 16 single-cell datasets, scConcept improves clustering performance by 27.1\% and interpretability by 50.7\% over state-of-the-art methods. These concept-level representations enable interpretable cell-state annotation and capture gene programs that generalize across datasets. In cancer applications, scConcept identifies clinically relevant programs associated with tumor progression and patient survival, and links them to candidate therapeutic targets. Together, scConcept establishes concept-level representation as a general and scalable abstraction for interpretable single-cell analysis.

  bioinformatics2026-04-24v1

• Probabilistic coupling of cellular and microenvironmental heterogeneity by masked self-supervised learning

  Kojima, Y.; Tanaka, Y.; Hirose, H.; Chiwaki, F.; Nishimura, K.; Hayashi, S.; Itahashi, K.; Ishikawa, M.; Shimamura, T.; Mano, H.

  Abstract

  Spatial omics technologies have advanced to single-cell resolution, enabling systematic analysis of tissue microenvironments alongside cellular-state heterogeneity. However, computationally defining microenvironmental states at single-cell resolution and identifying representations most informative for biological discovery remain major challenges. Here we present Mievformer, a Transformer-based masked self-supervised framework that learns microenvironmental embeddings by encoding neighboring cellular states and relative spatial configurations to parameterize the conditional distribution of continuous cell states at central spatial positions. Through InfoNCE optimization, Mievformer learns representations that capture the relative enrichment of cell states across microenvironments, formalized as a conditional density ratio, thereby enabling probabilistic inference of the coupling between microenvironmental and cellular heterogeneity. Mievformer outperformed existing methods in niche clustering on simulated spatial transcriptomics data and achieved the highest average performance across five real datasets spanning three spatial transcriptomics platforms when evaluated using DREC, a ground-truth-free metric that most strongly correlated with ground-truth performance in simulations. Beyond conventional clustering, Mievformer enables identification of cellular subpopulations based on their microenvironmental distribution and detection of gene-expression signatures associated with colocalization of specific cell populations. Together, these results establish Mievformer as a quantitatively robust and biologically informative framework for learning microenvironment representations in spatial omics.

  bioinformatics2026-04-24v1

• Efficient and scalable modelling of cotranscriptional RNA folding with deterministic and iterative RNA structure sampling

  Courtney, E.; Choi, E.; Ward, M.; Lucks, J. B.

  Abstract

  RNA structure sampling is central to modelling RNA ensembles, yet stochastic sampling methods are non-exhaustive, scale poorly, and are biased towards low-free-energy structures, while current suboptimal folding approaches generate an unpredictable exponential number of structures. These limitations are particularly problematic for modelling cotranscriptional folding, where vectorial synthesis continuously reshapes the energy landscape during transcription, stabilising transient out-of-equilibrium structures. Here we introduce iterative sampling, a deterministic framework that enumerates unique RNA secondary structures in strict order of increasing free energy, enabling progressive and exhaustive exploration of the structure space up to an arbitrary stopping criterion. To implement this approach, we developed two scalable algorithms, iterative deepening and a persistent data structure approach, that incrementally traverse the expansion tree by evolving partial structures in place, avoiding redundant recomputation and fixed energy windows. Implemented in memerna, this approach achieves orders-of-magnitude speedups over existing tools (10x over ViennaRNA; 100x over RNAstructure). Integration within the sample-and-select framework (R2D2) improves structural diversity and identifies conformations with greater agreement with experimental data. Comprehensive sampling further enables direct comparison of equilibrium and cotranscriptionally restrained ensembles. Analysis of the resulting structural probability distributions uncovers kinetic traps and putative transcriptional pause sites, supporting an intuitive cotranscriptional folding mechanism in which local 3'-hairpin formation transiently stabilises upstream structure to delay large-scale rearrangement. Together, these results establish iterative sampling as a scalable and general framework for resolving out-of-equilibrium RNA cotranscriptional folding.

  bioinformatics2026-04-24v1

• Verticall: A fast and robust tool for recombination detection in large-scale bacterial genomic datasets

  Odih, E. E.; Wick, R. R.; Holt, K. E.

  Abstract

  The inference and removal of horizontally acquired genomic regions is a crucial step in phylogenomics analyses for evolutionary studies. Existing tools perform well on clonal lineage-focused datasets on the scale of hundreds of genomes, but are limited in their ability to analyse larger or more diverse datasets. Here we present Verticall, a tool to identify recombinant regions in bacterial assemblies and generate recombination-free phylogenies, which scales to thousands of genomes from clonal to genus-level diversity. Verticall uses a non-parametric approach to assign genomic regions as horizontally or vertically related based on the distribution of pairwise genetic distances between genomes. Recombination-free phylogenetic trees may be inferred by either calculating a pairwise genetic distance matrix from vertical-only regions (distance-tree approach) or by pairwise comparisons of all genomes to a reference and then masking horizontally acquired regions in a pseudo-alignment to the reference (alignment-tree approach). We demonstrate Verticall's performance using four publicly available whole-genome sequence datasets of varying sample sizes (range: 154 - 4,857 genomes) and evolutionary scales (ranging from within-lineage to genus-wide diversity). Across all four datasets, Verticall showed comparable or superior performance to the established tools Gubbins and ClonalFrameML in terms of computational efficiency, plausibility of inferred phylogenetic trees, and recovery of temporal signal for molecular dating. Our results show that Verticall is a useful tool to more efficiently and accurately detect recombination, particularly applied to datasets for which existing tools are limited, including large datasets with hundreds to thousands of genomes and those that span entire species or genera. Verticall is available free and open source at https://github.com/rrwick/Verticall.

  bioinformatics2026-04-24v1

• Systematic Evaluation of AlphaFold2 and OpenFold3 on Protein-Peptide Complexes

  Fayetorbay, R.; Timucin, A. C.; Timucin, E.

  Abstract

  Protein-peptide interactions are important mediators of diverse biological processes. While deep learning has revolutionized protein structure prediction, comparative evaluation of these methods, specifically for protein-peptide complexes, remains an area of active investigation. Here, we present a systematic benchmarking of AlphaFold2 (AF2) and OpenFold3 (OF3) on a curated, non-redundant dataset of 271 protein-peptide complexes evaluated under CAPRI peptide criteria, partitioned into disordered (IDR) and structured (Non-IDR) peptide subsets. Results show that AF2 consistently outperformed OF3 across both subsets in overall success rate and proportion of high-quality models, while both methods exhibited comparable global fold prediction accuracy. We further demonstrate that AF2 exhibited memorization on a large set of protein-peptide complexes that were in its training data. Analysis of built-in and post-hoc confidence scores demonstrated that PAE-derived metrics, particularly pDockQ2, LIS, and ipSAE, provided the most reliable proxies for structural accuracy in AF2 predictions, whereas OF3's PAE distributions substantially diminished the discriminative power of its derived scores. Furthermore, we find that canonical DockQ threshold cutoffs for protein-protein complexes are not directly transferable to protein-peptide complexes, underscoring the need for method- and dataset-specific calibration. Peptide sequence composition and length were identified as potential modulators of prediction success, with glycine-rich short peptides and long receptors posing challenges to both methods. Collectively, these findings establish a peptide-specific evaluation framework and highlight the need for dataset/method-calibrated metrics to support the continued development of structure prediction tools for protein-peptide interactions.

  bioinformatics2026-04-24v1

• H2O: A Foundation Model Bridging Histopathology to Spatial Multi-Omics Profiling

  Gu, Y.; Wu, Z.; Yan, R.; Wang, Z.; Li, Y.; Lin, S.; Cui, Y.; Lai, H.; Luo, X.; Zhou, S. K.; Yuan, Z.; Yao, J.

  Abstract

  Spatial omics technologies have revolutionized the molecular profiling of tissues but remain constrained by high costs and limited scalability. While hematoxylin and eosin (H&E) staining is ubiquitous, it lacks molecular specificity. Here, we present H2O, a generalist AI framework that bridges the modality gap between histopathology and spatial multi-omics, enabling the direct inference of spatial transcriptomics (ST) and proteomics (SP) landscapes from routine H&E images. H2O integrates Vision Transformers (ViT) with Large Language Models (LLM) via contrastive learning to align histological morphology with semantic molecular knowledge. This cross-modal approach allows the model to incorporate spatial expression profiles into histological pattern recognition, effectively decoding the molecular heterogeneity underlying tissue morphology. Trained on a pan-tissue dataset of 1.3 million paired H&E-spatial patches across 25 organs and cancer types, H2O predicts spatial omics expression from histology with high concordance to sequenced measurements and consistently outperforms state-of-the-art models across three cancer benchmarks. Notably, H2O recovers the MIF-CD74/CD44 signaling axis directly from H&E images, highlighting its capacity to infer biologically meaningful cell-cell communication without molecular profiling. Applying on three additional public cohorts covering fetal and paediatric thymus tissues, human metastatic lymph node, and breast cancer, encompassing human development, 3D spatial frameworks, and integrative multi-omics, H2O yields biologically concordant insights, demonstrating superior accuracy, robustness, and generalizability across real-world applications in diverse scenarios. H2O converts routine histopathology into a portal for spatially resolved multi-omics profiling by computationally generating transcriptomic and proteomic landscapes, thereby enhancing tissue phenotyping and enabling scalable, integrative tissue-atlas construction.

  bioinformatics2026-04-24v1

• Genomic dialects: How amino acid properties and the second codon base shape the informational accents of life

  Martinez, O.; Ochoa-Alejo, N.

  Abstract

  Codon Usage Bias (CUB) is a fundamental feature of genomic architecture, reflecting a balance between mutational pressure and natural selection. We propose a "genomic dialects" framework, where species-specific CUB profiles represent "informational accents" constrained by biochemical and structural requirements. Utilizing a normalized informational index based on Shannons entropy, we analyzed CUB profiles for 18 amino acids across 1,406 species from the three domains of life. Linear models were employed to investigate the relationship between CUB and physicochemical properties, including Saiers second-codon-base classification, molecular volume, hydrophobicity, aliphatic/aromatic status, and dissociation constants. CUB distributions are highly skewed, with >52% of values below 0.1, suggesting a near-optimal use of the genetic codes potential. We demonstrate that amino acid properties significantly influence CUB, with Saiers classification explaining up to 69% of variance in Archaea and {approx}47% across all taxa. Hydrophobic amino acids (Q1 class) consistently exhibit higher average CUB than hydrophilic ones, particularly in microbes. Individual species models reveal extreme correlations; for example, in the alga Chlamydomonas reinhardtii, Saier classes explain >95% of CUB variance. Finally, we show that CUB-based dendrograms represent phenetic similarity ("genomic accents") rather than reliable phylogenetic reconstructions, as they rarely coincide with the true Tree of Life. Our findings indicate that the "rules" of genomic dialects are largely anchored in the dual requirements of translational fidelity and protein stability. The observed "informational accents" are proximately governed by the metabolic and genomic machinery under the constraints of the drift-barrier hypothesis. This study provides a robust framework for understanding how the physical realities of amino acids have shaped the evolution of the genetic codes informational use across the tree of life.

  bioinformatics2026-04-24v1

• Turep: Detecting cross-cancer tumor-reactive T cells in single-cell and spatial transcriptomics data

  Liu, W.; Tung, C.-H.; Sevick-Muraca, E. M.; Zhao, Z.

  Abstract

  Tumor-infiltrating lymphocytes are essential for anti-tumor immunity, yet distinguishing tumor-reactive T cells from non-reactive bystander cells remains a significant challenge. Existing signatures, often derived from single cohorts, lack robustness in cross-cancer prediction. We present Turep, a deep learning method designed for robust, cross-cancer prediction of tumor-reactive T cells using single-cell or spatial transcriptomics data. By integrating paired single-cell RNA and T cell receptor sequencing data from seven human malignancies, we identified a pan-cancer tumor-reactive gene signature and leveraged generative data augmentation to address data imbalance. Turep consistently outperformed existing biomarkers, achieving a mean area under the receiver operating characteristic curve of 0.870 across cancer types. In validation across diverse cohorts, we found that Turep-predicted tumor-reactive T cell proportions could predict clinical response to immunotherapy. Furthermore, extending Turep to spatial transcriptomics revealed that tumor-reactive T cells preferentially resided in spatial niches where target cells exhibited elevated antigen presentation. Overall, Turep provides a powerful, generalizable tool for identifying tumor-reactive T cells and their spatial architectures, facilitating personalized cancer immunotherapy strategies.

  bioinformatics2026-04-24v1

• CellPulse: A Foundation Model of Coordinated Gene Dynamics Simulating Viral Infectious Diseases

  Liu, D.; Zhu, X.; Zhang, L.; Xu, D.; Lou, J.; Xiong, X.; Ren, Y.; Wu, Y.; Zhou, X.

  Abstract

  Understanding how cells respond to perturbations like viral infections requires models capturing coordinated gene dynamics. However, current gene expression foundation models are predominantly reliant on single-cell data and static gene expression, limiting their applicability in real clinical scenarios. We present CellPulse, a direction-aware foundation model trained on the Virus Stimulated Atlas (VISTA), a newly curated atlas of over 23 million bulk RNA-sequencing differential expression profiles from viral infections. CellPulse models the direction and magnitude of gene expression changes via a structured representation of differential expression and a direction-aware attention mechanism, enabling the learning of coherent regulatory programs. It shows powerful diagnosing capability by accurately classifying 31 distinct virus types across diverse clinical and laboratory samples, solely from host transcriptional signatures. Crucially, without prior knowledge injection, CellPulse's interpretability reveals virus-associated host factors that mediate infection. Using a selection of host factors for in silico drug screening yielded numerous compounds with confirmed efficacies in wet-lab assays, while cell-based and animal experiments further verified the causal relationship between host targets and viral infections. Overall, CellPulse represents a generalizable foundation model for deciphering coordinated gene dynamics from bulk transcriptomics, bridging host response modeling with clinical relevance and therapeutic discovery for infectious diseases and beyond.

  bioinformatics2026-04-24v1

• Small Area Estimation of Forest Volume Using Mixed Effects Random Forests and Multi-Source Remote Sensing Data

  Vangi, E.

  Abstract

  Accurate estimation of forest growing stock volume (GSV) at fine spatial scales is essential for sustainable forest management, carbon accounting, and local decision-making. However, traditional forest inventories often lack sufficient sampling density to provide reliable estimates for small areas. This study evaluates the performance of two small area estimation approaches: the Empirical Best Predictor (EBP) based on a nested-error linear regression model, and the Mixed-Effects Random Forest (MERF) for estimating GSV at the forest stand level using multi-source remote sensing data. The analysis was conducted in the Vallombrosa Nature Reserve (Italy), integrating field measurements from 101 plots with auxiliary variables derived from Sentinel-2 imagery and airborne LiDAR. Both methods were applied to estimate the mean and total GSV across 658 forest stands, many of which lacked direct observations. Model performance was assessed using spatial cross-validation, and uncertainty was quantified using root-mean-square error (RMSE). Results show that MERF outperformed EBP in predictive accuracy, achieving higher R2 (0.67 vs. 0.37) and lower RMSE (151 m3 ha-1 vs. 202 m3 ha-1). MERF also produced more stable and precise uncertainty estimates, with improved coverage of observed values. While both methods yielded comparable total GSV estimates, EBP exhibited greater variability and sensitivity to model assumptions. In contrast, MERF effectively captured non-linear relationships and handled multicollinearity among predictors, though at the cost of reduced interpretability and higher computational demand. Overall, findings highlight the advantages of integrating machine learning with mixed-effects modeling for SAE in forestry, particularly under conditions of sparse sampling and complex ecological variability.

  bioinformatics2026-04-24v1

• A Systematic Evaluation of Single-Cell Batch Integration Metrics and sBEE: A Robust New Metric

  Myradov, M.; HOUDJEDJ, A.; Tastan, O.; Kazan, H.

  Abstract

  Single-cell RNA sequencing (scRNA-seq) datasets generated across laboratories and experimental conditions often exhibit batch effects that obscure biological variation. Numerous computational methods for batch integration have been developed, making rigorous benchmarking critical. Evaluation metrics are central to assessing method performance; however, existing metrics capture only partial aspects of integration quality and often rely on implicit assumptions about cell distributions in the embedding space. Consequently, benchmarking studies frequently report discordant rankings of batch integration methods across metrics, complicating interpretation and method selection. Here, we systematically evaluate widely used metrics under controlled scenarios that isolate common integration challenges, including imbalanced batch composition, partial cell-type overlap, and varying cluster geometries. By stress-testing metrics under these scenarios, we identify the conditions under which each metric succeeds or fails. Based on these observations, we introduce sBEE (single-cell Batch Effect Evaluator), a unified metric that jointly evaluates cross-batch distance relationships and local neighborhood batch composition. Across diverse scenarios, sBEE provides stable assessments of mixing quality and remains robust to failure modes that affect existing metrics. Together, our work provides a systematic evaluation of batch integration metrics and introduces a unified metric for a more reliable assessment of integration quality.

  bioinformatics2026-04-24v1

• MSAgent: An Evidence Grounded Agentic Framework for LLM-driven Scientific Exploration in Mass Spectrometry-based Metabolomics

  Li, Y.; Zhong, Y.; Liu, P.; Yusheng, T.; Zhan, H.; Xia, J.

  Abstract

  Mass spectrometry (MS) is a cornerstone high-throughput technology for molecular discovery, yet the reliable elucidation of chemical structures remains a formidable, expert-dependent bottleneck. Currently, achieving a reliable molecular identification from raw mass spectra necessitates a manual assembly, a labor-intensive ordeal of heuristic reasoning and the tedious integration of siloed computational tools, perpetuating a profound throughput gap between rapid data acquisition and the glacial pace of structural annotation. Here we present MSAgent, an autonomous agentic framework that bridges the gap between computational automation and expert intuition by emulating the cognitive logic of human specialists. By orchestrating a MSToolbox of over 50 domain-specific tools via Large Language Models (LLMs), MSAgent dynamically unifies the analytical pipeline into a scalable, evidence-grounded workflow, allowing for intent-aware planning, cross-resources outputs synthesis, and visual mechanistic interpretation within traceable reasoning chains and evidence-backed analytical reports. We evaluated MSAgent across multiple open benchmarks, including the established community challenges - Critical Assessment of Small Molecule Identification (CASMI) 2016/2022, CANOPUS, and LLM-oriented test cases. On CASMI, MSAgent consistently boosts retrieval performance by over 10% MRR across diverse benchmarks while ensuring high reliability, improving or preserving ranks in 95% of cases. For more challenging molecular de novo tasks on CANOPUS, MSAgent builds upon the outputs of baseline models with consistent refinement, yielding over a 40% average gain in Tanimoto similarity for ground-truth recovery. In addition, MSAgent demonstrates remarkable advantages in eliminating the hallucination phenomenon over LLMs without domain tool support, producing better-calibrated confidence (Pearson r = 0.438 vs -0.219 for gpt-4o). It improves exact-match rate by 38.8% over gpt-4o in candidate discrimination tasks, and achieved a 64% success rate in recommending high-quality candidate structures with Tanimoto similarity more than 0.7, where gpt-4o predominantly selected candidates with similarity below 0.3. Our work enables high-throughput mass spectrometry data to be analyzed in an intent-driven and automated manner, lowering the analysis barrier for no-expert to deliver molecular identification result with transparent analytical process, and accelerating discovery in metabolism and related fields by bridging the gap between experimental data acquisition and computational interpretation.

  bioinformatics2026-04-24v1

• SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data

  Hemberg, M.; An, S.; Gehlenborg, N.; Keller, M.

  Abstract

  Spatially resolved single-cell technologies enable profiling of cells in situ, yet computational approaches that jointly discover multicellular spatial patterns and characterize their molecular programs remain limited. Here we introduce SpatialQuery, a framework that can both identify cellular motifs, i.e. recurrent multicellular co-localization patterns, and perform molecular analyses focused on the motifs. It uncovers genes modulated by spatial contexts through differential expression analysis, and detects coordinated expression changes through covariation analysis. SpatialQuery can identify functional tissue units, and goes beyond pairwise analyses to characterize multicellular interactions. Applications to both spatial transcriptomics and proteomics data uncover cross-germ-layer signaling in gut tube patterning, disease-specific fibrotic and immunosuppressive niches in kidney and colon, and regional determinants of motif-associated transcriptional programs in a mouse brain atlas. SpatialQuery is available as a Python package, and we demonstrate how its light computational footprint enables integration into web-based cell atlas portals for interactive visualization and exploration.

  bioinformatics2026-04-24v1

• GenNA: Conditional generation of nucleotide sequences guided by natural-language annotations

  Shen, Y.; Cao, G.; Wu, J.; Chen, D.; Feng, C.; Chen, M.

  Abstract

  Deciphering the mapping between linear biomolecular sequences and complex biological functions remains a central challenge in genomics. Although existing generative nucleotide language models have made substantial progress in modeling sequence distributions, they generally lack explicit access to high-level biological semantics, limiting their ability to support semantics-guided conditional generation. To address this limitation, we present GenNA, a generative nucleotide foundation model guided by natural-language annotations. GenNA is pretrained on a multimodal nucleotide-text corpus spanning 2,221 eukaryotic species and comprising approximately 416 billion characters, and learns the relationships between sequence patterns and functional annotations within a unified autoregressive framework. Systematic evaluations show that, even without explicit supervision from biological rules, GenNA yields distinguishable perplexity scores in response to semantic mismatches between sequences and functional annotations, to different mutation types, and to perturbations of species labels. Moreover, across a range of natural-language-guided nucleotide generation tasks, the model produces sequences consistent with both target semantics and species context. Overall, GenNA provides a unified framework for natural-language-guided nucleotide modeling and conditional generation, and offers a feasible route toward integrating high-level functional descriptions with low-level sequence design.

  bioinformatics2026-04-24v1

• PanVariants: Best Practice for Pangenome-based Variant Calling Pipeline and Framework

  Yi, H.; Wang, L.; Chen, X.; Ding, Y.; Carroll, A.; Chang, P.-C.; Shafin, K.; Xu, L.; Zeng, X.; Zhao, X.; Gong, M.; Wei, X.; Hou, Y.; Ni, M.

  Abstract

  Background: Although pangenome references offer richer population diversity compared to linear references, current mainstream pangenome-based variant callers are limited to detecting only known variants stored in the graph. To address this limitation, we developed PanVariants, a novel pipeline designed to improve the detection of both known and novel variants accurately. We systematically evaluated its performance against the traditional linear alignment solution (BWA+GATK/Manta) and the existing pangenome-aware solution (DRAGEN/PanGenie) in three contexts: small variants (SNVs/indels) and structural variants (SVs) accuracy in Genome in a Bottle samples, clinical detection on positive samples, and application in cohort-based joint calling. Results: By integrating k-mer-based and mapping-based methods, PanVariants significantly reduced variant errors (FPs + FNs), achieving a 73% reduction compared to BWA+GATK and a 45% reduction compared to DRAGEN for SNVs. Retraining the DeepVariant model with high-quality DNBSEQ data further decreased errors by 15%. For SVs detection, PanVariants attained an F1-score of 89.39%, markedly outperforming DRAGEN (68.18%) and BWA+Manta (58.33%), approaching long-read sequencing performance (95.22%). In validation using clinical positive samples, PanVariants successfully detected all expected pathogenic variants while PanGenie failed. In the cohort joint-calling analysis, PanVariants detected more variants, made fewer Mendelian inheritance errors, and gave better per-sample accuracy than GATK. Conclusions: PanVariants establishes a robust framework and best-practice pipeline for pangenome-based variant detection, achieving both sensitive novel variant discovery and high accuracy for SNVs, indels and SVs. Our systematic evaluation of optional processing steps and input variables offers practical guidance for users. Validated across diagnostic and population-based applications, our findings strongly support the transition from linear to pangenome references in future genomics.

  bioinformatics2026-04-24v1

• SNooPy: a statistical framework for long-read metagenomic variant calling

  Faure, R.; Faure, U.; Truong, T. M. K.; Derzelle, A.; Lavenier, D.; Flot, J.-F.; Quince, C.

  Abstract

  Current long-read single-nucleotide variant callers were designed primarily for genomic data - particularly human genomes. While some have been used on metagenomic data, their underlying assumptions and training procedures fail to account for the inherent complexity of metagenomic samples. To date, no long-read variant caller has been purpose-built for metagenomic applications. To address this gap, we present SNooPy, a SNP-calling tool that implements a new statistical framework tailored to long-read metagenomic data. Unlike previous genomic methods, our approach makes no assumptions about the number of haplotypes present, their evolutionary relationships, or their sequence divergence. We demonstrate that SNooPy outperforms both traditional statistical and deep learning-based SNP callers. Our results suggest that future integration of this framework with deep learning approaches could further enhance variant calling performance. SNooPy is freely available on github.com/rolandfaure/snoopy.

  bioinformatics2026-04-20v2

• Evaluation of deep learning tools for chromatin contact prediction

  Nguyen, T. H. T.; Vermeirssen, V.

  Abstract

  Background: Three-dimensional chromatin organization plays a central role in gene regulation and is commonly measured using Hi-C technology. Recently, deep learning models have been developed to predict Hi-C contact maps from genomic and epigenomic features, offering a computational alternative to costly experimental assays. However, the performance, robustness, and biological interpretability of these models remain unclear due to the absence of systematic benchmarking. Results: We present a comprehensive benchmark to evaluate five Hi-C prediction models, C.Origami, Epiphany, ChromaFold, HiCDiffusion, and GRACHIP, across multiple evaluation criteria, including accuracy, visual fidelity and loop detection. Among all models, Epiphany achieved the strongest overall performance, combining high accuracy, cell-type generalization, realistic image quality and reliable loop detection. Moreover, we evaluated predicted contact maps using four different loop-callers to assess the impact of model choice on loop detection performance. Despite the coarse resolution, many models could recover biologically relevant interactions. Notably, structural map quality was more critical than the choice of loop-caller for reliable detection. Finally, ablation analyses revealed that epigenomic signals are influential features for accurate Hi-C prediction. Despite the use of multiple input modalities in many models, only a limited subset contributed substantially to predictive performance. Conclusions: This study provides a systematic comparison of deep learning models for Hi-C prediction and highlights the importance of specific regulatory signals in reconstructing 3D chromatin organization. The proposed evaluation framework clarifies model behaviours and offers guidance for the development and interpretation of Hi-C prediction methods.

  bioinformatics2026-04-20v2

• LagCI Enables Inference of Temporal Causal Relationships from Dense Multi-Omic Time Series

  Ge, Y.; Bai, S.; Qiang, Z.; Liu, Y.; Wu, Y.; Shen, X.

  Abstract

  Inferring causal relationships from time-series data is critical for uncovering the dynamics of biological regulation. However, in multi-omics studies, this task is often hampered by sparse temporal sampling and the limitations of existing methods. To address this, we developed Lagged-Correlation Based Causal Inference (lagCI), a computational framework designed to identify time-lagged associations by combining comprehensive lag-correlation profiling with a robust statistical filtering scheme. Rather than relying on simple cross-correlation, lagCI analyzes the entire correlation profile and applies a quality-scoring system to filter out spurious associations that often plague high-dimensional datasets. We first tested lagCI on wearable physiological data, where it successfully captured the well-known causal link between physical activity and heart rate, even accounting for variations in lag times between individuals. Moving to high-frequency human multi-omics, we used lagCI to build a directed network of 1,624 molecules connected by over 157,000 predicted interactions. This network didn't just mirror established biology (such as cytokine-hormone crosstalk); it also pointed to specific molecular hubs that seem to orchestrate the timing of metabolic and immune responses. Overall, lagCI provides a data-driven way to extract temporal insights from dense longitudinal omics. We've made the tool available as an R package with multiple interfaces to ensure it's accessible for both bioinformaticians and clinicians.

  bioinformatics2026-04-20v2

• Natively entangled proteins are linked to human disease and pathogenic mutations likely due to a greater misfolding propensity

  Anglero Mendez, M. F.; Sitarik, I.; Vu, Q. V.; Totoo, P.; Stephenson, J. D.; Song, H.; O'Brien, E. P.

  Abstract

  A recently discovered class of protein misfolding involving native entanglements could be a widespread mechanism by which loss-of-function diseases arise. Here, we test that hypothesis by examining if there is any statistical association between proteins predisposed to misfold in this way and a database of gene-disease relationships. We find that globular proteins containing non-covalent lasso entanglements (NCLEs) in their native structure, which are more prone to misfolding, are 61% more likely to be associated with disease, 68% more likely to harbor pathogenic missense mutations, and their misfolding-prone entangled regions are 64% more likely to harbor pathogenic missense mutations. Protein refolding simulations indicate that these disease associated, natively entangled proteins are 2.5-times more likely to misfold than comparable non-disease proteins that lack native NCLEs. These results indicate that native entanglement misfolding, especially in the presence of missense mutations, have the potential to contribute to a wide variety of diseases. More broadly, these findings open an entirely new space of therapeutic targets in which drugs are designed to avoid these misfolded states and increase the amount of folded, functional protein.

  bioinformatics2026-04-20v1

• Genome-wide identification and characterization of the NAC transcription factor family in Cynodon dactylon and their expression during abiotic stresses

  Poudel, A.; Wu, Y.

  Abstract

  Common bermudagrass (Cynodon dactylon) is a highly resilient and cosmopolitan grass widely used for turf, forage, and soil stabilization. Although its genome has been sequenced, little study has focused on characterizing genes underlying its resilience, including the NAC transcription factor family, which is well known for its physiological and stress-related functions. This study aimed to systematically characterize NAC TF genes in the bermudagrass genome and assess their potential roles in abiotic stress tolerance. A total of 237 CdNAC genes were identified and phylogenetically classified into 14 groups, including 40 members in the NAM/NAC1 class, which is associated with plant growth and development, and 23 members in the SNAC class, which is associated with stress responses. Tissue-specific RNA-seq analysis indicated that about one-fourth of CdNAC genes were expressed across all tissues, whereas 13 genes showed relatively higher expression in roots and 9 in inflorescence, suggesting both essential and specialized functions. Stress-responsive expression profiling revealed that 35 CdNAC genes were upregulated in response to drought, 43 to heat, 10 to salt, and 42 to submergence stress. Notably, CdNAC122, 149, and 155, the members of SNAC class, were consistently upregulated across all stress conditions, while others exhibited stress-specific expression, such as CdNAC37, 130, 145, and 199 in drought, CdNAC7, 12, 18, and 29 in heat, CdNAC46 and 151 in salt, and CdNAC9 and 31 in submergence. In contrast, 53 genes were downregulated during different stresses, with most belonging to NAM/NAC1, TERN, or OsNAC7 classes, possibly reflecting suppression of photosynthesis and development-related processes under stress. These results provide the first comprehensive characterization of CdNAC genes, reveal their distinct regulatory roles in abiotic stress responses, and establish a foundation for future functional validation and applications in breeding of stress-resilient bermudagrass.

  bioinformatics2026-04-20v1

• KIR*BLOOM: Accurate KIR genotyping using a new copy number-aware integrated genotype likelihood framework

  Gohar, Y.; Garcia, A. D.; Kichula, K. M.; Norman, P. J.; Dilthey, A. T.

  Abstract

  Killer-cell immunoglobulin-like receptor (KIR) genes, key modulators of natural killer (NK) cell activity, play critical roles in immune response and disease susceptibility. Accurate KIR genotyping from short-read sequencing data remains challenging because of high sequence similarity among genes, extensive copy number variation, and substantial allelic diversity. Here, we present KIR*BLOOM, a likelihood-based approach for KIR genotyping from short-read data that models read depth and sequencing error across alternative genotype configurations. KIR*BLOOM first identifies KIR-relevant read pairs, maps them to a KIR allele database, and reduces the candidate allele space by excluding alleles unlikely to be present. It then infers gene copy number and selects alleles under the inferred copy-number constraints. Finally, variant calling is used to refine CDS sequences and identify potential novel alleles. We evaluated performance on 45 whole-genome sequencing samples with haplotype-resolved assemblies from the HPRC or HGSVC, using Immuannot-derived annotations as ground truth. KIR*BLOOM achieved 99.85% precision, 99.92% recall, and a Jaccard index of 99.77% for copy-number inference. At five-digit allele resolution, it achieved 92.73% precision, 92.69% recall, and an 87.29% Jaccard index, outperforming T1K, GraphKIR, and Geny. Together, these results demonstrate that KIR*BLOOM enables highly accurate KIR genotyping from short-read sequencing data.

  bioinformatics2026-04-20v1

• Longitudinal Phylogenetic Inference of Copy Number Alterations and Single Nucleotide Variants from Single-Cell Sequencing

  Kulman, E.; Kuang, R.; Morris, Q.

  Abstract

  Longitudinal phylogenetic reconstruction reveals how cancers evolve over time and respond to treatments. Advances in targeted single-cell sequencing, combined with longitudinal sampling, now enable detailed longitudinal tracking of single nucleotide variants (SNVs) and copy number alterations (CNAs) at single-cell resolution. Here, we introduce LoPhy, the first method designed to reconstruct the evolution of SNVs and CNAs from these new longitudinal single-cell data. LoPhy is a sequential tree-building algorithm that reconstructs longitudinally-consistent phylogenies of SNVs and CNAs by maximizing a new factorized tree reconstruction objective. The algorithm incrementally grows a clone tree, adding SNVs and CNAs in the order they are observed across time points. Applied to a cohort of 15 acute myeloid leukemias (AMLs) and 4 TP53-mutated AMLs, LoPhy produced phylogenies that are biologically and temporally consistent with clinical observations, with many inferred CNAs validated by orthogonal bulk sequencing from the same cancer. These reconstructions highlight the role of CNAs in disease progression and resistance, revealing that AML clones selected after therapy are often defined by both large-scale CNAs and SNVs. More broadly, LoPhy can help uncover how SNVs and CNAs jointly shape the evolutionary trajectories of individual cancers at single-cell resolution. The LoPhy source code is available under a CC-BY-ND license at https://github.com/ethanumn/LoPhy.

  bioinformatics2026-04-19v3

• DOME Copilot: Making transparency and reproducibility for artificial intelligence methods simple

  Farrell, G.; Attafi, O. A.; Fragkouli, S.-C.; Heredia, I.; Fernandez Tobias, S.; Harrison, M.; Hermjakob, H.; Jeffryes, M.; Obregon Ruiz, M.; Pearce, M.; Pechlivanis, N.; Lopez Garcia, A.; Psomopoulos, F.; Tosatto, S. C. E.

  Abstract

  Unprecedented breakthroughs are being made in life science research through the application of artificial intelligence (AI). However, adherence to method reporting guidelines is necessary to support their reusability and reproducibility. The DOME Copilot solution extracts structured reports of AI methods using a large language model to help interpret manuscripts. It is a fast and efficient resource capable of scaling to annotate the corpus of global AI literature, unlocking value and trust in published methods.

  bioinformatics2026-04-19v1

• Pan-cancer survival modeling reveals structural limits of genomic feature integration in immunotherapy outcomes

  Hassan, W.; Adeleke, S.

  Abstract

  Background Immune checkpoint inhibitors (ICIs) have improved outcomes across multiple cancer types, yet reliable predictors of survival remain limited. While genomic features such as tumor mutational burden (TMB) are widely used, their contribution to predictive modeling in heterogeneous real-world cohorts remains unclear. We evaluated the relative contributions of clinical and whole-genome sequencing (WGS) features in pan-cancer survival modeling. Methods We analyzed 658 patients treated with ICIs with matched WGS data from the Genomics England. Using a leakage-controlled machine learning framework with strict train-test separation, we compared four models: TMB-only, clinical-only, clinical+TMB, and an integrated 11-feature clinico-genomic XGBoost survival model. Model performance was assessed using Harrells concordance index (C-index) with bootstrap confidence intervals. Results TMB alone demonstrated near-random discrimination (C-index 0.50; 95% CI 0.44-0.56). Clinical variables substantially improved predictive performance (0.59; 95% CI 0.53-0.64), with marginal gain from adding TMB (0.59). The integrated model achieved a C-index of 0.60 (95% CI 0.55-0.65). While improvement over TMB alone was significant, incremental gain beyond optimized clinical models was modest. Feature attribution analysis showed that model performance was dominated by clinical variables, with genomic features contributing limited additional signal. Conclusions These findings suggest that, in heterogeneous pan-cancer cohorts, predictive performance is constrained by the underlying data structure, in which dominant clinical signals overshadow genome-scale features. This study highlights fundamental limitations in integrating genomic data into survival models across diverse cancer types and provides a benchmark for future computational approaches.

  bioinformatics2026-04-18v1

• Unsupervised Machine Learning for Adaptive Immune Receptors with immuneML

  Pavlovic, M.; Wurtzen, C.; Kanduri, C.; Mamica, M.; Scheffer, L.; Lund-Andersen, C.; Gubatan, J. M.; Ullmann, T.; Greiff, V.; Sandve, G. K.

  Abstract

  Machine learning (ML) enables adaptive immune receptor repertoires (AIRRs) analyses for biomarker identification and therapeutic development. With the majority of AIRR data partially or imperfectly labeled, unsupervised ML is essential for motif discovery, biologically meaningful clustering, and generation of novel receptor sequences. However, no unified framework for unsupervised ML exists in the AIRR field, hindering the assessment of model robustness and generalizability. Here, we present an immuneML release advancing unsupervised ML in the AIRR field through unified clustering workflows, interpretable generative modeling, integration with protein language model embeddings, dimensionality reduction, and visualization. We demonstrate immuneML's utility in three use cases: (i) benchmarking generative models for epitope-specific sequence generation, assessing specificity and novelty, (ii) systematic evaluation of clustering approaches on experimental receptor sequences against biological properties, such as epitope specificity and MHC, and (iii) unsupervised analysis of an experimental AIRR dataset to examine potential confounding, a practice widespread in related fields but unexplored in AIRR analyses.

  bioinformatics2026-04-18v1

• LagCI Enables Inference of Temporal Causal Relationships from Dense Multi-Omic Time Series

  Ge, Y.; Bai, S.; Qiang, Z.; Liu, Y.; Wu, Y.; Shen, X.

  Abstract

  Inferring causal relationships from time-series data is critical for uncovering the dynamics of biological regulation. However, in multi-omics studies, this task is often hampered by sparse temporal sampling and the limitations of existing methods. To address this, we developed Lagged-Correlation Based Causal Inference (lagCI), a computational framework designed to identify time-lagged associations by combining comprehensive lag-correlation profiling with a robust statistical filtering scheme. Rather than relying on simple cross-correlation, lagCI analyzes the entire correlation profile and applies a quality-scoring system to filter out spurious associations that often plague high-dimensional datasets. We first tested lagCI on wearable physiological data, where it successfully captured the well-known causal link between physical activity and heart rate, even accounting for variations in lag times between individuals. Moving to high-frequency human multi-omics, we used lagCI to build a directed network of 1,624 molecules connected by over 157,000 predicted interactions. This network didn't just mirror established biology (such as cytokine-hormone crosstalk); it also pointed to specific molecular hubs that seem to orchestrate the timing of metabolic and immune responses. Overall, lagCI provides a data-driven way to extract temporal insights from dense longitudinal omics. We've made the tool available as an R package with multiple interfaces to ensure it's accessible for both bioinformaticians and clinicians.

  bioinformatics2026-04-18v1