Latest bioRxiv papers

Category: bioinformatics — Showing 50 items

• Anchors for Homology-Based Scaffolding

  Kaether, K.-K.; Gatter, T.; Lemke, S.; Stadler, P. F.

  Abstract

  Homology based scaffolding orders contigs based on conserved collinearity of homologous sequences across related species. Existing methods often rely on costly whole genome alignments or show limited robustness when integrating multiple references. Here, we introduce an anchor based scaffolding framework that adapts synteny anchors to efficiently infer contig order and orientation relative to one or more reference genomes. Our approach leverages precomputed, sufficiently unique anchors and their respective high confidence homology matches in a greedy approach, combining single reference to multi reference scaffolds using a maximum matching. Across simulated and real datasets, anchor based scaffolding achieves accuracy comparable to state of the art methods. Notably, the approach shows particular strengths in multi reference settings. These results demonstrate that synteny anchor based scaffolding provides an additional tool for homology based scaffolding with robust accuracy and superior performance in multi reference scenarios.

  bioinformatics2026-08-04v5

• Confidence-aware learning for transcriptome-based prediction of OXPHOS genes in Caenorhabditis elegans under incomplete functional annotation

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

  Abstract

  Assigning biological functions to genes remains a major challenge in genomics because reliable functional annotations are often scarce and unevenly distributed. Although transcriptomic datasets capture rich information about gene activity and coordination, exploiting these data for gene function prediction is difficult when only a small number of genes have high-confidence functional assignments and the remaining labels are uncertain. Here, we developed a confidence-aware learning framework that integrates complementary transcriptomic landscapes while explicitly incorporating uncertainty in functional annotations. The framework combines supervised learning from time-resolved bulk RNA-seq data with co-expression analysis of embryonic and adult single-cell transcriptomes. Supervised learning uses a two-round training scheme in which genes supported by limited functional evidence are incorporated only after an initial model has been established using high-confidence annotations. To reduce module-specific biases, we implemented an informed bagging strategy in which genes from individual functional modules are systematically withheld during training and predictions are integrated by consensus across models. We applied this framework to identify genes involved in oxidative phosphorylation (OXPHOS) in Caenorhabditis elegans. Integrating supervised and co-expression evidence prioritized a small set of high-confidence candidate genes with strong predictive performance on an independent test set. Experimental validation showed that disruption of the top-ranked candidate, ril-1, produces phenotypes consistent with impaired OXPHOS function. Our results demonstrate that transcriptomic landscapes can be systematically exploited for gene function prediction under incomplete functional annotation, providing a framework that may be transferable to other biological processes and organisms where reliable annotations remain limited.

  bioinformatics2026-08-04v4

• Longitudinal structural variant phylogenies define tumor evolution under therapeutic selection pressure in metastatic prostate cancer

  Liu, Y.; Lai, J.; Yang, Y.; Markowski, M. C.; Antonarakis, E. S.; De Marzo, A. M.; Yegnasubramanian, S.; Wood, L. D.; Sena, L. A.; Karchin, R.

  Abstract

  Therapeutic pressure shapes tumor evolution by selecting for subclones with distinct genomic architectures. In metastatic castration-resistant prostate cancer (mCRPC), structural variants (SVs) are central to this process but rarely incorporated into clonal reconstruction from bulk DNA sequencing. We describe SVCFit, a computational framework that estimates the cellular fraction of diverse SV classes from whole-genome sequencing and uses these estimates to reconstruct clonal evolutionary relationships. SVCFit explicitly models SV-type-specific breakpoint and breakend patterns and accounts for local copy-number context, enabling accurate SV cellular-fraction estimation across heterogeneous tumor genomes. On simulated datasets and in silico mixtures of metastatic prostate cancer samples, SVCFit achieves lower estimation error than SVclone, the current state-of-the-art for bulk-sequencing SV cellular-fraction estimation. Applied to longitudinal whole-genome sequencing from patients with mCRPC treated with bipolar androgen therapy, SVCFit reveals marked treatment-associated clonal reconfiguration, including contraction of highly rearranged subclones and expansion of resistant populations defined by distinct structural alterations. By enabling reconstruction of SV-defined clonal architecture from routine whole-genome sequencing, SVCFit expands the toolkit for studying tumor evolution under therapy in precision oncology. The COMBAT trial that provided these samples is registered on ClinicalTrials.gov (NCT03554317; first posted 13 June 2018).

  bioinformatics2026-08-04v4

• DIVAS: an R package for identifying shared and individual variations of multiomics data

  Sun, Y.; Marron, J. S.; Le Cao, K.-A.; Mao, J.

  Abstract

  Motivation: Multiomics data integration aims to identify biological patterns shared across molecular modalities. Most existing methods detect either jointly shared variation, across all modalities, or individual variation, unique to a single modality, but overlook partially shared variation, shared by only a subset of modalities. This is a critical limitation, because many biological mechanisms manifest in some but not all molecular modalities. Results: We present an open-source R package implementing DIVAS, a framework for systematically identifying jointly shared, partially shared and individual variations across multiple data types. DIVAS combines angle-based subspace analysis with inference through rotational bootstrap, hierarchically searching all combinations of modalities to decompose multiomics data into interpretable components with scores and loadings. In simulations with a known sharing structure, DIVAS recovered every component across a wide range of noise levels, whereas AJIVE and MOFA+ did not. Applied to multi-modal COVID-19 data, it reveals partially shared immune and metabolic dysregulation patterns underpinning disease severity that conventional approaches would miss. Availability and implementation: DIVAS is available as an R package on GitHub (https://github.com/ByronSyun/DIVAS). A step-by-step vignette is available on GitHub (https://byronsyun.github.io/DIVAS_COVID19_CaseStudy/).

  bioinformatics2026-08-04v2

• CPPLocPred: Subcellular Localization of Cell-Penetrating Peptides

  Bajiya, N.; Mehta, N. K.; Raghava, G. P. S.

  Abstract

  Cell-penetrating peptides (CPPs) are widely used to deliver therapeutic cargoes into cells. Although numerous computational methods have been developed for identifying CPPs and several predictors are available for protein subcellular localization, no method has been developed to predict the subcellular localization of CPPs. Here, we present CPPLocPred, a hierarchical machine-learning (ML) framework that predicts CPPs and their subcellular localization. In the first stage, we developed ML models to identify CPPs, achieving an AUC of 0.953 with an MCC of 0.7842 on an independent set, exhibiting performance equivalent to or better than existing state-of-the-art methods. In the second stage, we developed a method for predicting the subcellular localization of CPPs. Subcellular localization methods were trained (80% data using five-fold cross-validation) and validated (20% data) on experimentally validated CPPs for 663 Cytoplasm, 287 Nucleus, 57 Mitochondria, 186 Endo_lysosome, and 328 Others. Our primary analysis revealed that Mitochondrial and Nuclear associated CPPs are abundant in positively charged arginine- and lysine-rich patterns, whereas Endo_lysosomal CPPs preferentially comprise glycine-, proline-, and cysteine-rich motifs. We used a wide range of traditional peptide features, along with the embedding of protein language models, to develop ML models. Among all evaluated models, the CatBoost-based subcellular localization models with Distance Distribution of Residues (DDR) achieved AUCs of 0.814, 0.775, 0.970, 0.782, and 0.798 for Cytoplasm, Nucleus, Mitochondria, Endo_lysosome, and Others, respectively, on validation dataset. We developed CPPLocPred, which offers a practical platform for functional annotation and rational design of localization-specific CPPs for therapeutic applications (https://webs.iiitd.edu.in/raghava/cpplocpred/). HighlightsO_LIPrediction and subcellular localization of cell-penetrating peptides. C_LIO_LILocalization of CPPs depends on their amino acid and dipeptide composition. C_LIO_LIBest feature for subcellular localization was Distance Distribution of Residues. C_LIO_LICatBoost model achieved the highest performance for subcellular localization. C_LIO_LIA web server and standalone software to facilitate its use by the scientific community. C_LI

  bioinformatics2026-08-04v2

• Variational inference in coupled models of amino acid substitution

  Large, A. L.; Holmes, I. H.

  Abstract

  We investigate the use of Expectation--Maximization (EM) and variational Bayes for inferring rates and interactions under models of molecular coevolution. We first review EM theory for continuous-time Markov chains (CTMCs) and develop it for coevolutionary models, exploiting exchangeability and reversibility symmetries to constrain the parameter dimension. We fit several paired amino-acid coevolutionary models to structural alignments and compare the results to previous work. Our richest model trained on pooled coevolutionary data has explanatory power comparable to CherryML's Q2 matrix (also trained on pooled data), with one quarter the parameters. However, we observe that aggregation of training data can lead to a form of Simpson's Paradox: a mixture model, whose components are parameter-efficient continuous-time Bayes networks (CTBNs), resolves signals that wash out when a single model tries to capture everything. These signals include both correlated and anticorrelated hydropathy and volume-packing in coevolving amino-acid pairs, as well as the anticorrelated acid/base compensation that was detected by CherryML's Q2. We next present a closed-form evidence lower bound (ELBO) for CTBNs using EM statistics. Compared to the state of the art in variational modeling of CTBNs, the Euler-Lagrange equations derived by Cohn et al (JMLR, 2010), our closed-form ELBO is competitive in accuracy, considerably more efficient, simpler, and more stable. We conclude by describing a covariant indel model: a Dirichlet process selecting coevolving sites within TKF92, yielding an Infinite Pair HMM over alignments and structures. This model slightly outperforms TKF92 on a structural-alignment benchmark. Code and data are at https://tkfdp.net/.

  bioinformatics2026-08-04v2

• Interpreting Protein Language Models: high attention sites predict functional regions

  Pribus, S. J.; Altman, R. B.; Nayar, G.

  Abstract

  Computational proteomics has revolutionized biomedical research, guiding targeted experimental exploration to accelerate protein-based mechanistic discovery. Protein Language Models (PLMs) enable scalable, resource-efficient study; through large-scale training on only primary protein sequences, PLMs generate vector representations of protein structure that have been shown to capture biochemical, evolutionary, and structural properties. A core component of PLMs is the attention mechanism, which specifically captures long-range interactions across a protein sequence in attention matrices. Using the Evolutionary Scale Modelling 2 (ESM-2) PLM, we previously developed a novel method to identify "High Attention" (HA) sites. HA sites are specific residues that ESM-2 assigns the most attention to early during encoding. Here, we further characterize these HA sites across structural and functional metrics. Using unsupervised clustering, we find HA sites can be categorized as "structural core", "structural pathogenic", "core pathogenic", or "low-confidence". We further use AlphaMissense pathogenicity predictions and the pan-cancer analysis of whole genomes (PCAWG)-labeled pathogenic variant positions to show that HA sites predict protein regions with high pathogenic risk. Finally, we explore the utility of HA sites for suggesting candidate binding sites, identifying multiple cancer protein examples where HA sites identified regions with previously undiscovered high interaction likelihood and thus potential therapeutic utility. Our work demonstrates the biological interpretability of PLM representations and offers a valuable method to prioritize functionally relevant protein residues for targeted biomedical research.

  bioinformatics2026-08-04v1

• MAXWELL: Calibrating the probabilistic outputs of protein language models to the mutation-induced stability change landscape

  Li, M.; Cheng, X.; Jiang, F.; Hong, L.; Yu, Y.

  Abstract

  Designing mutations that enhance protein stability is a central goal in protein engineering. However, experimentally screening large numbers of candidate mutations is costly and time-consuming, creating a strong need for computational methods that can identify potentially stabilizing mutations. Among these approaches, protein language models are particularly promising because they learn context-dependent amino acid preferences from large-scale sequence and structure datasets. Nevertheless, most existing stability prediction methods use these models primarily as feature extractors and do not fully exploit the amino acid probability distributions they encode. Here, we introduce MAXWELL (Matrix-wise Landscape Learning), a novel post-training method that calibrates the probabilistic outputs learned by protein language models during pretraining to generate mutational landscapes that quantify the effects of individual amino acid substitutions on protein stability. When applied to ProteinMPNN, MAXWELL yields a state-of-the-art predictor of the effects of protein mutations on stability, outperforming ThermoMPNN and other representative methods on a curated benchmark of experimentally measured stability changes. We next applied MAXWELL to the design of ten single-point mutations in the DhaA dehalogenase, seven of which (70%) increased thermal stability. Among them, G171W showed the largest improvement, with a measured {Delta}T of 4.91 . These experimental results establish MAXWELL as a novel post-training strategy for protein language models and a practical framework for designing stabilizing mutations.

  bioinformatics2026-08-04v1

• The Fontan EV Score: A Circulating Extracellular Vesicle-Based Risk Stratification Tool for Fontan-Associated Liver Disease

  Takaesu, F.; Li, X.; Kievert, J.; Zhou, A.; Kemper, S.; Yuhara, S.; Hussain, S.; Watanabe, T.; Matsuda, J.; Taha, F.; Morrison, A.; Nelson, K.; Zucco, J.; Naguib, A.; McKee, C.; Hill, J.; Carrillo, S. A.; Breuer, C. K.; Kelly, J. M.; Brigstock, D.; Davis, M.

  Abstract

  Background: Fontan-associated liver disease (FALD) is a universal complication of the Fontan palliation characterized by chronic congestion and progressive hepatic fibrosis. Current diagnostics rely on invasive biopsies or non-specific biomarkers that fail to capture early fibrogenesis, creating a need for non-invasive biomarkers to stratify disease severity. Methods: We utilized an ovine Fontan model (n = 19) to investigate circulating serum extracellular vesicles (sEVs) as reporters of hepatic pathology. Longitudinal samples paired with liver elastography were collected, and sEVs were subjected to multi-omic profiling including small RNA sequencing and proteomics. Regularized regression was used to identify transcriptomic predictors, which were integrated with time post-surgery into an ordinal logistic regression framework to construct the Fontan EV Score (FES). Model performance was evaluated on a held-out test cohort and benchmarked against established fibrosis indices. To validate the biological relevance of the FES , TGF-{beta}-treated human liver organoids were generated and scored miRNA expression was assessed. Results: The sEV proteome exhibited robust separation by surgical physiology, while the small RNA cargo was primarily stratified by fibrotic status. Bioinformatics confirmed a high hepatic origin for these transcripts and identified enrichment of inflammatory pathways including Toll-like receptor and Interleukin-17 cascades in fibrotic subjects. The FES, incorporating time post-surgery and eleven small RNA biomarkers, demonstrated high predictive accuracy in the independent testing cohort with an AUC of 0.876 for moderate and 0.963 for severe fibrosis, substantially outperforming APRI (AUC = 0.618) and FIB-4 (AUC = 0.731). In human liver organoids, several scoring miRNAs, including miR-125a-5p and miR-193b-5p, were directionally responsive to profibrotic stimulation. Conclusions: Circulating sEVs carry a liver-associated cargo that can be leveraged for the non-invasive prediction of FALD severity. The FES provides a biologically validated scoring system that substantially outperforms existing serological indices and offers a new avenue for early detection and risk stratification of FALD.

  bioinformatics2026-08-04v1

• Pretrained deep-learning ITS classifiers read the flanking regions, not the ITS2 barcode, and so fail on the amplicon that environmental fungal surveys sequence

  O'Brien, A.; Parada, P.

  Abstract

  Deep-learning classifiers for the fungal internal transcribed spacer (ITS) report accuracies above 90% and are increasingly proposed for environmental metabarcoding. We benchmark two pretrained models, a convolutional network and a transformer sharing a training corpus of 5.23M sequences, against two established k-mer methods on 5,222 identical queries, one per genus, evaluated on both full-length ITS and the ITS2 subregion that dominates environmental sequencing. The design favours the classifiers: queries are drawn from the same public dataset they were trained on and stratified by whether a query's genus lies in their own label space, recovered from the distributed models, while the reference the k-mer methods consult mirrors that label space and excludes the queries themselves. Even so, on full-length ITS both classifiers are outperformed by both classical methods at every rank and in both strata: SINTAX recovers the correct family for 92.0% of seen-genus and 70.3% of novel-genus queries and best-hit alignment against a 56,327-sequence reference for 92.3% and 67.2%, against 77.9% and 57.7% for the transformer and 76.5% and 54.3% for the convolutional network. A hierarchical logistic regression on k-mer counts, fitted in ten minutes to 1.07% of the MycoAI training corpus, also exceeds both and places novel genera better than either search method, and refitted on ITS2 it recovers 89.2% of seen-genus families on that amplicon against 88.6% for best-hit alignment, so neither learned classification nor the amplicon is what fails. Restricting the identical records to ITS2 costs the k-mer methods 3.5 and 3.7 percentage points of seen-genus family accuracy but costs the classifiers 49.6 and 58.0, reducing them to 28.3% and 18.5%. An ablation identifies the cause. Grafting each query's unaltered ITS2 between the flanking regions of a donor record from a different phylum returns the donor's family for 34.4% of queries against the query's own for 4.2% in the convolutional model, and 63.7% against 0.8% in the transformer, from a baseline of 0.1% where no donor sequence is present. The models therefore read taxonomy principally from the flanking regions rather than from the ITS2 barcode, which explains the collapse and predicts the same failure for any subregion amplicon. Compounding this, on ITS2 the classifiers' output probability all but ceases to separate novel from known genera (AUROC 0.541 and 0.503, the latter at chance, against 0.866 for alignment identity and 0.785 for the SINTAX bootstrap), so the failure is not detectable from the models' own output. We recommend that reported accuracies for such models specify the amplicon region of the evaluation, state the length distribution of the training corpus, and include a same-query classical baseline.

  bioinformatics2026-08-04v1

• A calibrated novelty flag for fungal ITS metabarcoding: choosing the error rate at which sequences are declared new

  O'Brien, A.; Parada, P.

  Abstract

  Environmental fungal surveys routinely recover internal transcribed spacer (ITS) sequences that cannot be assigned at fine taxonomic ranks, the so-called fungal "dark matter," and in practice such sequences are set aside by thresholding a similarity or confidence score at a conventional value. Those conventions do abstain, but the error rate a given threshold implies is neither stated nor selectable, and a threshold defined for one kind of score does not transfer to another. We present a conformal novelty flag that supplies what is missing: each query receives a p-value with a distribution-free guarantee that the rate of falsely declaring a known sequence novel is bounded by a user-chosen alpha. On a leave-one-genus-out benchmark built from UNITE the flag holds its nominal rate across two orders of magnitude in alpha, so the practitioner selects an operating point on a stated frontier rather than inheriting one: at alpha=0.05 it fires on 4.3% of known-genus queries and recovers 19.2% of genuinely novel genera, and at alpha=0.20 on 19.7% and 50.9%. It calls 4,996 of 5,000 non-fungal SSU outgroup sequences novel, anchoring the far end of a monotonic known-to-outgroup gradient. The guarantee is score-agnostic in practice and not only in principle: applied to alignment identity, a k-mer bootstrap consensus and two neural classifiers' output probabilities, across two amplicon regions and a sevenfold change in reference size, all eleven calibrations fell within 1.1 percentage points of nominal. Detection over those same eleven settings ranged from 5.2% to 53.3%, a tenfold spread under an essentially constant error rate, which makes explicit that the guarantee is on the error rate and not on power: a well-calibrated flag built on an uninformative score is valid and useless, and two of our settings are exactly that. Applied to a soil fungal dataset the flag identifies 20.7% of amplicon sequence variants as novel at a controlled 5% error rate, falling to 16.3% under an abundance filter. Half of those recur near-identically among the unnamed environmental variants of GlobalFungi while fewer than one in ten matches a named species hypothesis, a sixfold skew towards the uncatalogued against 1.9-fold for the sequences the flag passes: present in soils worldwide and named in almost none of them. The flag turns an arbitrary cutoff into a decision with a stated error rate, and in doing so converts dark matter from a residue into a set of prioritizable targets. Source code and the benchmark harness are available under the MIT license at https://github.com/ayobi/its-novelty.

  bioinformatics2026-08-04v1

• Scientific computing in the age of agentic AI: an exploratory field report

  Li, J.; Rubinsteyn, A.; Feldman, S.; O'Donnell, T.; Ferguson, J. M.; Patro, R.; Driver, I.; Ewels, P. A.; Krueger, F.; Angerer, P.; Gold, I.; Manning, J.; Heumos, L.; Ahangari, M.; Goyal, V.; Masoudi, H.; Pedersen, B.; Bai, A.; Li, H.; Shringarpure, S.; Ho, A.

  Abstract

  Scientific computing has become a central component of modern scientific discovery. Yet many computational tools are developed by small, specialized teams under incentives that encourage the release of rapidly prototyped tooling without commensurate attention to engineering concerns, including performance and maintainability. These gaps are particularly visible in the life sciences, where the advent of high-throughput sequencing and molecular profiling has made the production and processing of datasets routine at scales that strain reliability and cost. Recently, LLM-based agents have become increasingly capable, with publicly available systems possessing both significant domain knowledge in many scientific fields and the ability to autonomously operate over complex and specialized codebases in pursuit of well-defined goals. Together, these developments create a practical opportunity for scientific computing. Many of the persistent weaknesses of the scientific computing ecosystem stem from technical debt and a shortage of sustained engineering labor and expertise. Here, we examine coding agents as a potential way to address these weaknesses: we present an exploratory field report of eight early case studies in the application of LLM agents to scientific computing across a range of project scopes, from lightweight maintenance tasks to full performance-oriented rewrites of scientific libraries, with a focus on the life sciences. Each of these case studies is accompanied by reflections from the individual or group responsible for the work, including lessons from the process. Overall, we find that the use of coding agents in scientific computing holds great promise for accelerating scientific research and increasing the reliability of critical systems, but that outstanding concerns remain, including responsibility and ownership for such projects, and we suggest collaboration and stewardship with existing maintainers when feasible.

  bioinformatics2026-08-04v1

• Evolutionary Design of Membrane-Lytic Antimicrobial Peptides with Mixture of Experts

  Dong, R.; Song, C.

  Abstract

  Antimicrobial peptides (AMPs) hold great promise in combating drug-resistant pathogens, yet current design models do not explicitly account for their primary mode of action, membrane disruption. To address this gap, we propose AMPainterV2, a model that designs membrane-lytic AMPs using generative adversarial imitation learning. AMPainterV2 integrates a Mixture-of-Experts (MoE) policy network to support insertion, deletion, and mutation operations, thereby expanding the evolutionary design space. In vitro experiments demonstrate that all ten top-ranked peptides evolved from random sequences are active membrane-lytic AMPs, with the best candidate achieving a mean minimal inhibitory concentration (MIC) of 1.5 M and superior membrane-lytic activity compared to polymyxin B. Particularly, a deletion-only version of AMPainterV2 enables membrane-lytic AMP miniaturization, yielding six miniaturized peptides with comparable or improved activity and an average 36% length reduction. Collectively, AMPainterV2 advances mechanism-driven AMP design and offers a valuable tool for cost-effective AMP development.

  bioinformatics2026-08-04v1

• ComBatFamQC: Streamlining Interactive Batch-Effect Diagnostics and Harmonization for Neuroimaging Data in R

  Ren, Z.; Horwath, E.; Wen, S.; Melhem, R.; Anderson, J. K.; Johnson, W. E.; Shinohara, R. T.; Chen, A. A.; Shou, H.

  Abstract

  As multisite and multi-study data aggregation becomes increasingly common for improving statistical power and sample diversity, robust harmonization methods are needed to address biases introduced by batch variation, particularly in neuroimaging research. Although a variety of harmonization approaches are available, the lack of systematic guidance for diagnosing batch effects and selecting appropriate methods remains a major challenge. To address this gap, we introduce ComBatFamQC, a comprehensive R package designed to streamline batch-effect diagnosis, harmonization, and post-harmonization analysis. ComBatFamQC integrates a user-friendly Shiny app for interactive batch-effect diagnostics, state-of-the-art harmonization methods from the ComBat family, including ComBat, longitudinal ComBat, ComBat-GAM, and CovBat, and tools for downstream analysis after harmonization. The package provides qualitative visualizations, statistical tests for batch-effect assessment, and a consistent interface that supports both in-sample and out-of-sample harmonization through the Shiny app, the R console, or the command line. In addition, it includes functions for post-harmonization analyses to facilitate downstream modeling. Its modular design also supports the systematic incorporation of future harmonization methods and expanded downstream analysis capabilities.

  bioinformatics2026-08-04v1

• Spatial Transcriptomics of Glioblastoma Defines Spatially Anchored Transcriptional Activity Informing Therapeutic Vulnerability and Resistance

  Ravikumar, V.; Maddox, A.; Kulkarni, R.; Hara, T.; Rao, A.; Al-Holou, W.

  Abstract

  Intratumoral heterogeneity is thought to hinder targeted therapy in glioblastoma (GBM), but the extent of variation in known targets and tumor cell-intrinsic and extrinsic regulatory mechanisms within tissue remains unclear. In this work, we use Visium spatial transcriptomics to profile 44 tumor slides and 128,176 niche-annotated ST spots, including 14 new slides from 7 GBM specimens, along with spatial annotation from the Ivy Glioblastoma Atlas Project. First, we characterize the differential expression of AVB3, EGFR, VEGF, and PDGFRA pathway activities across space, elucidating the limited success of individual targeted therapies. Second, we identify transcription factor modules driving intratumoral heterogeneity and characterize a novel transcription factor module at the interface between core tumor and the invasive edge. Third, we identify cell type-specific receptor-ligand signaling enriched in specific spatial niches and relate these to specific molecular program alterations. Finally, we identify and characterize transcriptional pathways and their activity across spatial niches through transcription network analysis and demonstrate how these pathways can inform a combined therapeutic approach. Together, through novel insights into the spatial patterning of transcription factor regulation, cellular interactions, and biological pathway activity, our work informs rational combination therapies targeting spatial niche specific vulnerabilities.

  bioinformatics2026-08-03v3

• A Systematic Comparison of Single-Cell Perturbation Response Prediction Models

  Li, L.; You, Y.; Fu, Y.; Liao, W.; Fan, X.; Lu, S.; Cao, Y.; Li, B.; Ren, W.; Kong, J.; Zheng, S.; Chen, J.; Liu, X.; Tian, L.

  Abstract

  Predicting single-cell transcriptional responses to perturbations is central to dissecting gene regulation and accelerating therapeutic design, yet the field lacks a rigorous, task-spanning assessment of model behavior. We present a large-scale benchmark of 13 representative methods and baselines across 25 datasets spanning diverse perturbation modalities and species, including two primary immune-cell drug-response resources. We evaluated three core tasks-generalization to unseen single-gene perturbations, prediction of combinatorial interactions, and transfer across cell types-using 24 metrics covering expression-level accuracy, relative changes, differential expression recovery, and distributional similarity.Across tasks, performance depended strongly on perturbation effect size and evaluation perspective: expression-level agreement was highest for small-effect perturbations resembling controls, whereas delta- and DE-based metrics improved with larger effects, providing clearer signals. Models shared a conservative bias, with fine-tuned foundation models compressing variance and underestimating synergistic effects in combinations. PerturbNet showed superior recovery of DE signatures in Tasks 1 and 2, while no method consistently generalized across cell types in Task 3, where biological consistency dominated outcomes.This benchmark establishes current methodological limits, clarifies that different metrics probe distinct biological signals rather than redundant summaries of the same prediction problem, and provides a foundation for developing virtual-cell models that more faithfully capture heterogeneous perturbation responses.

  bioinformatics2026-08-03v3

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

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

  Abstract

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

  bioinformatics2026-08-03v3

• BioGraphX-RNA: A Universal Physicochemical Graph Encoding for Interpretable RNA Subcellular Localization Prediction

  Saeed, A.; Abbas, W.

  Abstract

  RNA subcellular localization is a critical determinant of cellular function. However, current computational approaches often operate as ""black boxes", overlooking the complex interplay among sequence, structure, and physicochemical interactions that govern RNA localization. Building upon the BioGraphX framework originally developed for proteins, we introduce BioGraphX-RNA, a universal physicochemical graph-encoding framework that provides a structure-informed encoding by translating primary nucleotide sequences into multiscale interaction graphs using explicit biophysical rules. When combined with frozen RiNALMo embeddings via an interpretable gated fusion layer, BioGraphX-RNA achieves competitive performance with DeepLocRNA and uniquely quantifies the relative contribution of sequence versus structure for each RNA. On human datasets, the gated fusion model attains macro AUROC values of 0.7575 +/-0.0054 (mRNA), 0.9228 +/- 0.0137(miRNA), and 0.5600 +/- 0.0191 (lncRNA). For miRNA, the graph only model alone reaches 0.9396 +/-0.0045, outperforming both the RiNALMo language model and a RNAfold partition function graph (0.9139 +/- 0.0138), validating the structure;informed proxy hypothesis. In a blind cross-species prediction task on mouse data, the model shows limited zeroo shot transfer, indicating that biophysical graph features do not improve cross species generalization. Gating analysis reveals RNA type-specific modality reliance, with miRNA exhibiting a near equilibrium balance between sequence and structure. SHAP based interpretation suggests potential correlates such as patterned GC content for nuclear retention and structural accessibility for exosome targeting. These advances are achieved with only 2.05 million trainable parameters, aligning with Green AI principles. BioGraphX-RNA demonstrates that explicitly integrating biophysical constraints into graph-based encodings enables accurate and interpretable predictions for structured RNAs, advancing structure-aware RNA biology and laying a foundation for precision medicine.

  bioinformatics2026-08-03v3

• vartracker: an end-to-end tool for pathogen longitudinal variant analysis and visualisation

  Foster, C. S. P.; Rawlinson, W. D.

  Abstract

  Longitudinal sequencing can reveal fine-grained pathogen evolution during acute and chronic infections and inform public health responses. However, integrating ordered pathogen genomic data into a coherent evolutionary and clinical framework can be tedious and error-prone. We present vartracker, an open-source tool for longitudinal pathogen variant analysis and visualisation. Given an ordered sample manifest, vartracker supports three entry points: raw sequence reads, reference-aligned BAM files, or user-supplied VCF and coverage inputs. Raw-read and BAM inputs are processed through an integrated Snakemake workflow, whereas VCF mode starts from precomputed files. Variants are normalised and annotated relative to a reference genome, tracked across timepoints, and classified as original or newly emerging and as transient or persistent. Inferred amino acid changes are reported, and for SARS-CoV-2 analyses, relevant published literature for key mutations can be automatically linked through a functional database. vartracker outputs a schema-documented results table, provenance metadata for reproducibility, publication-quality static figures, and an interactive heatmap for data exploration. Although packaged with SARS-CoV-2 reference assets and initially developed for SARS-CoV-2 datasets, vartracker is pathogen-agnostic when appropriate reference data are supplied. We demonstrate its utility using SARS-CoV-2 and respiratory syncytial virus A (RSV-A) datasets. vartracker is freely available through GitHub, PyPI and Bioconda.

  bioinformatics2026-08-03v2

• crispAIPE: Probabilistic Modelling of Prime Editing Variant Correction Efficiency

  Ozden, F.; Lu, P.; Minary, P.

  Abstract

  Prime editing installs precise substitutions, insertions, and deletions without double-strand breaks or donor templates, but the efficiency of an individual pegRNA design is hard to predict in advance, and existing tools give only point estimates, leaving designers unable to judge which predictions to trust. We present crispAIPE, a transformer-based probabilistic framework that quantifies per pegRNA-target pair uncertainty by modelling the three competing prime-editing outcomes on the 2-simplex with a Dirichlet likelihood and pairing the posterior with split-conformal highest-density regions calibrated on a held-out fold, giving finite-sample coverage guarantees on the simplex without requiring the Dirichlet model itself to be well-calibrated. Trained on 92,423 PRIDICT Library-1 pegRNAs under mutation-level target-disjoint splitting, crispAIPE attains Spearman rho = 0.835, 0.843, and 0.693 on the edited, unedited, and indel fractions (Pearson r = 0.845, 0.855, 0.659), and its conformal regions match nominal coverage where region-construction baselines do not. pegRNA architecture and edit context, in particular GC content of the mutated reverse transcription template (RTT) and edit size for deletions, are associated with prediction uncertainty. Reusing the Library-1 calibration quantile unchanged on PRIDICT Library-2, the conformal region area generalises as an actionable filter for cross-cell-type design, identifying the pegRNAs whose Library-1 predictions transfer best while small-sample head fine-tuning further improves accuracy. Tool and trained models: https://github.com/furkanozdenn/pe-uncert.

  bioinformatics2026-08-03v2

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

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

  Abstract

  eneral-purpose language models are being increasingly utilized in protein-design workflows, yet their ability to evaluate variant effects remains unclear. To answer this question, we introduce PG-LLM, a benchmark built on ProteinGym to evaluate general-purpose language models on 217 protein-variant prioritization tasks. Each task follows the same format: a language model receives a wild-type protein sequence, an assay description, and is tasked with ranking 50 mutant sequences by fitness without access to multiple-sequence alignments or protein structures. We evaluate thirteen language models and rescore 95 published protein predictors on the same candidate sets with the same evaluation metric. Claude Opus 5 leads the primary leaderboard with a Spearman correlation of {rho} = 0.406, narrowly ahead of GPT-5.6 Sol at 0.402. However, GPT-5.6 Sol scores higher than Opus 5 when the two models are compared only on assays scored by both. Opus 5 outperforms 49 of 95 published protein predictors, including 41 of 46 sequence-only methods, and approaches ESM2-650M at {rho} = 0.411, but remains below the leading predictor VenusREM at {rho} = 0.523. Variant-ranking performance improves with test-time compute across GPT, Claude, and Gemini models, but the gains taper before closing the gap to specialist protein predictors. Unlike sequence-only predictors, which perform better on proteins with deeper evolutionary alignments, LLM accuracy changes little across alignment-depth. To address contamination risk, we additionally source 59 DMS assays from 19 studies whose scores first became public after January 2026. On this set, we evaluate three LLMs and seven biomolecular baselines, observing performance trends similar to those on the main PG-LLM benchmark. PG-LLM shows that tool-free language models capture substantial protein-variant signal and already outperform many established sequence-based predictors. These results establish the emerging capability of language models as biomolecular reasoners while defining the remaining headroom for their reliable use in variant-prioritization workflows.

  bioinformatics2026-08-03v2

• Explainable Generative AI Uncovers a Molecular Continuum in Medulloblastoma with Implications for Rare Cancer Subtyping and Treatment Equity

  Prol-Castelo, G.; Tejada-Lapuerta, A.; Urda-Garcia, B.; Nunez-Carpintero, I.; Garcia-Verellen, E.; Montagud, A.; Valencia, A.; Cirillo, D.

  Abstract

  Medulloblastoma is a childhood brain tumor traditionally classified into four molecular subgroups. Recent evidence suggests that Groups 3 and 4 represent a biological continuum rather than distinct entities, a paradigm shift with significant implications for understanding disease biology and treatment strategies. Nevertheless, assessing this hypothesis is challenging mainly due to data scarcity. In this study, we analyze the largest available transcriptomics dataset to provide compelling evidence for the existence of an intermediate subgroup between Groups 3 and 4, characterized by distinct molecular features. To overcome limitations posed by data scarcity, we employ synthetic data generation using a Variational Autoencoder and apply explainability techniques to identify key relationships between gene expression and disease subgroups. Furthermore, by incorporating Machine Learning Fairness approaches, we demonstrate that overlooking this intermediate subgroup can result in treatment disparities. Our findings are further supported by both existing and newly proposed studies using diverse datasets and methodologies, including graph-based analyses and multi-scale simulations, underscoring the robustness and reproducibility of our results. This study demonstrates the potential of synthetic data generation to refine rare disease subtyping and advance our understanding of the underlying biological mechanisms.

  bioinformatics2026-08-03v2

• Do Geometric Outliers Identify Important Genes in Single-Cell Foundation Models?

  Whalley, J. P.

  Abstract

  Single-cell foundation models (scFMs) produce gene embeddings that are increasingly interpreted biologically, but it is unclear whether different models organise gene space consistently, or whether geometric extremes identify important genes. The same four-metric screen was applied to Geneformer, scGPT and scFoundation, which differ in architecture, training objective and expression encoding. The models agreed weakly on individual outlier genes but showed structured class-level convergence: ribosomal enrichment occurred in all three, although weaker and caller-dependent in scFoundation, while strong mitochondrial enrichment was confined to Geneformer and scGPT. Gene-level agreement was itself uneven, with Geneformer and scGPT overlapping above chance and scGPT and scFoundation not, and caller stability differed between models. Most outliers were not extreme in ESM-2 protein-sequence space, indicating principally model-specific rather than protein-sequence geometry. These patterns did not translate into the tested notions of gene importance: deleting the highest-anomaly Geneformer genes did not impair cell-type annotation beyond matched controls, and covariate-adjusted outlier status was not associated with ClinVar membership in any model. Gene-embedding geometry therefore characterises model-specific structure, but does not provide standalone evidence of downstream leverage or disease relevance.

  bioinformatics2026-08-03v2

• What limits local ancestry inference at low divergence: a feasibility threshold, a metric that conceals failure, and a deficit of input more than architecture

  Tian, Q.

  Abstract

  Local ancestry inference assigns each position along an admixed chromosome to a source population, underpinning admixture mapping, ancestry-specific association testing and admixture dating. Validation is almost exclusively on continentally divergent sources (Hudson's FST {approx} 0.1) and coalescent simulations; we examine both restrictions. Across FST from 0.0022 to 0.243 we compare five methods--two likelihood baselines, RFMix, FLARE and a dilated convolutional network--on identical sites with exact ground truth, and on 11 real 1000 Genomes pairs. Three findings follow. First, a feasibility floor: at FST = 0.0022 no method exceeds 0.575, and CHB/CHS at FST = 0.00042 yields at best 0.551. Pairs motivating fine-scale analysis, such as northern versus southern Han, fall below it. Second, per-site accuracy conceals a failure of tract structure: the most accurate method per site produces 78.8x too many tracts, implying an admixture time 61.2x too old, which Viterbi decoding removes at no cost to accuracy (+0.0002). Third, the simulated lead does not survive real data, and the deficit is one of input more than of the architectures we varied: attention, state-space layers, capacity, objective and self-supervised pretraining each move accuracy by at most 0.006, while supplying the haplotype information the released tools receive recovers +0.031 on 8 of 8 pairs below FST = 0.04 and nothing above it--necessary but not sufficient, since the network still trails on 10 of 11 pairs. Two quantities usually held fixed matter more than architecture: the statistic summarising reference matching, and reference panel size, which no method is near saturating.

  bioinformatics2026-08-03v1

• STAR Suite: Transcriptomics processing in a single binary through AI-assisted development

  Hung, L.-H.; Baker, D.; Flynn, B.; Huangfu, D.; Luo, R.; Robson, P.; Zhou, T.; Yeung, K. Y.

  Abstract

  Single-cell transcriptomic assays - pooled CRISPR/Perturb-seq screens, clonal lineage tracing, and 10x Flex fixed RNA profiling - are central to modern biology, yet their processing has depended on Cell Ranger, a proprietary tool restricted to 10x products, with no open end-to-end pipeline for Perturb-seq or Flex. We present STAR Suite, which extends the STAR aligner into a single executable that processes bulk RNA-seq, scRNA-seq, Perturb-seq, 10x Flex, and SLAM-seq in one command, with no added dependencies. For single-cell assays it reproduces Cell Ranger 9.0.1 (gene-level Pearson 0.99-1.0) while running 3.6- to 5.7-fold faster, via novel exact-matching and alignment-validated hashing algorithms. Served to both AI agents and human operators through one validated interface, STAR Suite is the production standard for the NIH MorPhiC consortium; its 132,226 lines of C/C++ were built under a human-directed, AI-implemented workflow. Source code, workflow recipes, and per-run provenance are released under the MIT license

  bioinformatics2026-08-02v6

• MGMG: Cell Morphology-Guided Molecule Generation for Drug Discovery

  Tang, Q.; Ding, D.; Yuan, X.; Seabra, G.; Ramdhan, P. A.; Liu, C.-Y.; Thai, M. T.; Li, C.; Luesch, H.; Li, Y.

  Abstract

  Designing novel molecules with desired bioactivity remains a fundamental challenge in drug discovery. Most molecular design methods follow target-based drug discovery paradigms that rely on well-defined drug targets, thereby limiting their applicability to diseases lacking known targets or reference compounds. Here we introduce Morphology-Guided Molecule Generation (MGMG), a phenotypic drug discovery-oriented approach that integrates cellular morphological profiles from compound treatments with molecular textual descriptions without requiring molecular target information. Cell morphology offers the guidance on desired bioactivity-relevant phenotypic effects, while textual descriptions provide direct and interpretable cues about molecular structure. Leveraging complementary structural and bioactivity context, MGMG significantly enhances molecule generation performance, especially in scenarios where textual descriptions are under-informative or morphological signals are weak. MGMG can also be applied to genetic perturbations, enabling activator design from gene overexpression morphology without requiring knowledge of reference compound structure. In addition, in silico docking demonstrates that MGMG-generated molecules, despite lacking target information, exhibit binding affinities comparable to reference compounds, preserving key interactions while introducing structural diversity. Overall, MGMG jointly utilizes morphological and textual description inputs to guide molecule generation, enabling diverse, bioactivity-aware compound design in a target-agnostic fashion.

  bioinformatics2026-08-01v2

• Pangenome Graph Node-Phenotype Association shows GWAS-like quality results with only few individuals

  Carrette, C.; Sabot, F.; Muller, C.

  Abstract

  Purpose: We introduce GraNPA, standing for Graph Node-Phenotype Associa- tion, a method performing a GWAS-like analysis on a pangenome variation graph (PVG) built using a small number of individual genome sequences, without the need for additional population materials or kinship information for qualitative phenotypes. This method reduces the number of individuals required for associ- ation studies and prevents reference bias from variant calling in these types of analyses. Background: A PVG represents the multiple alignment of a set of complete genomes. It contains all variations, from single nucleotide polymorphisms (SNPs) to large structural variations (SVs), which are represented as nodes in the graph. By integrating phenotype information within nodes, we can assign a Phenotype Score (PS) to each node in the PVG and identify phenotype-related regions directly within it. These regions represent statistically significant shifts in PS distribution, highlighting their implication in the phenotype. Finally, GraNPA provides their positions and scores for further analysis. Results: This method was tested using simulated data and two publicly available datasets: the Sub1A gene locus for Oryza sativa in a 13 indi- viduals PVG, and the insertion responsible for the white-headed cattle with a PVG of 24 individuals. Source code of GraNPA is available here https://forge.ird.fr/diade/graphgwas/granpa under GNU GPLv3. Conclusion: GraNPA was able to identify the expected area in two simulated datasets and the responsible loci for these two known traits using only a few 1 dozen complete genomes in these PVGs. While currently limited to qualitative phenotypes, this method opens the way to more efficient ones relying on PVGs and few individuals.

  bioinformatics2026-08-01v1

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

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

  Abstract

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

  bioinformatics2026-07-31v2

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

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

  Abstract

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

  bioinformatics2026-07-31v2

• Forecasting cell state futures from static snapshots

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

  Abstract

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

  bioinformatics2026-07-31v2

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

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

  Abstract

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

  bioinformatics2026-07-31v1

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

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

  Abstract

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

  bioinformatics2026-07-31v1

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

  zhang, X.

  Abstract

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

  bioinformatics2026-07-31v1

• mock-fastq-generator: A synthetic FASTQ generator

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

  Abstract

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

  bioinformatics2026-07-31v1

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

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

  Abstract

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

  bioinformatics2026-07-30v2

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

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

  Abstract

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

  bioinformatics2026-07-30v2

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

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

  Abstract

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

  bioinformatics2026-07-30v2

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

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

  Abstract

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

  bioinformatics2026-07-30v2

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

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

  Abstract

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

  bioinformatics2026-07-30v1

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

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

  Abstract

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

  bioinformatics2026-07-30v1

• Expanded unbiased population-genomic summary statistics in pixy

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

  Abstract

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

  bioinformatics2026-07-30v1

• Rich structure alphabets enable highest accuracy protein search

  Edgar, R. C.

  Abstract

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

  bioinformatics2026-07-30v1

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

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

  Abstract

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

  bioinformatics2026-07-30v1

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

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

  Abstract

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

  bioinformatics2026-07-30v1

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

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

  Abstract

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

  bioinformatics2026-07-29v2

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

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

  Abstract

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

  bioinformatics2026-07-29v2

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

  Chandrasekar, S.

  Abstract

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

  bioinformatics2026-07-29v2

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

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

  Abstract

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

  bioinformatics2026-07-29v2

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

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

  Abstract

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

  bioinformatics2026-07-29v1

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

  Cang, H.; Sun, S.

  Abstract

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

  bioinformatics2026-07-29v1