Latest bioRxiv papers

Category: bioinformatics — Showing 50 items

• A geometry-over-coevolution principle governs protein complex assembly in AlphaFold

  Li, S.; Mu, Z.; Yan, C.

  Abstract

  AlphaFold has revolutionized protein complex structure prediction, yet how it assembles intermolecular interfaces remains poorly understood. Contrary to the prevailing view that inter-protein coevolution drives complex prediction, we uncover a geometry-over-coevolution principle governing assembly in AlphaFold-Multimer and AlphaFold3. Through systematic perturbation of evolutionary and structural inputs and development of residue-level constraint propagation mapping to trace the emergence and propagation of geometric information within the network, we show that prediction accuracy is governed primarily by monomer-derived structural geometry and interface-specific sequence-geometry compatibility, rather than direct inter-protein coevolutionary signals. Our mapping reveals a hierarchical assembly mechanism in which monomer-level geometric representations are established first and progressively propagated to constrain cross-chain interfaces. This mechanism further explains why antigen-antibody complexes are predicted less accurately, as their intrinsic interface plasticity and non-canonical architectures limit the propagation of geometric constraints across interfaces. Together, these findings establish a mechanistic framework for understanding how artificial intelligence models assemble protein complexes and provide principles for improving next-generation structure prediction.

  bioinformatics2026-09-08v4

• ProMaya: a hierarchical universal Deep Learning framework for accurate and interpretable Protein-Protein interaction identification

  Bhati, U.; Gupta, S.; kesarwani, V.; Shankar, R.

  Abstract

  Protein-protein interactions (PPIs) are molecular lego which define the physical states of cells. Accurately identifying PPIs remains challenging due to the interplay of several factors ranging from electrostatic to molecular geometry, topology, and physics. Existing computational approaches capture only fragments of this orchestra, limiting their generalizability across protein families and interaction types. Here, we present ProMaya, a hierarchical multi-scale Graph-transformer framework that integrates 3D atomic geometry, electronic distribution, residue-level structure and disorder, surface mass-density signatures, and large protein language-model embeddings of interacting proteins. Highly comprehensively benchmarked across nine species and 47 GB experimentally validated data, ProMaya achieved consistently >95% average accuracy, outperforming state-of-the-art tools by >12%. As driven by its explainability, the first time introduced atomic and protein language information dramatically boosted it to an outstanding level for PPI discovery in any species, potent to even bypass costly experiments. ProMaya system is freely accessible at https://scbb.ihbt.res.in/ProMaya/

  bioinformatics2026-09-08v2

• SPARKLE: evidence-constrained correction of local RNA leakage in high-resolution spatial transcriptomics

  Wang, S.; Zhu, B.; Li, S.; Wei, X.

  Abstract

  High-resolution sequencing-based spatial transcriptomics, including Stereo-seq and Visium HD, aggregates dense capture units into cell-resolved expression matrices. During tissue processing and permeabilization, RNA released from source cells can spread to neighbouring capture locations, reducing cell-type specificity and biasing downstream analyses. Here we developed SPARKLE (Spatial Ambient RNA Kernel-based Leakage Estimator), a cell-level correction method that uses capture locations outside cell-segmentation masks as within-sample spatial evidence of leakage. SPARKLE fits sparse spatial kernels to out-of-mask observations to estimate a sample-level propagation scale and gene-specific leakage coefficients. It corrects only genes supported by out-of-mask goodness of fit and uses expression-dependent conservative shrinkage to protect highly expressing source cells. In ten simulated scenarios, SPARKLE achieved the highest cell-wise concordance in eight and the lowest RMSE in nine. In axolotl brain, mouse brain and human ovarian cancer, SPARKLE removed ectopic marker signal from neighbouring cells while retaining source-cell expression, improved agreement with independent single-cell and single-nucleus references, and recovered an inferred fibroblast-to-tumor COL1A2-SDC4 communication route that was obscured by ectopic COL1A2 expression. Conclusions remained stable across plausible spatial scales and background-bin sizes. Runtime scaled near-linearly with tissue-window area and was further accelerated on GPU. SPARKLE is therefore a reference-free, fast and scalable method for correcting local RNA leakage from evidence contained within each sample, improving the reliability of cell-type localization, tissue-compartment identification and cell-cell communication inference.

  bioinformatics2026-09-08v2

• EvSpark: Lossless Speculative Decoding for Hybrid DNA Foundation Models

  Ding, H.; Wu, N.; Qiu, T.

  Abstract

  DNA foundation models such as Evo2~7B adopt hybrid Hyena/attention architectures (StripedHyena2) whose single-stream autoregressive decoding is bounded by weight bandwidth at ${\sim}45$~\tokps. Speculative decoding on such hybrids faces a systems problem that prior SSM work solves only partially: after a draft is verified, rolling back model state cannot be reduced to truncating a KV cache, because the inference state mixes fixed-length FIR sliding windows, infinite-memory IIR recurrences, and append-only KV caches. We present \textbf{EvSpark}. (i)~A block-verification forward pass together with a per-position \emph{state-slicing} rollback protocol that handles all three state classes jointly with zero recomputation, reducing speculative overhead to 1.05--1.16$\times$ under an identity drafter and eliminating the second per-round forward pass of snapshot-and-replay schemes. (ii) A DSpark-style distilled neural drafter ported to a hybrid architecture ($\gam$-parallel trunk $+$ target hidden-state prefix $+$ full-matrix Markov head); controlled injection-layer comparisons at matched budget and two seeds find no layer-choice effect beyond seed noise at realistic budgets, while smoke-scale budgets systematically misrank layer types; the ${\sim}10^{11}$ residual magnitudes of late blocks require an fp32-scale distillation codec. (iii)~Under a 24-prompt $\times$ 1024-token $\times$ 2-seed protocol, EvSpark delivers \textbf{\speed{3.16}} end-to-end at its deployment configuration ($\gam{=}12$, 80M supervised positions) and \speed{2.88} on the reference model used for our deep-dive analyses ($\gam{=}7$, 150M), holding from 1k to 262k context and across 32k tokens of generation depth. In exact arithmetic the scheme preserves the target distribution; empirically, greedy decoding reproduces native outputs token-for-token (zero non-tie divergences across 24 prompts $\times$ 4 checkpoints), and the sampling path sits at the native bootstrap noise floor for 7/10 prompts with bounded bf16-level deviation elsewhere (${\le}1.7\times$ floor; worst-case unigram TVD 5.6\%). The budget-sweet-spot configuration adds \textbf{1.06 GPU-hours} of training on RTX~4090-class hardware and reaches \speed{3.03} (incremental to a one-off teacher-signal dump of ${\approx}19$ GPU-h shared by all configurations).

  bioinformatics2026-09-08v1

• FFPERescuer: deep unsupervised domain adaptation for the reconstruction of gene expression profiles derived from formalin-fixed paraffin-embedded samples

  He, l.; Song, K.; Li, Y.; Dong, Y.; Wong, C. Y. N.; Qi, L.; Zhang, X.; Lenos, K.; Back, T. d.; Elbers, C.; Xu, C.; Leung, R. M. H.; Deng, R.; Zhang, Y.; Qiao, S.; Gao, F.; Chen, Y.; Ng, S. S.-M.; Zhou, S.; Vermeulen, L.; Wang, X.

  Abstract

  Formalin-fixed paraffin-embedded (FFPE) tumor tissues often suffer from RNA degradation, posing a long-standing challenge for reliable transcriptomic profiling. Here, we propose FFPERescuer, a deep learning framework employing unsupervised domain adaptation, to rectify distorted gene expression data. FFPERescuer comprises a partial encoder that maps a small subset of genes to high-level representations and a decoder to reconstruct full gene expression profiles. On simulated data with varying noise levels, FFPERescuer faithfully recovered gene expression profiles, achieving high Pearson correlation coefficients (PCCs > 0.85) with the ground truth. In FF-FFPE-matched cohorts, FFPERescuer significantly enhanced expression profile concordance, with average PCCs increased by 23% (P < 0.05). Applying to cancer subtyping, FFPERescuer improved classification accuracy from 67% to 92%, recapitulated subtype-specific biological properties lost in the FFPE-derived data, and enhanced survival associations. Our studies provide a powerful framework for reliable transcriptomic profiling from FFPE-archived tumor samples that are widely available in the clinic.

  bioinformatics2026-09-08v1

• Microhomology-Driven Genomic Alterations in Cancer Genomes: Patterns, Prevalence, and Clinical Implications.

  Kostka, D.; Sztromwasser, P.; Kimmel, M.; Jaksik, R.

  Abstract

  Homologous recombination deficiency (HRD) can force cancer cells to rely on alternative DNA repair pathways, including microhomology-mediated end joining (MMEJ), an error-prone mechanism that can generate deletions with microhomology at repair junctions. Because such patterns may reflect DNA repair defects with clinical relevance, this study aimed to assess the biological and prognostic significance of microhomology-associated deletions and optimize their detection parameters to improve patient stratification and inform targeted therapeutic strategies, including PARP inhibition. We optimized the microhomology length threshold (Mlt parameter) using signal-to-noise ratios and survival models. We evaluated the utility of whole-exome (WES) versus whole-genome sequencing (WGS). We then investigated how Loss of Function (LoF) alterations affect microhomology-associated deletion burden and assessed their prognostic significance across an ovarian cancer (OV) cohort and a TCGA Pan-Cancer dataset. Applying an Mlt = 2 threshold maximized the precision and statistical reliability of microhomology-associated deletion identification. WGS yielded higher reproducibility, whereas WES proved insufficient for accurate estimation. Inactivation of tumor suppressor genes, including RB1 and CCDC122, was associated with increased microhomology-associated deletion burden. Importantly, higher burden correlated with extended overall survival in ovarian cancer. Furthermore, while baseline microhomology-associated deletion burden varies by tumor type, Pan-Cancer analysis identified candidate gene-level alterations, including FDX1 and PDE8B, whose LoF increased microhomology-associated deletion burden across tumors. Precise parameterization combined with WGS resolution highlights the extent to which specific gene losses affect deletion burden consistent with MMEJ-mediated repair. Identifying these genetic vulnerabilities and the resulting deletion burden may provide a candidate prognostic biomarker and foundation for personalized therapies.

  bioinformatics2026-09-08v1

• AnnoAudit: a marker-based protocol for auditing single-cell atlas annotations reveals annotation-driven artifacts in a widely used traumatic brain injury resource

  Zhang, L.; Rao, H.; Li, M.; Qian, X.; Zhang, Y.; Yan, Q.; Gao, R.

  Abstract

  Single-cell atlas annotations are routinely treated as ground truth but rarely validated before use. We present AnnoAudit, a marker-based audit protocol that combines four convergent checks - marker scoring, unsupervised clustering, margin-gated module scoring, and applicability-gated pretrained models - into a composite Annotation Contamination Score (ACS), plus a trajectory-correlation fingerprint tracing suspicious signals to their cell type of origin. Applied to CEREBRI (GSE269748), the most widely used single-cell TBI atlas (73 citations), the official "glutamatergic neuron" label is systematically contaminated: of 45,051 labeled cells, only 963 (2.1%) are marker-confirmed glutamatergic neurons; the remainder are microglia (31.4%), astrocytes (18.6%), oligodendrocytes (15.9%), and other types. The contamination generates coherent false signals - a biphasic trajectory for 40 of 307 ion-channel genes, a KCNC3-specific OXPHOS signature, and an inversion of KCNC3 regulation at 7 days - whereas the corrected response is a sustained acute KCNC3 up-regulation conserved across four datasets and three injury models, and the fingerprint traces 96.3% of 244 informative ion-channel trajectories to non-neuronal populations. Simulation-calibrated ACS is 82.6% for CEREBRI and 70.4% for a human ALS atlas (GSE330130); three marker-defined controls pass. AnnoAudit needs only the deposited count matrix and a canonical panel; we propose it as a routine quality step.

  bioinformatics2026-09-08v1

• A shared functional organisation underlies vascular disease remodelling

  Bradford, A.; Bidula, S.; Fabian, L.; Warren, D.

  Abstract

  Cardiovascular disease involves coordinated remodelling across multiple biological processes. Gene-level signatures vary substantially between studies because different combinations of genes can support similar biological processes. Pathway-level analyses can provide more stable representations of these processes but typically consider pathways independently. We hypothesised that grouping related pathways into conserved biological functions and quantifying their relative weighting would reveal a higher-order, transferable property of vascular tissue that we term functional organisation. We quantified the relative weighting of six conserved biological functions across independent transcriptomic datasets spanning human vascular disease, experimental models and therapeutic interventions. Vascular tissues exhibited a reproducible functional organisation defined by the balance of these functions. A dominant remodelling trajectory captured coordinated, nonlinear rebalancing of structural, immune, signalling and metabolic programmes, while a second dimension distinguished contractile/ECM organisation from immune activity. This organisation was preserved across independently reconstructed reference cohorts and remained robust to analytical sensitivity testing. When independent datasets were projected into this fixed framework, biological and clinical phenotypes occupied coherent positions along the remodelling landscape. Plaque-derived vascular, stromal and immune cell populations also occupied ordered functional positions, linking cellular heterogeneity to tissue-level organisation. The same organisational structure generalised across atherosclerosis, peripheral vascular disease and abdominal aortic aneurysm and revealed continuous biological heterogeneity within conventional clinical classifications. Genetic, pharmacological and dietary perturbations reproducibly shifted functional organisation, demonstrating that organisational state is dynamic and biologically responsive. Together, these findings identify functional organisation as a reproducible tissue-level property of vascular remodelling and provide a framework for understanding cardiovascular disease as coordinated rebalancing of biological functions rather than alteration of individual pathways in isolation.

  bioinformatics2026-09-08v1

• CellART: a unified framework for extracting single-cell information from high-resolution spatial transcriptomics

  Chen, Y.; Liu, Y.; Wang, Z.; Zeng, Y.; Chao, Z.; Jiang, P.; Chen, H.; Wang, J.; Xiao, J.; Yang, C.

  Abstract

  Understanding how different cell types assemble into tissues and organs, as well as how they interact to transmit and receive biological signals, is essential for advancing biomedical and biological research. Recent advancements in spatial transcriptomics (ST) technologies have opened new avenues for investigating biological systems by achieving subcellular spatial resolution. Since cells are the fundamental units of life, extracting single-cell information from high-resolution ST data is crucial. However, existing ST platforms often capture sparse transcript counts per spot or measure only a limited number of genes, complicating the extraction of comprehensive single-cell information. In this study, we introduce CellART, a unified framework designed to extract single-cell information across diverse high-resolution ST platforms, including VisiumHD, Xenium, MERFISH, and Stereo-seq. By leveraging multimodal data, such as staining images, spatial transcriptomics data, and single-cell RNA sequencing references, CellART simultaneously performs cell segmentation and cell type annotation through a seamless integration of deep learning and probabilistic modeling. We demonstrate the efficiency, generalizability, and robustness of CellART across various high-resolution spatial transcriptomics platforms, capable of processing datasets containing millions of spots. Comprehensive experiments validate the biological relevance and accuracy of the recovered cellular information within spatial configurations. Notably, we highlight the utility of CellART in breast and colorectal cancer datasets, showcasing its ability to fully leverage high-resolution ST data. By enhancing cellular resolution, CellART facilitates the identification of transient cancer cell states and immune cell subtypes. Furthermore, CellART enables investigations into cancer-immune cell communication, uncovering both established interactions and novel ligand-receptor pairs. The outputs of CellART are compatible with widely used community tools, facilitating a variety of downstream analyses.

  bioinformatics2026-09-08v1

• NANOCUTSIGHT: A NANOPORE-SEQUENCING APPROACH AND ANALYSIS PIPELINE TO ASSESS GENOME EDITING EFFICACY IN VARIOUS CELL POPULATIONS

  Bergeron, D.; Gaudreault, V.; Duval, M.; Nassari, S.; Boudreau, F.; Durand, M.; Choquet, K.; Jean, S.

  Abstract

  Genome editing has revolutionized biomedical sciences and is now an essential tool to define molecular pathways through genetic interaction and loss-of-function studies. Through its diverse variations, it allows for the generation of specific knockout cell lines or organisms, as well as the creation of endogenously edited gene regions. While high-throughput methodologies exist to map CRISPR/Cas9 genetic modifications, the validation of guide efficiencies in cell populations is often performed through analysis of the targeted gene product by western blotting or by deconvolution of Sanger sequencing chromatograms using TIDE or ICE assays. Here, we highlight a rapid nanopore sequencing pipeline, which we have named NanoCutSight, to quantify the percentage of indels at a specific genomic locus and to identify the types of modifications generated. We also benchmarked the methodology on various guide RNAs and in both cultured cell and organoid models. We believe that NanoCutSight will simplify the analysis of complex sample editing and enable the rapid screening of edited samples.

  bioinformatics2026-09-08v1

• TCRdenoise - an unsupervised similarity-based approach for denoising of TCR-pMHC specificity data

  Lund, J. M.; Deleuran, S. N.; Nielsen, M.

  Abstract

  Public repositories of T cell receptor (TCR)-peptide-MHC (pMHC) interactions constitute a critical resource for studying adaptive immunity and developing predictive models of TCR specificity. However, recent evidence suggests that a substantial fraction of reported TCR-pMHC interactions may be incorrectly annotated, limiting the quality of downstream analyses and machine learning applications. Here, we present an unsupervised sequence similarity-based framework for denoising peptide-specific TCR repertoires. The method combines pairwise TCR similarity metrics derived from TCRbase and TCRdist3 with hierarchical clustering and a novel adaptation of the silhouette score designed to address the prevalence of singleton clusters and highly imbalanced cluster structures. By incorporating a pseudo-cluster containing singleton and background TCRs, and by optimising both clustering distance thresholds and minimum cluster-size criteria, the proposed approach identifies TCRs likely to represent true antigen-specific binders while filtering putative noise. Using experimentally validated repertoires from TCRvdb, we demonstrate that the modified silhouette score closely tracks clustering solutions that maximise separation between binding and non-binding TCRs, achieving strong agreement with independent validation based on the Matthews correlation coefficient. Extension to a large collection of peptide-specific TCR data revealed a strong negative correlation between the percentage of TCRs classified as noise and the predictive performance of peptide-specific binding models. Further, the denoising classification labels on this data set were corroborated using structural modeling confidence scores of the peptide-TCR interface extracted from a refined AlphaFold 3 modeling pipeline. Additionally, retraining NetTCR on denoised data improved internal cross-validated performance compared with models trained on the full data set, whereas models trained exclusively on TCRs classified as noise performed close to random. Together, these results demonstrate that sequence similarity-based denoising can effectively enrich for biologically meaningful TCR-pMHC interactions and improve the quality of training data for predictive immunological models. The proposed framework provides a scalable strategy for improving the reliability of public TCR databases and facilitating the development of more accurate TCR specificity prediction methods.

  bioinformatics2026-09-08v1

• Atlas-scale single-cell analysis beyond in-memory paradigm with scAtlasPy

  Xu, H.; Ye, Y.; Zhang, S.; Xie, R.; Li, J.; Lin, J.; Hu, Y.; Gao, L.

  Abstract

  Single-cell atlases are rapidly outgrowing the memory capacity of standard workstations, challenging the in-memory paradigm underlying mainstream computational ecosystems. Here, scAtlasPy decouples scale of atlas from memory capacity by leveraging the disk-resident computing. It enables full-resolution analysis of a 100-million-cell atlas with only 42.9 GB peak memory, whereas state-of-the-art platforms are limited at 3 million cells with 512 GB memory. scAtlasPy achieves 137,745 cells/s, 10.4x faster than scDataset with 82.6% lower memory usage for random minibatch retrieval. Its extensible architecture offers a flexible platform for diverse atlas-scale analytical tasks, facilitating the discovery of complex cellular heterogeneity and functions in massive cell atlases.

  bioinformatics2026-09-08v1

• Novel Pipeline for Large-Scale Comparative Population Genetics

  Majoros, S. E.; Cottenie, K.; Adamowicz, S. J.

  Abstract

  As scientists continue to ask complex questions about biodiversity and deal with increasingly large amounts of data, there is a demand for new methods and computational developments to perform scientific analyses. Analytical pipelines and modules can provide a way to meet these demands and ensure reproducibility in scientific methods and analyses. The goal of this study was to create efficient, reproducible, reusable programming modules that are publicly available for future research. These modules were used to determine population genetic structure measures and compare these measures across species with different biological traits. The functionality of the modules is shown through a case study on Diptera (true fly) species from Canada and Greenland. We leveraged high-throughput DNA sequencing data from Northern areas, as it is a valuable resource and provides new opportunities to study the Arctic. Data were pulled from public databases (Barcode of Life Data System and Global Biodiversity Information Facility), as well as taxon-specific literature. The pipeline we developed in R includes fifteen modules, including modules to prepare and filter the data, calculate population genetic structure measures (e.g., FST), and run a multiple regression. These modules can be easily adapted and applied to a diverse set of animal groups, geographic regions, and biological traits. Best practices were followed for pipeline development, and the modules were designed and tested to work for datasets of different sizes by providing multiple different analyses and filtering options. Biological results were also obtained for Diptera species. Habitat and larval diet were both significantly related to population genetic structure. Evidence of isolation by distance and a relationship between population genetic structure and both latitude and longitude were also found. Overall, this study has created efficient, reusable bioinformatics modules, and provided insight into the factors affecting population genetic structure in Northern fly communities.

  bioinformatics2026-09-07v4

• Learning Universal Representations of Intermolecular Interactions with ATOMICA

  Fang, A.; Desgagne, M.; Zhang, Z.; Zhou, A.; Loscalzo, J.; Pentelute, B. L.; Zitnik, M.

  Abstract

  Molecular interactions underlie nearly all biological processes, yet most representation models describe isolated entities or specialize in a single molecular setting. Here, we introduce ATOMICA, an interaction-centered geometric deep learning model designed to learn transferable representations of intermolecular interfaces across proteins, small molecules, metal ions, and nucleic acids. Self-supervised pretraining on 2,037,972 interaction complexes yields representations spanning atoms, molecular building blocks, and complete interfaces. The latent space captures molecular identity and interaction context, supporting sequence recovery and zero-shot prioritization of residues involved in non-covalent interactions. ATOMICA provides structural information complementary to sequence representations on RNA and protein-pocket ligand classification. Across protein-pocket analyses, ATOMICA distinguishes ATP- and ADP-associated pocket states and retrieves ligand-matched pockets across proteins without detectable structural alignment. The latent space also enables cross-modal comparison, with orthosteric inhibitor embeddings retrieving regions proximal to native peptide and protein interfaces. Applied to the dark proteome, ATOMICA-Ligand predicts candidate ions or cofactors for 2,646 pockets, and five heme candidates show Soret-band shifts consistent with heme association. Together, these results show how interaction-centered molecular representations can transfer structural information across molecular interaction types and generate experimentally testable hypotheses.

  bioinformatics2026-09-07v4

• Benchmark validity in graph neural network scoring of metabolic reaction activity on Recon3D: detecting label leakage, memorized noise and input-invariant models

  Phongwattana, T.; Chan, J. H.

  Abstract

  Context-specific genome-scale metabolic modeling begins with scoring which of the approximately 10,600 human reactions are active in a patient's tumor. Methods in this literature are routinely benchmarked against activity labels obtained by thresholding the same transcriptomic matrix that is supplied to the model as input. We report a self-audit of our own graph attention scorer, MetaGNN, evaluated on TCGA colorectal (n=624), breast (n=1,095) and lung adenocarcinoma (n=517) cohorts, in which two independent failure modes produced a near-ceiling benchmark score and a positive architectural result, neither of which survived inspection. First, under expression-thresholded supervision the framework reaches AUROC 0.9864 +/- 0.0008 on TCGA-BRCA. That figure partitions into 5,925 reactions whose labels are a deterministic threshold of the model's own input, where ranking by the cohort-mean input alone gives AUROC 1.000; and 4,675 reactions whose stored labels we reproduce bit for bit from a seeded pseudo-random number generator, where the model nonetheless reaches 0.9291 +/- 0.0030 by memorizing a patient-invariant label vector that patient-level splitting leaves fully visible during training. Second, on the cohort supervised independently of the input, the archived models never received patient data at all. Their released feature tensors are uniformly zero, and independently trained models show no agreement on which patient deviates where (|r| <= 0.004 on per-patient output residuals, against r = +0.32 between output and input residuals on expression-bearing reactions for a model with verified features). A dispersion ratio comparing between-patient output spread against Monte Carlo Dropout sampling spread sits at 1.02 to 1.03 for all three configurations, against a no-signal null of 1.02 and 2.44 for the verified model. We therefore withdraw a +0.105 AUROC gain attributed to relational edges in an earlier draft of this work. Retraining on rebuilt, verified features gives AUROC 0.5800 +/- 0.0017, below both the raw-expression baseline of 0.6342 +/- 0.0058 that we establish for this cohort and an information-free indicator baseline of 0.6085. Zero-shot transfer of the BRCA model is at or below chance on METABRIC microarray (0.4926 +/- 0.0113, n=200) and on same-platform CPTAC-BRCA RNA-seq (0.4986, n=106). We release the code, the curated colorectal cohort, a script that replays the label vector from its generating seed, and the screening checks we now run before reporting any score. Source code: https://github.com/thiptanawat/MetaGNN-Framework (MIT).

  bioinformatics2026-09-07v1

• labapi: a Python object model for the LabArchives electronic lab notebook

  Li, C.; Lawrimore, J.; Moraczewski, D.; Thomas, A. G.

  Abstract

  labapi is a Python library that enables computational workflows to connect to LabArchives' electronic lab notebook (ELN). Without an Application Programming Interface (API) connection, researchers must manually add workflow outputs through the LabArchives web interface, navigating to the appropriate page and uploading each output so that it appears with the experimental notes that provide context. labapi translates the flat LabArchives API into a Python object model following the existing hierarchy of the web interface, allowing workflows to navigate and modify notebook content through familiar paths. labapi enables researchers to build interconnected workflows that both read in and write data to LabArchives' ELN automatically. Researchers can inspect those outputs in the notebook, and later analysis code can read them back for another stage of analysis.

  bioinformatics2026-09-07v1

• PharmCast: rapid generation of three-dimensional pharmacophore fingerprints from two-dimensional structure without conformer generation

  Muskal, S. M.; McGregor, M. J.

  Abstract

  A three-dimensional pharmacophore fingerprint records the binding features a molecule can present. It is a description of a hand in search of a glove. Because it is defined by presented features instead of two-dimensional structure, it can identify pharmacophoric similarity between structurally distinct compounds and support scaffold hopping and the identification of structurally distinct compounds with comparable binding features. The descriptor has remained a niche tool because its cost is dominated by conformer generation. In the reference pipeline, generating 100 conformers requires 2.82 s of the 2.86 s needed to fingerprint one screening collection compound; the bit calculation requires 0.039 s. We therefore removed the conformational stage. PharmCast is a feedforward neural network that predicts all 10,549 bits of a PharmPrint ensemble fingerprint directly from a SMILES string. On the same machine, PharmCast generated pharmacophore fingerprints for two molecules and compared them in 0.584 ms, whereas the conventional conformer-based pipeline took 5.71 s. PharmCast version 10 was trained on 5,887,229 molecules drawn from a screening collection, activity-backed ChEMBL compounds from 142 to 1000 Da, and peptide loops excised from crystal structures. We evaluated 155,648 purchasable catalog compounds excluded from every training set, 139,700 activity-backed ChEMBL compounds not present in the version 10 training set, and 13,500 peptide loops reserved for testing. Median fingerprint error, Pearson r, and pairwise ranking accuracy were 0.008, 0.980, and 0.936 for screening collection chemistry; 0.016, 0.984, and 0.952 for loop peptides; and 0.027, 0.936, and 0.889 for activity-backed ChEMBL compounds. The reference calculation reproduces itself at an error of 0.006 and r of 0.995. Ensemble pharmacophore fingerprints can therefore be predicted from constitution alone at a cost suitable for screening the collection and optimization. Keywords: pharmacophore, fingerprint, scaffold hopping, surrogate model, virtual screening, applicability domain

  bioinformatics2026-09-07v1

• Keloid transcriptomics reveal heterogeneity in fibroblast subtype enrichment, gene expression, and immune cell responses

  Panzer, J. J.; Pan, M.; Nair, M.; Loveless, I. M.; Adrianto, I.; Huang, L.; Chitale, D.; Francescone, R.; Vendramini-Costa, D. B.; de Guzman Strong, C.; Levin, A. M.; Jones, L. R.

  Abstract

  Keloid disease (KD) is a fibroproliferative skin disorder resulting from abnormal scar formation that causes pain, itching, and decreased quality of life. While multiple KD transcriptomic studies exist, the influence of cell type composition on bulk tissue gene expression is unknown. We characterized fibroblast subtype and immune cell enrichment using bulk RNA-Seq of head and neck keloid and matched adjacent normal skin tissue (MANST) from 14 patients (10 African American and 4 European American). Cell type enrichment was calculated by single sample gene set enrichment analysis. Linear mixed-effects models were employed for 1) differential cell type enrichment across tissue, 2) tissue type-specific associations between fibroblast subtypes and immune cells, and 3) differentially expressed genes (DEGs) across tissue. Validation was conducted in an independent cohort of 8 African Americans. Three fibroblast subtypes and 14 immune cell types were differentially enriched across tissue type. Further, 17 tissue type-specific fibroblast subtype-immune cell enrichment associations were identified, with 14 exhibiting decreased association in keloid tissue relative to MANST. After adjustment for cell type enrichment, MIR31HG and NR4A2 were significant DEGs with the largest positive and negative fold-changes, respectively. By considering cell type enrichment, underlying keloid tissue-specific cell type and gene expression associations were revealed.

  bioinformatics2026-09-07v1

• A systematic evaluation of SIRT6 as transcriptomic biomarker of aging

  Kashuk, E.; Tarakanova, A.; Malygina, A.; Kuzovkina, N.; Ponomareva, A.; Toiber, D.; Khrameeva, E.; Smirnov, D.

  Abstract

  SIRT6 is a NAD+-dependent sirtuin that plays central roles in chromatin regulation, DNA repair, telomere maintenance, metabolic homeostasis, and inflammatory control. Although SIRT6 have long been implicated in aging because of its association with several hallmarks of aging, the available evidence remains largely context-dependent and mechanistic, limiting the interpretation of SIRT6 as a robust and evolutionarily conserved biomarker of aging. To comprehensively investigate the role of SIRT6 as an aging biomarker, we established SIRT6.db, a multi-species transcriptomic resource that integrates SIRT6-targeted perturbation experiments across diverse biological systems and organisms form all available SIRT6-related publications collected via a large-scale textual analysis of the SIRT6 literature using topic modeling. Based on this database, we identified both species-specific and evolutionarily conserved transcriptional and functional signatures associated with SIRT6 perturbation and established their relevance to hallmarks of aging. We showed that SIRT6 expression is generally stable during normal aging, but becomes dysregulated in Alzheimer's disease in cell type and stage-specific manner, highlighting the context dependence of its potential as a transcriptomic biomarker of aging.

  bioinformatics2026-09-07v1

• WGCNA+: AI-powered WGCNA for Integration of Multi-Omics Data

  Zito, A.; Escriba' Montagut, X.; Cano-Muniz, S.; Martinelli, A.; Akhmedov, M.; Kwee, I. W.

  Abstract

  Background: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely adopted systems biology method to discover gene modules and module-trait associations, mostly from transcriptomics. Designed for a single layer, it cannot jointly analyze multi-omics layers, a consequential limitation in modern biomedical research. WGCNA modules are often hard to interpret, requiring vast follow-up for contextualization. Moreover, no integrated framework exists to visualize condition-specific, cross-omics relationships at module or feature level. Results: To address these limitations, we developed WGCNA+, a novel R package extending WGCNA to multi-omics. WGCNA+ offers key innovations: (i) a unified multi-omics pipeline for per-layer network inference and cross-layer module enrichment; (ii) SVD-accelerated topological overlap matrix calculation that greatly reduces computation time; (iii) a consensus framework identifying modules reproducible across independent datasets/conditions; (iv) LASAGNA, a companion R package for phenotype-conditioned, multi-partite graph visualization of cross-omics relationships; (v) AI-powered annotation and infographics offering immediate biological insight. We tested WGCNA+ across public transcriptomics, proteomics, and miRNA datasets. WGCNA+ detects biologically meaningful modules, cross-omics feature and phenotype correlations, and provides AI-powered interpretation that accelerates research. Conclusions: WGCNA+ addresses existing gaps with a principled, efficient framework for co-expression network analysis across omics. It detects cross-omics regulatory modules and their phenotype association to support basic research, biomarker discovery and pathway analysis. It uniquely offers AI-assisted interpretation and infographics, aiding hypothesis generation. Complementing WGCNA+, LASAGNA is a phenotype-aware multi-partite visualization framework to explore cross-omics relationships. Altogether, these features make WGCNA+ an innovative, powerful tool for clinical and translational research. Availability and implementation: WGCNA+ and LASAGNA are implemented in R language for statistical computing, version[&ge;]3.5. WGCNA+ and LASAGNA are fully and freely available with no restrictions (https://github.com/bigomics/WGCNAplus; https://github.com/bigomics/lasagna)

  bioinformatics2026-09-07v1

• Predicting Endometriosis Status and Menstrual Cycle Phase Using DNA Methylation

  Nagasuri, A.; Khan, U.; Grosjean, P.; Siddharth, A.; Kosti, I.; Mortlock, S.; Houshdaran, S.; Rahmioglu, N.; Missmer, S. A.; Zondervan, K. T.; Montgomery, G.; Becker, C. M.; Rogers, P.; Irwin, J.; Oskotsky, T.; Lindquist, K.; Seaman, C.; Giudice, L. C.; Sirota, M.

  Abstract

  Endometriosis is a chronic inflammatory disease associated with pelvic pain, infertility, and delayed diagnosis. Growing evidence suggests that altered DNA methylation contributes to disease development and could serve as a biomarker for disease. We developed a leakage-safe machine learning pipeline to classify endometriosis case-control status and menstrual cycle phase using genome-wide DNA methylation data from eutopic endometrial tissue. The dataset consisted of 984 samples profiled using the Illumina Infinium MethylationEPIC array, with measurements across approximately 759,000 CpG sites. Technical variation was corrected using SmartSVA batch correction. Ridge logistic regression models were trained using stratified 80/20 train-test splits, with regularization strength selected via stratified cross-validation. Feature selection approaches included ridge coefficient ranking, per-CpG t-tests, and univariate logistic regression with FDR correction. Model validity was evaluated using label-shuffling analyses. Menstrual cycle phase classification showed strong performance (mean cross-validation AUROC: 0.971, held-out test AUROC: 0.989), reflecting genome-wide hormonally driven methylation. Ridge regression produced lower but meaningful performance for endometriosis classification (mean cross-validation AUROC: 0.854, held-out test AUROC: 0.875). Ridge coefficient-based feature selection identified compact predictive CpG sets, supporting the hypothesis that endometriosis-associated methylation signal is distributed across many loci rather than a few highly predictive CpGs. Pathway enrichment analyses identified substantial enrichment for menstrual cycle phase but limited enrichment for disease status following FDR correction, consistent with a diffuse endometriosis-associated signal. These findings demonstrate that ridge regression can detect methylation patterns associated with both endometriosis and menstrual cycle phase, highlighting the importance of accounting for cycle-related epigenetic variation in endometrial DNA methylation studies.

  bioinformatics2026-09-07v1

• Learning from tandem mass spectra at scale with a self-supervised foundation model for proteomics

  Nieuwoudt, M.; Reverenna, M.; Patel, D.; Catzel, R.; Houngue, I. H. J.; Daniel, J.; Eloff, K.; Santos, A.; Lopez Carranza, N.; Jenkins, T. P.; Van Goey, J.; Kalogeropoulos, K.

  Abstract

  Mass spectrometry-based proteomics increasingly relies on machine learning, yet existing models are trained for defined supervised tasks such as peptide identification, de novo sequencing or fragment intensity prediction, limiting transfer across datasets, instruments and acquisition methods. Here we present InstaNovo-FM, a self-supervised foundation model for bottom-up proteomics trained to reconstruct masked regions of tandem mass spectra. We assemble a diverse training corpus spanning 1.47 billion MS/MS spectra and 184.6 million high-confidence annotations. We train an encoder-only transformer on the annotated tier using a physics-aware masked reconstruction objective. We demonstrate that the InstaNovo-FM embeddings encode fundamental experimental and biological properties, including fragmentation method, sequence properties and post-translational modifications, without requiring peptide labels. Furthermore, this foundation model directly enables diverse downstream applications, including de novo peptide sequencing, database-free identification and analytical run classification. InstaNovo-FM establishes a unified representation space for peptide fragmentation spectra, enabling robust transferability across the proteomics ecosystem.

  bioinformatics2026-09-07v1

• EISCA and EISTA: Full-Spectrum Pipelines for Single-Cell and Spatial Transcriptomics Analysis

  Wu, H.; Lister, A.; Macaulay, I. C.; Long, K.; Uauy, C.; Lan, Y.; Wickham, G. J.; Swarbreck, D.; Videm, P.; Stubbs, A.; Soranzo, N.; de Waard-van Baardwijk, M.; Nilchi, A. N.; Papatheodorou, I.

  Abstract

  Single-cell and spatial transcriptomics are transforming our understanding of cellular heterogeneity and tissue organization, yet their analytical complexity remains a major bottleneck. Here, we present EISCA and EISTA, two standardized, end-to-end pipelines for single-cell RNA-seq and imaging-based spatial transcriptomics analysis. Built on the Nextflow nf-core framework, both pipelines implement modular, scalable, and reproducible workflows spanning primary, secondary, and tertiary analyses, from raw data processing to advanced downstream analyses. EISCA supports droplet- and plate-based scRNA-seq technologies, while EISTA is tailored for high-resolution spatial platforms including Vizgen MERFISH and 10x Xenium. Together, they integrate state-of-the-art methods for quality control, normalization, clustering, integration, cell-type annotation, differential expression, and cell-cell communication, with EISTA further enabling spatial statistical analyses. A central design principle is to balance standardization with flexibility: workflows can be executed end-to-end or modularly, enabling iterative, exploratory analyses with minimal overhead. Both pipelines deliver rapid preliminary results alongside an out-of-the-box report, facilitating immediate data assessment and accelerating downstream discovery. Case studies in plant immunity and human sepsis demonstrate that EISTA and EISCA reproducibly can be used to recover biologically meaningful insights. Collectively, these pipelines provide efficient, flexible, and scalable solutions for comprehensive single-cell and spatial transcriptomics analyses.

  bioinformatics2026-09-07v1

• A physics-informed hybrid deep learning model for spatiotemporal rice disease prediction using multi-source data

  Jin, Z.; Wang, J.; Huang, W.; Zhang, J.; Ma, H.; Salguero-Gomez, R.

  Abstract

  Accurate, reliable, large-scale disease predictions are essential to ensure rice production. Existing disease prediction models often face a trade-off between interpretability and predictive capability, necessitating the integration of mechanistic knowledge and data-driven learning within a modelling framework. Accordingly, we propose a physics-informed hybrid gated recurrent unit (PI-HGRU) model for spatiotemporal dynamic prediction of rice sheath blight disease, caused by a fungus. Our model embeds differential equations describing disease transmission dynamics into a hybrid gated recurrent unit (HGRU) framework as mechanistic constraints, thereby enabling collaborative modelling between epidemiological processes and data-driven learning. We conducted model training and evaluation using a long-term, multi-source dataset spanning 17 years (2000-2016) and covering 16 major rice-producing provinces in southern China. These rich data include spatiotemporally aligned field disease observations, remote sensing data, meteorological data, and soil property data. In addition, to address the challenges of irregular sampling intervals and inconsistent sequence lengths in disease survey data, we adopted a sliding time-window-based prediction framework. We further conducted a time-window sensitivity analysis to determine appropriate configurations of the input time window and lag time, enabling the model to represent the cumulative and delayed effects of environmental factors. Our PI-HGRU framework substantially outperforms the purely data-driven HGRU baseline model, improving the squared Pearson correlation coefficient (r2) by 22.8% while reducing the root mean square error (RMSE) and mean absolute error (MAE) by 10.2% and 18.0%, respectively. Furthermore, analysis of the models intermediate variables showed that the transmission rate {beta}(t) exhibited interpretable relationships with environmental conditions within the input time window, providing a process-related link between environmental drivers and modeled disease transmission dynamics. Overall, our work demonstrates that integrating epidemiological mechanisms into deep learning models in a physics-informed manner can improve predictive accuracy and stability while enhancing model interpretability, highlighting its potential for large-scale disease forecasting and precision disease management.

  bioinformatics2026-09-07v1

• Real Science Is Harder Than Benchmarks: Evaluating Advanced AI Frameworks on Published Studies. II. Antibody Properties, Lipid-RNA Interactions

  Bhutada, P.; Goyal, N.; Lakhankiya, T. K.; Narahari, S. D.; Thangaraju, S. S. N.; Nayak, T. S.; Peng, Y.; Thota, G. S. A.; Thota, R. S. D.; Wang, Z.; Lee, K.; Sinitskiy, A.

  Abstract

  Artificial Intelligence (AI) frameworks for automating scientific research have shown strong performance on benchmarks, but their utility for real-world industrial research remains insufficiently characterized. Extending the analysis presented in the first paper of this series, we evaluated the same five advanced AI research frameworks (Kosmos, K-Dense, ToolUniverse, BioAgents from bio.xyz, and the AI Scientist-v2 from Sakana AI) on two more projects of high practical importance for biopharmaceutical development: predicting antibody developability properties with the use of pretrained protein language model embeddings, and modeling non-covalent lipid-RNA interactions in lipid nanoparticles with all-atom molecular dynamics (MD) simulations. The AI frameworks again showed genuine strengths, including unprompted identification of subtle methodological issues, successful use of pretrained protein embeddings, and consistent reporting of p-values and confidence intervals often absent from the original papers. However, no framework approached the scope of the original studies, and severe failures and hallucinations were observed. Our results confirm and extend the conclusion of the first paper that real published research from pharmaceutical companies that we tried to reproduce proved to be considerably harder for current AI frameworks than standard benchmarks suggest.

  bioinformatics2026-09-07v1

• PoolParty: streamlined design of DNA sequence libraries in Python

  Liu, Z.; Cordero, A.; Kinney, J. B.

  Abstract

  Background: Computationally designed DNA sequence libraries are essential components of massively parallel reporter assays (MPRAs), deep mutational scanning (DMS) experiments, and other multiplex assays of variant effect (MAVEs). They are also increasingly used in silico to analyze genomic AI models. Designing these libraries, however, remains tedious and error-prone due to the scarcity of purpose-built software. Results: Here we describe PoolParty, a Python package that streamlines the design of complex oligo pools using a simple but flexible API. In PoolParty, each library is represented by a computational graph that can be specified in just a few lines of code. Over 50 built-in operations cover nucleotide- and codon-level mutagenesis, motif insertion, barcode generation, and more. PoolParty automatically generates informative names for each sequence and provides "design cards" detailing how each sequence was generated. Visualization methods let users quickly audit library content and inspect the underlying graph. PoolParty thus transforms oligo pool design from a tedious task requiring custom functions and scripts into a structured, transparent, and reproducible process. Conclusions: PoolParty streamlines the design of DMS, MPRA, and other multiplex assay libraries, and the design cards it provides can help researchers systematically probe and interpret genomic AI models. PoolParty can also be extended to support new assays and analysis strategies as they emerge.

  bioinformatics2026-09-05v3

• Relational Graph Convolutional Networks for Glioblastoma Biomarker Discovery via ceRNA and Copy Number Variation Analysis

  Khandelwal, S.; Zhan, J.; Jarvis, N.

  Abstract

  Glioblastoma (GBM) is a highly aggressive brain tumor with a five-year survival rate of 6.9%, attributable in substantial part to the shortage of reliable biomarkers. Competing endogenous RNA (ceRNA) and copy number variation (CNV) analyses each carry biomarker-identification potential, but existing work treats them separately and does not integrate multiple regulatory mechanisms. We therefore applied relational graph convolutional networks (RGCNs) to ceRNA and CNV knowledge graphs under a late-fusion ensemble architecture. Across 10-fold cross-validation the RGCN discriminated best among the graph architectures tested (AUCROC 0.874 {+/-} 0.070), significantly exceeding graph convolutional, graph attention and relational attention networks. Combining the ceRNA and CNV branches at the decision level gave the best overall performance (AUCROC 0.883 {+/-} 0.072; PR-AUC 0.208 {+/-} 0.152) and improved on the ceRNA-only model in precision--recall terms, although that improvement does not survive correction for multiple comparisons and we therefore report it as suggestive. Screening the late-fusion ranking against the existing glioma literature left five candidates that are absent from the curated glioblastoma biomarker set and the subject of at most one prior glioma report, among them hsa-miR-203b and hsa-miR-5683, each differentially expressed by more than fivefold on a log_2 scale. All five are computational predictions. Relational graph learning over a ceRNA network, combined with genomic dosage at the decision level, is thus a workable framework for biomarker prioritization, and the five loci give targeted experimental work a place to start.

  bioinformatics2026-09-05v2

• platpy: A spatial-first framework for multi-layer spatial transcriptomic analysis

  Maynard, T. M.

  Abstract

  Background The emergence of accessible spatial transcriptomic platforms such as 10x Genomics Visium HD and Xenium has created demand for analysis tools that can handle the complexity and scale of spatial datasets. Current frameworks approach spatial data primarily as an extension of single-cell RNA-seq pipelines, where spatial coordinates are retained as metadata rather than treated as a first-class organizing principle. As a result, common tasks such as multi-modal data alignment, region-of-interest selection, and cross-resolution visualization require manually managing disparate data types, coordinates, and scales, making spatial analysis unnecessarily time-consuming and error-prone. Results We present platpy (pipeline for layered analysis of transcriptomics), a Python-based "spatial-first" framework that treats absolute physical micron coordinates as the organizing principle for all data types. All data -- morphology images, transcript point clouds, expression matrices, segmented cells, and user-defined regions -- are stored as typed objects ("Channels") that carry their own spatial metadata, keeping all layers in automatic registration regardless of platform, resolution, or analysis operation. Two complementary interfaces simplify access to underlying data: the ViewPort, a compositing engine for efficient multi-channel visualization, and the DataPort, which extracts raw data in its native format for downstream analysis. A set of spatial analysis tools demonstrates the practical benefits of the framework, including ROI-based expression binning, cortical unfolding, and sub-micron fine alignment of transcript and image data. The use of modern Python data management methods helps maintain the efficiency of the framework, allowing for quick visualizations and analysis with a low memory footprint. Conclusions Platpy is designed to complement rather than replace widely used tools in the spatial analysis ecosystem (scanpy, squidpy, CellPose, StarDist), by handling the spatial mechanics of large datasets so that the analyst can focus on the biology. Platpy is freely available under the MIT license at https://github.com/maynardt/platpy.

  bioinformatics2026-09-05v2

• Covariance Nonstationarity is Evident in Spatial Transcriptomics and Provides a New Categorization of Spatially Varying Genes

  Velidi, P.; Wei, Z.; Nathoo, F.

  Abstract

  Gaussian process models underlie many spatial transcriptomics tools but typically assume stationary covariance. Covariance non-stationarity has long been recognized in spatial statistics as an important feature of spatial data, yet it has received little attention in spatial transcriptomics. We show that this omission is consequential: covariance non-stationarity is substantially evident across spatial transcriptomic datasets and alters the characterization of spatially varying genes. While typically ignored, non-stationarity of spatial covariance in gene expression may correspond to tissue heterogeneity or cell aggregates. Across 12 Visium datasets, we use approximate Bayes factors from R-INLA to compare stationary and non-stationary Mat'ern covariance functions. Evidence for covariance non-stationarity appears in 3% to 50% of genes across tissue samples. We find that gene sets associated with immune, cytokine, and other effector functions are enriched among genes favoring non-stationary spatial covariance. Covariance stationarity is therefore not a benign technical simplification in spatial transcriptomics; it is frequently violated, the violation is biologically structured, and it changes the definition and classification of spatially varying genes.

  bioinformatics2026-09-05v2

• Hierarchical Breakdown of RNA Structure Prediction in CASP16: From Reliable Local Helices to Speculative Multimer Assembly

  Nithin, C.; Pilla, S. P.; Kmiecik, S.

  Abstract

  CASP16 provided a community-wide benchmark for assessing RNA structure prediction, including the first large-scale blind assessment of RNA-RNA multimer prediction. CASP16 results showed that accurate three-dimensional modeling, especially for RNA-RNA multimers, remains a major challenge across the field. In this work, we use the submissions of our group (LCBio) as a diagnostic case study to examine the current limits of RNA structure prediction. In the official CASP16 best-of-submitted-models analysis, our workflow ranked first in the RNA-RNA multimer category and remained competitive for monomers. This makes the submitted model set useful for examining why high-ranking multimer predictions can still deviate substantially from experimental structures. We combine hierarchical analysis with representative case studies to connect this field-wide limitation to specific structural failure modes, showing that prediction accuracy decreases from relatively reliable canonical base-pairing and local helical organization to less reliable non-canonical interactions, stacking geometry, tertiary motifs, and assembly-level features. In RNA-RNA multimers, errors in monomer structure can combine with uncertainty in interface geometry and model selection, reducing the accuracy of the assembled complexes. These findings point to monomer structure accuracy, interface modeling, and model selection as key areas for improving RNA-RNA multimer prediction.

  bioinformatics2026-09-04v3

• Adding layers of information to scRNA-seq data using pre-trained language models

  Krissmer, S. M.; Menger, J.; Rollin, J.; Vogel, T. M.; Binder, H.; Hackenberg, M.

  Abstract

  Pre-trained language models promise to enrich single-cell analyses with contextual information from large biomedical text corpora, but it remains unclear how to optimally align this knowledge with quantitative scRNA-seq data. To address this, we construct text-based training datasets from both scRNA-seq data and biomedical literature targeted to the experimental setting at hand. We then fine-tune lightweight encoder-only biomedical language models to learn a shared, literature-enriched representation. Controlled evaluations across immune and developmental datasets show that this representation preserves cell identity while adding robust and interpretable contextual layers of functional, disease-associated, and developmental information to single-cell analysis workflows.

  bioinformatics2026-09-04v3

• RNA structure conservation in plastids across plant evolution

  Mehta, D.; Xiao, C.; Hua, J.; Siqueira Reis, R.

  Abstract

  Plastid genomes are deeply evolutionary conserved. RNA structures within the primary or mature transcript play central role in plastid regulation of RNA processing, stability, and translation. However, the identity and conservation of RNA structures selected in plastid's evolution are still largely elusive. Here, we developed a stringent, covariation-based pipeline that perform an unbiased screen for conserved RNA secondary structures across entire plastid genomes. We analysed ~14,000 plastid genomes and identified a repertoire of 57 high-confidence conserved structures. We recovered known functional classes, e.g., 16S rRNA, tRNA, group II intron, and 3' end stem-loop, evidencing that our genome-wide analysis is reliable. We further uncovered novel putative cis-acting structures within the UTRs and introns of key photosynthetic genes, including psbN, clpP, and atpF, as well as putative trans-acting antisense RNAs to petB and psbT, suggesting uncharacterized elements with major regulatory function. Experimental in vivo RNA probing demonstrated that nearly half of the conserved structures adopt the predicted conformation in Arabidopsis plastid. Our comprehensive, yet stringent atlas of conserved plastid RNA structures provides the foundations for new regulatory discoveries in plastid biology.

  bioinformatics2026-09-04v3

• Integrating Spatially Adjusted Protein Summaries for Survival Prediction in Spatial Proteomics

  Ahn, S.; Oh, E. J.; Prada, D.; Shojaie, A.

  Abstract

  Recent advances in spatial proteomics, particularly imaging mass cytometry, enable the measurement of protein expression at the single-cell level while preserving a spatial context. Conventional survival analyses, however, typically rely on patient-level averages of protein intensities and therefore overlook spatial heterogeneity and tissue architecture. To address this limitation, we introduce a framework that incorporates spatial information into survival modeling by generating spatially adjusted protein summaries (SAPS). In this approach, cell-level protein intensities within each patient are modeled using spatial spline regression to capture spatial trends. From these models, we extract two complementary features: a spatially adjusted mean expression and a residual variance that reflects cell-to-cell variability unexplained by spatial effects. These summaries are then incorporated into Cox proportional hazards models in combination with clinical covariates. We further show that our estimator is asymptotically equivalent to an oracle estimator under mild regularity conditions. In simulation studies, our proposed framework achieved improved predictive performance compared to other alternative methods. The application of the method to breast cancer imaging mass cytometry data indicate that spatially adjusted summaries may enhance survival prediction and reveal biologically interpretable spatial protein patterns, suggesting high translational potential. This methodology offers an efficient means of translating complex spatial proteomics data into patient-level features, providing both improved survival prediction and new insights into the role of spatial heterogeneity in cancer outcomes. R package is available on the Comprehensive R Archive Network repository at https://cran.r-project.org/web/packages/SurvSPro/index.html

  bioinformatics2026-09-04v2

• Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

  Kalkhoven, E.; Baak, R. E.; Hooiveld, G. J. E. J.; Schrauwen, P.; Hoeks, J.; Raymakers, R.; van der Stolpe, A.; Kersten, S.

  Abstract

  Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

  bioinformatics2026-09-04v2

• Creating DNAm Algorithms Using the Illumina Methylation Screening Array (MSA)

  Seale, K.; Hassouneh, S.; Giosan, I.; Sugden, K.; Balague-Dobon, L.; Dwaraka, V.; Lasky-Su, J. A. B.; Mallin, M.; Caspi, A.; Moffitt, T.; Smith, R.; Carreras-Gallo, N.

  Abstract

  Most established DNA methylation (DNAm) biomarkers were developed on legacy Illumina EPIC arrays. The Infinium Methylation Screening Array (MSA) offers a lower-cost, higher-throughput alternative with reduced probe content, but EPIC-trained algorithms cannot be assumed to transfer directly. Here we present a reproducibility-based framework for developing and transferring DNAm algorithms on the MSA. Using paired biological replicates profiled on EPICv1 and MSA (1,764 EPICv1-MSA sample pairs, plus within-array MSA replicates on the same and different beadchips), we quantified probe-level agreement using mean absolute error (MAE) and intraclass correlation coefficients (ICC). Of 140,150 CpG sites shared between EPICv1 and MSA, 40,786 (29.1%) met both stability criteria (MAE < 0.05 and ICC(2,k) > 0.6). This stable feature space supported two modelling streams. First, we trained 134 epigenetic biomarker proxies (EBPs) natively on MSA, with and without kernel principal component analysis (kPCA) for sample-level harmonisation. All 134 reached same-beadchip ICC(2,1) >= 0.80 (median 0.97) and 96.3% reached different-beadchip ICC(2,1) >= 0.60 (median 0.81), with a median Spearman correlation of 0.48 against observed values. Among the 72 kPCA-selected models with a comparable stable-probe baseline, 70 (97%) showed higher cross-beadchip ICC (median improvement +0.18). Second, we transferred three established clocks using model-specific strategies: OMICmAge and SystemsAge were retrained to estimate their EPICv1-derived values (held-out test-set rho = 0.944 and 0.912-0.949), whereas DunedinPACE required stable-probe normalisation and robust linear calibration, which raised cross-array ICC(2,1) from 0.784-0.810 to 0.891-0.925 and reduced MAE from 0.085-0.089 to 0.041-0.050 across three sample sets. Reduced probe content does not preclude reproducible DNAm biomarker measurement, and transfer strategy must be matched to model architecture.

  bioinformatics2026-09-04v1

• On doubting image quality assessment metrics for microscopy virtual staining

  Li, W.-s.; Way, G. P.

  Abstract

  Pairing label-free microscopy with virtual staining could reduce the cost and experimental burden of fluorescence microscopy, but its impact is conditional on generalizable inference. Most virtual staining studies assess performance using image quality assessment (IQA) metrics developed for natural images, yet how well these metrics translate to microscopy remains unknown. Here, we examined the behavior of seven commonly-used full-reference training objectives and metrics, MAE, PSNR, SSIM, foreground PSNR and SSIM, LPIPS, and DISTS, under controlled image degradation and realistic out-of-distribution virtual staining. We applied graded intensity, textural, and morphological transformations to Cell Painting images spanning 18 cell lines, seeding densities, and fluorescence channels. Channel, cell line identity and seeding density explained substantial metric variation after controlling for degradation magnitude. DISTS and foreground metrics showed more favorable balance between degradation sensitivity and biological invariance, although no metric reported performance independent of biological context. Incrementally degrading images and evaluating concomitant metric degradation further revealed that most metrics used only a small fraction of their nominal numerical ranges and frequently plateaued while image degradation visibly continued. We next trained three popular virtual staining model architectures (UNet, WGAN-GP, UNeXt) on five U2-OS seeding densities separately, and computed metrics on model predictions across 17 unseen cell lines. We observed that architecture and training U2-OS seeding density together explain less than 2% of metric variation. Visual inspection suggested comparable scores across cell lines correspond to qualitatively distinct errors, such as differences in cell morphology and marker intensity. These findings show that conventional IQA metrics do not effectively translate to virtual staining applications. Selection or optimization of virtual staining models against real application such as in label-free high content drug screening should instead be approached in an application-oriented fashion.

  bioinformatics2026-09-04v1

• Gene function prediction from bulk coexpression is bounded by cell-type-level signal

  Adrian-Hamazaki, A.; Pavlidis, P.

  Abstract

  It is widely accepted in genomics that coexpression of RNA transcripts suggests a commonality of function. This intuition is explicitly leveraged in machine learning methods that predict gene function, where it is often combined with other features such as protein interactions and sequence similarity. For example, including coexpression data from human tissue expression boosts performance for predicting Gene Ontology annotations. However, the biological underpinnings of this observation have not been well-investigated. Building on earlier results from our group, in this work we show that gene function is predictable from coexpression substantially because it reflects differences in expression between cell types, and these differences are also intrinsic to the ground truth labels. Using simulations and analyses of real data, we show that variance in the cellular composition of bulk samples impacts function learnability and attribute this to cell type marker gene content in the GO terms. We further show that cell type profiles, where the relationship between gene expression and cell type is made transparent, are effective for predicting gene function while increasing interpretability. These results indicate that function prediction models trained on bulk coexpression are largely limited to cell-type-level resolution rather than fine-grained biochemical function, with direct consequences for how such predictions should be interpreted.

  bioinformatics2026-09-04v1

• Modelling interpretable patient-level representationsfrom structured and simple multimodal data

  Oksza-Orzechowski, K.; Lazecka, M.; Koperski, L.; Wojtowicz, D.; Mozejko, M.; Schulz, D.; Liechti, R.; Marzetta, F.; Morfouace, M.; Hong, H. S.; Tissot, S.; Bodenmiller, B.; Staub, E.; Szczurek, E.

  Abstract

  Patient cohort profiling increasingly includes structured views for multiple modalities, such as single-cell RNA sequencing, spatial transcriptomics or proteomics, and histology, each providing multiple subobservations per patient, including single cells, spatial spots or patches. To model such data along with simple patient-level views, current multimodal integration methods typically rely on separately precomputed summaries and fail to fully leverage information in structured views. Here we present FACTMx, a variational framework that jointly models structured and simple views to learn interpretable patient-level representations. FACTMx couples latent patient factors with subobservation clustering and per-patient component proportions, enabling direct interpretation and downstream association analyses. The framework supports different structured-view mixture assumptions, including topic- and Gaussian-structured data, while retaining modular encoder-decoder parameterisations. In simulations spanning sparse and dense dependencies and multiple noise regimes, FACTMx improved reconstruction, integration and recovery of structured components relative to previous methods. Applied to non-small cell lung cancer cohorts, FACTMx captured survival-associated latent signals linked to immune microenvironments, gene expression pathways and spatially coherent histological patterns. In a longitudinal coronary syndrome cohort, FACTMx highlighted an outcome-associated axis connected to ejection-fraction change, immune cell states, soluble mediators and cardiac injury markers. These results support joint structured-simple modelling for interpretable multimodal patient stratification.

  bioinformatics2026-09-04v1

• A pan-cohort transcriptional landscape of breast cancer maps subtype and microenvironmental programs

  Arora, S.; Suresh, R.; Holland, N.; Glatzer, G.; Jensen, M.; Konnick, E. Q.; Pritchard, C.; Li, Y.; Parsons, H. A.; Hurvitz, S. A.; Holland, E. C.

  Abstract

  Breast cancer comprises heterogeneous transcriptional states that are incompletely captured by discrete clinical or molecular subtype labels. To visualize this heterogeneity in a unified framework, we integrated bulk RNA-seq data from 2,284 patient samples across 13 studies using 18,089 protein coding genes, a harmonized processing pipeline, batch correction, consensus clustering and PaCMAP dimensionality reduction to construct an interactive breast cancer transcriptional landscape. Consensus clustering identified five major regions, which were annotated using PAM50 scores calculated for each sample: Luminal A, Luminal B, HER2 enriched, and two basal associated clusters. The basal clusters separated into an immune rich region marked by T cell-inflamed, tumor-associated macrophages (TAM), and low-purity signatures, and a cell-cycle driven region enriched for proliferation and DNA replication programs. Overlay of marker genes, pathways, kinases, neuronal like signaling programs, cancer associated fibroblasts (CAF) states, and TAM programs revealed spatially organized subtype biology and microenvironmental heterogeneity. Finally, projection of therapy associated resistance signatures identified landscape regions linked to predicted resistance to HER2-targeted therapy and hormone receptor directed endocrine therapies. By enabling interactive exploration of transcriptional states, marker genes, pathways, and therapeutic response programs, this resource provides a community framework for biomarker discovery in breast cancer.

  bioinformatics2026-09-04v1

• QuickSeg: A fast, versatile and accurate algorithm for genomic copy number segmentation using dynamic programming

  Schlotmann, B.; Favero, F.; Locallo, A.; Weischenfeldt, J. L.

  Abstract

  Copy number alterations are among the most common genomic aberrations in cancer and their accurate identification relies on robust segmentation of sequencing read-depth signals. Existing segmentation methods typically balance computational efficiency against segmentation accuracy and remain sensitive to technical artifacts present in sequencing data. Here, we present QuickSeg, a fast and versatile methodology that uses an exact dynamic programming algorithm to detect copy number segments using median-based error function. Motivated by the observation that sequencing depth distributions contain a small but pervasive population of outlying observations, this approach provides increased robustness to technical noise while simultaneously reducing the computational complexity of the segmentation problem. Across whole-genome sequencing of cancer cohorts, using breakpoint-supported somatic copy number alterations, we demonstrate improved segmentation precision over two widely used baseline methods, Circular Binary Segmentation (CBS) and Piecewise Constant Fitting (PCF), across a broad range of sensitivity thresholds. QuickSeg also consistently outperformed both methods with respect to runtime and memory usage. Collectively, our results show that robust median-based optimization provides both biological and computational advantages for copy number segmentation, enabling accurate analysis of large sequencing cohorts with minimal computational requirements.

  bioinformatics2026-09-04v1

• AltraFlowSOM: A Semi-Supervised Framework for Imaging Mass Cytometry Phenotyping

  ANILKUMAR REKHA, A.; Bettacchioli, E.; Le Dantec, C.; Hemon, P.; Jouve, P. E.; Hillion, S.

  Abstract

  Imaging Mass Cytometry (IMC) enables the simultaneous quantification of 40+ protein markers at single cell resolution in tissue, however biologically faithful phenotyping at scale remains a critical bottleneck. Unsupervised clustering fragments coherent populations or conversely merges biologically incoherent ones into a single cluster, supervised classifiers impose a closed vocabulary, and the presence of rare subsets (encoding clinically relevant biology) in conjunction with abundant subsets may be detrimental to detection performances. We present AltraFlowSOM, a semi-supervised extension of FlowSOM that embeds partial expert annotations directly into self-organizing map training via a two-layer SuperSOM architecture, balancing label-guided topology anchoring with unsupervised discovery. By anchoring the map to biologically labelled reference points, AltraFlowSOM circumvents the canonical dependency between batch correction and clustering. Evaluated under Leave-one-out cross validation on two independent IMC cohorts, Lupus Nephritis (n=22 ROIs) and Sjogren syndrome (n=10 ROIs), AltraFlowSOM outperformed all unsupervised and supervised baseline on Adjusted Rand Index, F1 scores (macro and weighted), weighted purity and in the identification of rare populations. The median Treg cell recovery exceeded that of all comparator methods. AltraFlowSOM resolves the scalability-alignment-discovery trilemma, by establishing a semi-supervised SOM as a generalizable method for high dimensional IMC phenotyping.

  bioinformatics2026-09-04v1

• Poly Pipeline: A Polyvalent Spatial Transcriptomics Workflow Validated Across Polyploid and Diploid Organisms

  Carvalho, P. C.; Millsteed, T.; Henry, R. J.

  Abstract

  Spatial transcriptomics (ST) has emerged as a transformative approach for visualizing tissue landscapes, yet it faces significant challenges regarding data standardization, sparsity, and the analysis of complex genomes, particularly polyploid plants. To address these limitations, we introduce Poly Pipeline, a robust and universal bioinformatic workflow designed to streamline analysis across diverse plant and animal genomes. The pipeline integrates a comprehensive converter for proprietary formats, clustering algorithms, and hdWGCNA co-expression networks, which indirectly preserves the expression signatures of low-expressed duplicated genes. Benchmarking across datasets from wheat, rice, Arabidopsis, and mouse demonstrated the broad applicability of the pipeline in identifying relevant clusters, showing effectiveness across diverse organisms and data types. By providing a unified and reproducible framework, Poly Pipeline addresses a critical gap in analyzing genomic redundancy, especially that related to polyploidy, and promotes FAIR data principles for the broader scientific community.

  bioinformatics2026-09-04v1

• Restrictome-EVOLVE: population-resolved haplotype architecture of human antiviral restriction-factor loci

  Maghembe, R. S.; Bahati, S. Y.; Makaranga, A.

  Abstract

  Human antiviral restriction factors act across multiple stages of viral replication, but whether their population-resolved haplotype architecture differs systematically from comparable genomic regions is unclear. We tested this using phased public human genomic data from 660 individuals in seven African and African-diaspora populations, representing 30 canonical restriction-factor units and 436 target windows. Each canonical unit was compared with 80 exact matched genomic controls, yielding 2,400 frozen controls and 4,429,760 target-control endpoint comparisons across 19 retained haplotype endpoints. All 30 canonical units showed lower differentiation effects and lower robust population-private haplotype effects than their matched controls. Within-population diversity effects were higher in 19 of 30 units, whereas dominant-haplotype concentration effects were lower in 22 of 30. Nineteen units occupied a deconcentrated/high-diversity state, eight a concentrated/low-diversity state, and three a lower-diversity/lower-concentration state. Of 127 global endpoint/context summaries, 88 reached a global false-discovery-rate q value below 0.05; 76 were lower in restriction-factor targets and 12 were higher. Directional sign-test inference detected widespread repeated displacement relative to matched controls, whereas no matched-cell empirical-rank test reached global false-discovery-rate significance. These results show that human antiviral restriction-factor loci occupy a reproducible matched-control haplotype architecture characterized by attenuated population partitioning and reduced robust private structure, together with substantial locus-specific variation in within-population diversity and haplotype concentration. The comparative framework separates population-genomic structure from claims of functional or adaptive causality.

  bioinformatics2026-09-04v1

• When DL-Based Prescreening Meets Synthon-Based Docking: Target-Adapting PharmacoNet via MEL-Steered Correction

  Liu, W.; Hong, Y.; Ku, T.; Lee, W.; Nguyen, E.; Xu, A.; Katritch, V.

  Abstract

  As chemical libraries expand into the trillions of molecules, Virtual SYNthon Hierarchical Enumeration Screening (V-SYNTHES) has emerged as a leading strategy for making gigascale virtual screening computationally tractable. In V-SYNTHES, a Minimal Enumeration Library (MEL) of chemical fragments is docked against a target first, and only the top-scoring fragments are expanded into full ligands for large-scale docking. However, among the large number of comparably well-docked fragments, only a small fraction can be expanded under a fixed docking budget, leaving most similarly promising fragments unexplored. General-purpose prescreening tools can be adopted to address this constraint, reallocating the same docking budget across a larger pool of fragments' enumerated full ligands by their proxy score. However, such tools are applied without accounting for target-specific pocket environments. One such method, PharmacoNet, predicts interaction hotspots from a protein structure and ranks candidates via graph matching against a fixed set of interaction-type weights. We recognize that V-SYNTHES's initial fragment-docking step, ordinarily used only for selection of best fragments for expansion, already reveals which of these hotspots and interaction types a given pocket actually favors, and we can recover this signal to fine-tune PharmacoNet accordingly. We introduce MEL-Steered PharmacoNet, a parameter-efficient adaptation framework that specializes PharmacoNet to a given target through two composable mechanisms: (i) empirical density-map steering of predicted pharmacophore hotspots, and (ii) empirical fine-tuning of interaction-type scoring weights. Across three structurally distinct GPCR targets (CB2, GPR91, 5-HT2AR), MEL-Steered PharmacoNet achieves substantial enrichment factor (EF100) gains over a random baseline, and improves EF100 over PharmacoNet by 8.94x, 6.87x, and 1.69x, respectively. The fitted per-target weights further reveal distinct, chemically interpretable interaction profiles that PharmacoNet's generic fixed weights fail to capture. These results show that fragment-docking data already generated by the standard V-SYNTHES pipeline can adapt a general-purpose pharmacophore prescreening method to an individual target, significantly improving its performance while retaining its ultra-fast screening ability, with no additional experimental data or model retraining.

  bioinformatics2026-09-04v1

• Evaluating performance bias in face-to-BMI vision transformer models across diverse human populations

  Hoffman, J.; Gurven, M.; Kaplan, H.; Stieglitz, J.; Trumble, B. C.; Beheim, B.; Hooper, P. L.; Lee, R. B.; Phelps, J. R.; Hill, K.; Codding, B. F.; Brewer, S.; Lim, Y. A. L.; Lea, A. J.; Wallace, I. J.; Venkataraman, V. V.; Kraft, T. S.

  Abstract

  Computer vision models that estimate body mass index (BMI) from facial features offer a non-invasive, low-cost alternative to physical measurement, with uses in telemedicine, emergency care where a scale or measuring tools arent available, automated self-monitoring, and large-scale epidemiological research. Most of these models, however, are trained on government records, social media images, and celebrity photographs, sources that introduce dataset biases and fail to represent the general public. This study tests how well a face-to-BMI machine learning model generalizes across populations, specifically how morphological diversity and population-specific training data affect cross-cultural accuracy. We trained and evaluated Vision Transformer (ViT-H/14) models on paired BMI measurements and facial photographs from four Indigenous populations: the Orang Asli of Malaysia, the Ju/hoansi of Southern Africa, the Sama residing in the Philippines, and the Tsimane of Bolivia. To evaluate how training data composition affects predictions, we compared four training strategies, from single-population models (focal models) to models trained on the full combined global dataset (global models). In-distribution training always produced the best performance. Models exposed to a target populations morphology, whether focal or global, consistently predicted BMI most accurately for that population. But when a target population differed from the training sample, adding more cross-cultural variation to training improved out-of-distribution predictions. Therefore, training on a populations own data works best when that data exists, and training on data spanning a wide range of human morphology is the strongest fallback when it doesnt. These findings suggest that while target population training data produces the most accurate results, training on datasets that capture global morphological variation substantially improves performance in unrepresented populations. Broader diversity in training data is essential for developing machine learning health tools that generalize reliably across human populations.

  bioinformatics2026-09-04v1

• siProGenA: Generative siRNA Candidate Construction via Position Proposal and Guide Generation

  Ma, Z.; Zhou, J.; Wang, R.; Deng, Z.; Wu, Z.; Zheng, Y.

  Abstract

  Small interfering RNAs (siRNAs) are short guide RNAs that recruit the RNA-induced silencing complex (RISC) to complementary target sites on messenger RNAs (mRNAs), triggering Ago2-mediated cleavage and gene silencing. siRNA design requires compact candidate sets that cover a target while preserving efficacy, specificity, and practical sequence constraints. Existing pipelines usually enumerate candidate windows, assign a canonical guide to each window, and then rank preconstructed siRNA--mRNA pairs. This has produced strong pairwise efficacy predictors, but leaves a candidate-construction gap: candidate positions and guide sequences are fixed before the model begins to rank them. We address this gap by decomposing siRNA candidate construction into two generative decisions: where to place candidates within an mRNA segment, and what constrained guide variants to consider at a candidate position. We instantiate this framework as siProGenA, using a Discrete Denoising Diffusion Probabilistic Model (D3PM) for mRNA-conditioned position proposal and a Bayesian Flow Network (BFN) for temperature-controlled guide generation. On 62 positive test segments, the diversity-aware final library reaches Hit@1 = 0.790 and Hit@5 = 0.903. In a measured-site controlled Stage~2 evaluation, seed- and cleavage-preserving variants outscore the canonical complement for 89.8% of measured sites, with supporting gains across additional computational scorers, random-mismatch controls, and biophysical diagnostics. Together, the results support a modular proposal--generation view of siRNA candidate construction for prioritizing compact candidate sets.

  bioinformatics2026-09-04v1

• BROOQS: Spectral Methods Resolve Level-1 Hybridization Cycles without Tests of Symmetry

  Arasti, S.; Mirarab, S.

  Abstract

  Modern phylogenomic analyses often seek to reconstruct both vertical and reticulate evolutionary histories. While the prevalence of non-vertical evolution is increasingly appreciated, inferring networks remains conceptually challenging and computationally demanding. Following the success of quartet-based methods for handling gene tree discordance, several quartet-based network inference methods have been developed. A key insight of these methods is that level-1 networks can be constructed by first building a multifurcating tree called tree-of-blobs and then resolving each polytomy into a cycle. This two-step approach makes the problem easier both conceptually and computationally. However, these quartet-based methods often rely on noisy statistical tests of asymmetry in quartet frequencies. Moreover, they either enumerate all quartets, losing some scalability, or subsample them, losing information. We introduce BROOQS, a quartet-based method for resolving trees of blobs into a level-1 phylogenetic network. BROOQS efficiently aggregates information from all quartets around a blob without enumerating them, builds a pairwise similarity matrix, and uses robust spectral ordering algorithms to recover the cyclic ordering without relying on individual quartet symmetry tests. We prove theoretically that our spectral method is consistent under the network multi-species coalescent (NMSC) model. Across simulated and empirical datasets, BROOQS consistently improves accuracy and scalability compared to existing methods and extends to thousands of taxa.

  bioinformatics2026-09-04v1

• Experimental and In Silico Analysis of the Structural Dynamics of Dengue NS2B-NS3 Protease

  Muthuvel, s. k.; BALAKRISHNAN, S. S.; MANJINI, S.; DHAL, K.; DAS, S.; S, D.

  Abstract

  The Dengue virus (DENV), a major global health concern, causes dengue fever, predominantly affecting tropical and subtropical regions. The NS2B-NS3 protease complex of DENV is critical for viral replication, making it a promising target for antiviral drug development. This study investigates the structural dynamics of the NS2B-NS3 protease under varying pH conditions, integrating experimental and computational approaches. The recombinant NS2B-NS3 protease was expressed in E. coli, purified using affinity chromatography, and analysed for purity via SDS-PAGE. Circular dichroism (CD) spectroscopy revealed pH-dependent secondary structural changes, indicating stability at neutral to slightly basic pH and destabilization under acidic and highly basic conditions. Dynamic light scattering (DLS) analysis demonstrated protein aggregation and structural heterogeneity under extreme pH levels. Complementary in silico techniques, including homology modelling and molecular dynamics (MD) simulations, provided detailed insights into the conformational changes of the protease. The modelled structure, validated and refined through computational tools, revealed structural stability at physiological pH, with notable disruptions at pH extremes. DSSP (Dictionary of Secondary Structure of Proteins) and principal component analyses highlighted significant secondary structural transitions, especially at acidic pH where -helices and {beta}-sheets transformed into random coils Docking and MD simulation was carried out with the small molecule for therapeutic analysis between Dengue NS2bNS3 and small molecule. This study emphasizes the pH-dependent conformational plasticity of the NS2B-NS3 protease, contributing to the understanding of its functional mechanisms and providing a foundation for the rational design of pH-specific inhibitors. These findings underscore the importance of structural biology in advancing therapeutic strategies against dengue fever.

  bioinformatics2026-09-04v1

• Modeling Patient-Reported Pain Trajectories with Frequent Minimum and Maximum Scores

  Liu, Y.; Harris, R. E.; Clauw, D.; Bayman, E.; Leroux, A.; Lindquist, M. A.

  Abstract

  Chronic pain is a widespread public health issue that imposes substantial health, emotional, and economic burdens on individuals and communities. Because pain is subjective and lacks objective biomarkers, it is typically measured using patient-reported scores, often on a numerical scale from zero to ten. Increasingly, pain studies use ecological momentary assessment, with multiple daily assessments over days and across study phases (e.g., a series of baseline and post-intervention assessments). These data frequently show many ratings at the extremes (i.e., at minimum or maximum pain scores), commonly referred to as zero- and one-inflation in the statistical literature, along with considerable within-person variability both within and across days. These phenomena present challenges for statistical analyses, as they violate assumptions of most commonly used statistical techniques (e.g., the normality assumption of linear mixed models). We propose a Bayesian beta-binomial mixed-effects model for modeling potential zero- or one-inflated pain scores while accounting for variability using random effects on the mean and variance parameters across subjects. A simulation study demonstrates that the method accurately estimates model parameters across realistic sample sizes, time points, and zero- and one-inflation levels. An application to data from two longitudinal pain studies demonstrates that the model fits the data better and, when correctly specified, yields accurate uncertainty intervals for longitudinal changes in pain compared to existing models, especially for zero- and one-inflated outcomes. Additionally, the model directly estimates the probability of clinically meaningful pain events. The proposed method provides a powerful statistical framework for studying the patient-reported pain trajectories.

  bioinformatics2026-09-03v4

• kamino: fast proteome-wide variant calling for amino acid phylogenomics

  Derelle, R.; Lees, J. A.; Chindelevitch, L.

  Abstract

  Amino acid-based phylogenetics usually relies on first clustering and aligning orthologous proteins. This approach is powerful but computationally demanding. Here, we present kamino, a reference- and alignment-free method that rapidly builds amino acid phylogenomic alignments directly from proteomes. As with similar algorithms, homologous regions are identified through shared sequences flanking variable regions. The method uses local changes in recoded k-mer occupancy to efficiently identify variable positions within these homologous regions and extract the corresponding pseudo-aligned sequences. It generates phylogenetically informative alignments across diverse prokaryotic and eukaryotic datasets. Phylogenetic analyses show that it accurately recovers Mycobacterium tuberculosis lineages, most curated GTDB taxa, and relationships consistent with published Drosophila and mammalian phylogenies, while producing signals broadly similar to BUSCO-based approaches. Runtimes are comparable to genome-based alignment-free methods and several orders of magnitude faster than classical marker-based pipelines, with moderate memory requirements. The method performs well across a broad range of divergence levels, from within-species comparisons to family-level prokaryotic and phylum-level eukaryotic datasets. kamino therefore provides a fast and simple route from proteomes to phylogenomic alignments across a broad range of evolutionary scales. The program is implemented in Rust and freely available at https://github.com/rderelle/kamino.

  bioinformatics2026-09-03v2