Latest bioRxiv papers

Category: bioinformatics — Showing 50 items

• CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants

  Huang, J.; Yang, Z.; Yin, M.; Li, C.; Li, J.; Wang, Y.; Huang, L.; Li, M.; Liang, C.; He, F.; Han, R.; Jiang, Y.

  Abstract

  Genomic selection (GS) leverages genome-wide markers and phenotypes to predict breeding values, with its effectiveness largely dependent on the accuracy of genomic prediction (GP) models. However, GP methods often struggle to capture inter-individual variability and are limited by the curse of dimensionality, where the number of SNPs far exceeds the sample size. To address these challenges, we present CLCNet (Contrastive Learning and Chromosome-aware Network), a novel deep learning framework that integrates contrastive learning and chromosome-aware feature modeling. CLCNet comprises two key components: (i) a contrastive learning module that enhances the model's ability to capture fine-grained, genotype-dependent phenotypic differences among individuals, and (ii) a chromosome-aware module that captures structured feature selection at both chromosome and genome levels, thereby distilling the most informative SNPs. We evaluated CLCNet across four crop species, covering ten agronomically important traits, and compared it with a diverse set of classical linear, machine learning, and deep learning models. CLCNet achieved superior prediction performance, with statistically significant improvements in Pearson correlation coefficient (PCC), ranging from 0.34% to 12.19% over baseline, together with reduced mean squared error (MSE). Performance gains were more pronounced for traits with moderate linkage disequilibrium (LD; r2= 0.21-0.36) and high heritability (h2 > 0.66), such as those in maize, rapeseed, and soybean. For cotton traits characterized by high LD (r2 = 0.74) and lower heritability (h2 < 0.50), CLCNet maintained robust performance without degradation. Overall, these results demonstrate that CLCNet is an effective framework for improving genomic prediction accuracy and holds strong potential for practical applications in plant breeding.

  bioinformatics2026-05-10v7

• A ML-framework for the discovery of next-generation IBD targets using a harmonized single-cell atlas of patient tissue

  Joglekar, A.; Joseph, A.; Honsa, P.; Ruppova, K.; Pizzarella, V.; Honan, A.; Mediratta, D.; Vollmer, E.; Geller, E.; Valny, M.; Macuchova, E.; Zheng, S.; Greenberg, A.; Taus, P.; Kline-Schoder, A.; Konickova, R.; Cerna, L.; Sharim, H.; Ness, L.; Camilli, G.; Chouri, E.; Kaymak, I.; D'Rozario, J.; Castiblanco, D.; Oliveira, J.; Prandi, F.; Popov, N.; Moldoveanu, A. L.; Oliphant, C.; Escudero-Ibarz, L.; Uhlitz, F.; Freinkman, E.; Sponarova, J.; Vijay, P.; Joyce, C.; Leonardi, I.; Nayar, S.; Raveh-Sadka, T.; Solomon, N.; Platt, A.; Ort, T.; De Baets, G.; Corridoni, D.; Wroblewska, A.; Rahman, A.

  Abstract

  Target discovery for IBD has traditionally relied on genetic associations, which lack the cellular resolution needed to identify novel, actionable, cell type-specific disease pathways. Here, we describe an integrated analytical and experimental framework that leverages harmonized single-cell data to systematically discover novel therapeutic strategies for IBD. We used AMICA DBTM, Immunai's harmonized database of single-cell RNA datasets to construct a harmonized 1 million single-cell atlas of the human intestine. We applied a machine learning framework (Immune Patient Representation, IPR) to identify disease-associated transcriptional programs and cell type-specific gene targets. Candidate targets were prioritized using atlas-derived metrics, refined using custom criteria emphasizing translational actionability, and validated across independent clinical cohorts. Select candidates were evaluated in human primary-cell models reflecting the target's cell-type context. The IPR framework identified 85 disease-associated transcriptional programs and ranked 400 cell type-specific target genes across immune and stromal lineages. Disease-associated programs were interpreted using a structured AI-assisted reasoning framework for structured biological reasoning, linking them to IBD-relevant pathways and guiding the identification of novel, promising gene targets. Functional validation of two cell-type-specific candidates, PTGIR in myeloid cells and IL6ST in fibroblasts, confirmed the reduction of inflammatory and fibrotic pathways linked to IBD pathology. Multi-omic profiling and projection of in vitro phenotypes to patient datasets demonstrated the reversal of disease-associated programs via mechanisms distinct from those of existing biologics. Our single-cell anchored, machine-learning framework integrates in silico discovery with experimental validation, revealing new cell type-specific therapeutic opportunities and supporting a scalable approach for precision target discovery in IBD and other immune-mediated diseases.

  bioinformatics2026-05-10v3

• CRISPR-HAWK: Haplotype- and Variant-aware Guide Design Toolkit for CRISPR-Cas

  Kumbara, A.; Tognon, M.; Carone, G.; Fontanesi, A.; Bombieri, N.; Giugno, R.; Pinello, L.

  Abstract

  Current CRISPR guide RNA design tools rely on reference genomes, overlooking how genetic variation impacts editing outcomes. As genome editing advances toward clinical applications, incorporating population diversity becomes essential for ensuring therapeutic efficacy across diverse populations. We present CRISPR-HAWK, a framework integrating individual- and population-scale variants and haplotypes into gRNA design. Analyzing therapeutic targets across 79,648 genomes reveals that genetic variants substantially alter guide performance. For the clinically approved sickle cell disease therapeutic guide targeting BCL11A, we identify haplotypes that completely abolish predicted cutting activity. Across seven therapeutic loci, 82.5% of guides contain variants modifying on-target activity. Variants also create novel protospacer adjacent motif sites generating individual-specific guides invisible to reference-based design. These findings demonstrate that variant-aware selection is critical for equitable genome editing. CRISPR-HAWK is available at https://github.com/pinellolab/CRISPR-HAWK and https://github.com/InfOmics/CRISPR-HAWK

  bioinformatics2026-05-10v2

• Reconstructing True 3D Spatial Omics at Single-Cell Resolution

  Yang, Y.; Luo, Y.; Zhang, K.; Bu, Y.; Xia, Z.; Peng, H.; Yan, R.; Liu, Q.; Chen, Y.; Shen, L.; Chen, E.

  Abstract

  Capturing the three-dimensional (3D) organization of cells is essential for deciphering complex biological processes, yet comprehensive 3D spatial omics is severely hindered by the destructive nature of physical sectioning and the depth limitations of intact tissue imaging. Current computational methods rely on 2.5D stacking of discrete slices, which inherently disrupts tissue topology and fails to resolve continuous depth-dependent molecular gradients. To bridge this gap, we introduce DeepSpatial, an Optimal Transport flow matching framework that models tissue evolution as a continuous dynamic vector field. By solving the underlying probability flow ODEs, DeepSpatial enables the direct extraction of uninterrupted, infinitely resolvable tissue states at arbitrary spatial depths. Using Deep STAR/RIBOmap 3D technologies, we demonstrate that DeepSpatial achieves improved 3D reconstruction fidelity relative to 2.5D approaches, yielding structures that more closely recapitulate native tissue microenvironments in real-world datasets. Across diverse spatial omics modalities, including spatial proteomics using imaging mass cytometry in human breast cancer and spatial transcriptomics using openST in head and neck squamous cell carcinoma metastatic lymph nodes, DeepSpatial produces biologically interpretable and high-fidelity reconstructions across datasets. We evaluated the scalability and robustness of DeepSpatial on a large-scale mouse brain dataset, reconstructing a continuous 3D cellular atlas comprising 39 million cells within 41.6 hours. Systematic downstream characterization validated its ability to recapitulate consistent spatial architectures, cell-type distributions, transcriptomic patterns, and microenvironmental structures across brain regions. Collectively, these results demonstrate DeepSpatial as a generalizable and efficient solution for true 3D spatial reconstruction across scales and modalities.

  bioinformatics2026-05-10v2

• RNAcomp2D: a web visualization tool for comparingmultiple predictions of RNA secondary structure

  Vitale, R.; Milone, D. H.; Stegmayer, G.

  Abstract

  Ribonucleic acids (RNAs) are involved in many important biological processes. In particular, non-coding RNAs are crucial regulators of cellular processes, playing a significant role in gene expression. RNA secondary structure is key to infer their specific function and for understanding how they interact with other molecules. Many computational models have been developed in the last decade to predict the secondary structure, achieving increasingly higher success rates. However, each new method has its own input-output interface, programming language, computational requirements and, sometimes, a dedicated server to run the model or just a source code in a repository. Thus, nowadays it is very hard to obtain predictions from multiple methods and compare them at once. A unified interface is urgently needed, which allows accessing several methods at the same time, visualizing and comparing predictions among them, and also with a reference structure when available. We introduce here RNAcomp2D, a web-based tool that allows users to enter an RNA sequence, or select one from RNAcentral, and obtains predictions of RNA secondary structures using several state-of-the-art methods. Both classical thermodynamic methods and the latest deep learning models are packaged in containers and accessible in an unified website. All the predictions, and the reference structure if available, are shown at the same time in a single graphical interface. Moreover, as new models continue to be developed, this tool is designed to be scalable, allowing the addition of more prediction methods in the future. Web server is available at https://sinc.unl.edu.ar/web-demo/rnacomp2d/. Data and source code are available at https://github.com/sinc-lab/RNAcomp2D

  bioinformatics2026-05-10v2

• Predicting Pre-treatment Resistance or Post-treatment Effect? A Systematic Benchmarking of Single-Cell Drug Response Models

  Shen, L.; Sun, X.; Zheng, S.; Hashmi, A.; Eriksson, J.; Mustonen, H.; Seppänen, H.; Shen, B.; Li, M.; Vähä-Koskela, M.; Tang, J.

  Abstract

  Intratumoral heterogeneity drives variable drug responses in cancer. Single-cell RNA sequencing (scRNA-seq) enables characterization of such heterogeneity and prediction of drug response at single-cell resolution. Accordingly, various computational models have been developed to infer drug response from scRNA-seq data. However, their performance, robustness, and generalizability across different biological contexts remain insufficiently evaluated. To address this gap, we benchmarked representative single-cell drug response prediction models using 26 curated datasets comprising over 760,000 cells across 12 cancer types and 21 therapeutic agents. We constructed balanced and imbalanced scenarios to reflect realistic drug-response label distributions. To address the lack of ground-truth labels in conventional scRNA-seq datasets, we incorporated lineage-tracing data with experimentally validated drug-response annotations, enabling evaluation in a clinically relevant pre-treatment prediction setting. Our results show that prediction performance was markedly higher in cell lines than in tissue samples. Under imbalanced conditions, most methods exhibited sharp performance declines, whereas scDEAL demonstrated the highest robustness. Independent validation using an in-house pancreatic ductal adenocarcinoma dataset further confirmed scDEAL's robustness and ability to capture biologically meaningful state transitions. Label-substitution experiment revealed that this robustness was partially driven by the model's specific training-label construction. However, benchmarking with lineage-tracing data revealed a fundamental limitation: most models capture drug-induced transcriptional changes but struggled to predict intrinsic resistance before treatment. In summary, our study defines the performance boundaries of current approaches and highlights their limitations in addressing intratumoral heterogeneity, class imbalance, and intrinsic resistance prediction, emphasizing the need for the next-generation single-cell drug response models with stronger clinical relevance.

  bioinformatics2026-05-10v2

• A high-quality, chromosome-scale genome assembly of the shade-tolerant wild rice, Oryza granulata

  Zhang, F.; Yang, Y.-h.; Li, W.; Shi, C.; Zhu, X.-g.; Gao, L.-z.

  Abstract

  Oryza granulata Nees et Arn. ex Watt, a diploid wild rice (GG genome), possesses exceptional shade tolerance and is a key genetic resource for rice improvement. However, previous genome assemblies lacked continuity and completeness. Here we present a chromosome-scale reference genome of O. granulata using PacBio SMRT (113*), Hi-C (95*), and Illumina sequencing. The final assembly is ~764.24 Mb, with a scaffold N50 of ~59.32 Mb, and ~96.47% of the sequence anchored to 12 chromosomes. BUSCO completeness is ~98.6%. We annotated ~42,064 protein-coding genes, of which ~95.39% were functionally annotated, along with ~73.46% repetitive elements. The genome assembly and raw sequencing data are available at NGDC (PRJCA061980), NGDC GSA (CRA068332), and NGDC GWH (GWHISVE00000000.1). This high-quality genome will serve as a fundamental resource for evolutionary genomics, conservation biology, and breeding of shade-tolerant rice cultivars.

  bioinformatics2026-05-10v2

• Scalable integration and prediction of unpaired single-cell and spatial multi-omics via regularized disentanglement

  Sun, J.; Liang, C.; Wei, R.; Zheng, P.; Yan, H.; Bai, L.; Zhang, K.; Ouyang, W.; Ye, P.

  Abstract

  Deciphering cellular states requires methods capable of integrating large-scale heterogeneous single-cell and spatial omics data. However, these data are typically unpaired due to destructive assays and further confounded by modality heterogeneity, technical noise, and immense scale. Here we present scMRDR, a scalable computational framework based on regularized disentangled representation learning for integrating fully unpaired single-cell and spatial multi-omics datasets. Built on a unified and structure-preserving architecture, scMRDR removes the need for pairing supervision while maintaining computational efficiency, enabling scaling to large datasets spanning multiple disparate omics modalities. Across diverse real-world benchmarks, scMRDR demonstrates strong performance in batch correction, modality alignment, and biological signal preservation. The framework further supports cross-modal translation across omics modalities and enables spatial coordinate imputation for non-spatial single-cell datasets using a reference atlas. The resulting spatial mapping allows spatially resolved analyses, including identification of spatially variable genes and characterization of epigenetic regulatory programs in their native tissue context. These capabilities position scMRDR as a scalable and versatile framework for large-scale multi-omics integration.

  bioinformatics2026-05-10v2

• scLASER: a robust framework for simulating and detecting time-dependent single-cell dynamics in longitudinal studies

  Vanderlinden, L. A.; Vargas, J.; Inamo, J.; Young, J.; Wang, C.; Zhang, F.

  Abstract

  Longitudinal single-cell clinical studies enable tracking within-individual cellular dynamics, but methods for modeling temporal phenotypic changes and estimating power remain limited. We present scLASER, a framework detecting time-dependent cellular neighborhood dynamics and simulating longitudinal single-cell datasets for power estimation. Across benchmark experiments, scLASER shows consistently higher sensitivity than traditional cluster--based approaches, with particularly pronounced gains in rare cell types and non-linear temporal patterns. Applications to inflammatory bowel disease (95,813 cells, 38 patients) reveal treatment-responsive NOTCH3+ stromal trajectories with high cell type discrimination (AUC > 0.92), while analysis of COVID-19 data (188,181 cells, 84 patients) identifies three distinct axes of T cell activity (cytotoxic effector, NK immunoreceptor signaling, and interferon-stimulated gene programs) over disease progression. scLASER enables robust longitudinal single-cell analysis and optimization of study design.

  bioinformatics2026-05-10v2

• Interpretable neural networks prioritize cancer driver genes from genome-wide dependency landscapes

  Yin, Q.; Chen, L.

  Abstract

  Identifying cancer driver genes and their therapeutic impact remains a core challenge in computational cancer biology. We introduce xNNDriver and xAEDriver, two interpretable neural network frameworks that connect cancer mutations with genome-wide DepMap gene dependencies, pathway activity, and drug-response patterns. xNNDriver is a supervised pathway-guided model that evaluates whether a gene's mutation status is encoded in the genome-wide dependency landscape; we interpret model fitness as a driver potential score, which quantifies the strength of this mutation-dependency signal and prioritizes genes with broad functional footprints. Across 3,008 candidate genes, xNNDriver recovers major established drivers and highlights literature-supported candidates, while pathway analyses reveal biologically coherent programs related to metabolism, growth factor signaling, and immune regulation. To capture combinatorial functional states, xAEDriver uses an unsupervised autoencoder to learn Driver Variant Representations (DVRs), latent binary features guided by the frequency distribution of known driver mutations. DVRs capture cell-line-specific dependency patterns and expression patterns and are associated with drug sensitivity and pathway activity. Together, these interpretable deep learning models demonstrate that gene dependency landscapes encode rich, interpretable signals of oncogenic function and provide a hypothesis-generating framework for prioritizing drivers, pathways, and therapeutic vulnerabilities for further experimental validation.

  bioinformatics2026-05-10v2

• WSInsight: a cloud-native, agent-callable platform for single-cell whole-slide pathology

  Huang, C. H.; Awosika, O. E.; Fernandez, D.

  Abstract

  Translational study of the tumour microenvironment increasingly demands single-cell phenotyping at cohort scale. WSInsight is an open, reusable, cloud-native platform that performs patch- and single-cell H\&E inference on giga-pixel slides streamed from local, S3, or NCI~GDC storage, and returns QuPath- and OMERO-ready outputs with neighborhood-composition features. Validated on TCGA-BRCA and TCGA-CRC, it is callable from pathology viewers and AI agents through a standards-conformant MCP interface.

  bioinformatics2026-05-10v2

• OTRec: Deep learning recommender for prospective druggable disease-target associations

  Ofer, D.; Linial, M.

  Abstract

  Identifying druggable disease--target associations remains a central challenge in translational medicine, limiting therapeutic discovery and repurposing. Here, we present OTRec, a deep learning--based recommender system that ranks such associations at scale and evaluates them in a temporal hold-out setting. Unlike approaches that rely on manually curated or aggregated evidence scores, OTRec employs a two-tower architecture to learn latent representations from 663,351 disease--target pairs. The model integrates heterogeneous inputs, including textual descriptions, ontology-derived features, and biological annotations such as tractability, Gene Ontology (GO) terms, and pathway information. We perform temporal validation by training on the 2022 Open Targets (OT) release and evaluating on clinical trial data from 2025. OTRec improves on the retrospective OT association score (ROC-AUC: 0.872 {+/-} 0.005 vs 0.559; PR-AUC: 0.288 {+/-} 0.009 vs. 0.08). In 5x5 target-disjoint cross-validation, OTRec reaches ROC-AUC 0.950 and PR-AUC 0.844) improving on the OT evidence score (ROC-AUC 0.91; PR-AUC 0.45). We rank the druggable genome across ~19,000 OT platform (OTP) diseases and release ~282,500 candidate associations above a 0.65 score threshold (in-distribution CV precision 0.92), covering 4,346 diseases including 2,322 orphan diseases, through an interactive prediction platform.

  bioinformatics2026-05-10v2

• Entropy Sorting Feature Selection: information-theoretic gene set identification improves single-cell RNA sequencing data interpretability

  Radley, A.; Boezio, G.; Shand, C.; Perez-Carrasco, R.; Briscoe, J.

  Abstract

  Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve cellular heterogeneity, but extracting meaningful signals remains challenging due to technical noise and batch effects. Most methods for denoising scRNA-seq data have focused on using latent representations such as principal component analysis and deep learning to prioritise biological signals. By contrast, despite its influence on downstream analyses, feature selection has received relatively limited attention, leading to widespread reliance on the comparatively simplistic strategy of highly variable gene selection. Here we present Entropy Sorting Feature Selection (ESFS), a modular, user-friendly framework that substantially improves the interpretability of scRNA-seq data. Notably, ESFS reveals complex expression dynamics that are obscured in latent representations. We demonstrate the utility of ESFS in diverse data: identifying coherent developmental programs across eight independent human embryo datasets without batch integration; resolving spatial gene expression in mouse colon missed by conventional analyses; disambiguating shared and tumour-specific microenvironments in glioblastoma; and disentangling spatial, temporal, and neurogenic programs in the developing mouse neural tube. Beyond delivering a powerful and user-friendly software that deepens insight into complex biological systems, our work establishes Entropy Sorting as a novel information theoretic for advanced data analysis methods.

  bioinformatics2026-05-10v2

• Haplotype-resolved diploid genome inference on pangenome graphs

  Chandra, G.; Doan, W. T.; Gibney, D.

  Abstract

  Recent algorithmic advancements have shown how to utilize pangenome graphs in combination with the haplotype reconstruction framework of Li and Stephens to accurately reconstruct a haplotype from a reference pangenome graph and a set of input reads. However, significant work remains in developing techniques that utilize a pangenome graph to obtain a pair of phased haplotypes, called a diploid pair. We introduce new problem formulations and scalable algorithms for inferring phased diploid genomes from a pangenome graph and a set of input reads. We implement them in our tool, DipGenie. The key idea is to jointly optimize genotyping and phasing along global paths through the pangenome graph, guided by a biologically motivated recombination budget that constrains inferred haplotypes to plausible mosaics of reference haplotypes. We evaluate DipGenie on real Illumina short-read data from the highly polymorphic MHC region in 22 leave-one-out diploid experiments, benchmarking against three tools that also operate on graph structures: VG, which samples haplotypes directly from the pangenome graph, and PanGenie + Beagle and Paragraph + Beagle, which derive local graphs from a VCF panel for per-site genotyping and delegate phasing to a statistical method. At full coverage, DipGenie achieves a geometric mean switch error rate (SER) of 0.86%, which is 5.7 x lower than PanGenie + Beagle (4.88%), 7.9 x lower than VG (6.77%), and 13.2 x lower than Paragraph + Beagle (11.35%). For structural variant calling, DipGenie leads with a geometric mean F1-score of 0.571, compared to 0.470 (PanGenie + Beagle), 0.450 (VG), and 0.379 (Paragraph + Beagle). These advantages hold at every coverage level tested.

  bioinformatics2026-05-10v2

• LIVIA: a browser-based tool for assessing and visualizing predicted protein interactions

  Kim, A.-R.; Perrimon, N.

  Abstract

  As protein structure prediction tools become widely adopted across biology, there is a growing need for accessible methods to assess and visualize predicted protein-protein interactions (PPIs). Here we present LIVIA (Local Interaction Visualization and Analysis), a browser-based tool that computes local PPI confidence metrics across multiple prediction platforms, identifies predicted interface residues, embeds an interactive Mol-star 3D viewer, and generates visualization scripts for ChimeraX and PyMOL. The tool automatically detects prediction formats; all parsing and computation occur locally on the users machine. LIVIA is freely available at https://flyark.github.io/LIVIA.

  bioinformatics2026-05-10v1

• Building an open ecosystem for molecular neuroimaging: standards and tools from the OpenNeuroPET initiative

  Ganz, M.; Norgaard, M.; Pernet, C.; Matheson, G. J.; Galassi, A.; Ceballos, E. G.; Wighton, P.; Bilgel, M.; Eierud, C.; Gonzalez-Escamilla, G.; Buckholtz, J.; Blair, R.; Markiewicz, C. J.; Hardcastle, N.; Greve, D. N.; Thomas, A. G.; Poldrack, R. A.; Calhoun, V. D.; Innis, R. B.; Knudsen, G. M.

  Abstract

  Molecular neuroimaging with positron emission tomography (PET) and single-photon emission computed tomography (SPECT) enables quantification of specific molecular targets in the living brain. Despite its scientific impact, molecular neuroimaging research has historically faced challenges due to high costs, small sample sizes, laboratory-specific analysis pipelines, and limited large-scale data sharing. These factors have hindered reproducibility and the broader reuse of valuable PET datasets. The OpenNeuroPET initiative was established to address these barriers by developing standards, infrastructure, and open-source tools for organizing, sharing, and analyzing molecular neuroimaging data. Through collaborations across Europe and North America, OpenNeuroPET has supported the PET extension of the Brain Imaging Data Structure (PET-BIDS), providing a standardized framework for PET datasets and metadata. Building on PET-BIDS, tools such as PET2BIDS, ezBIDS, and BIDSCoin facilitate data conversion and curation. In parallel, OpenNeuro now hosts PET-BIDS datasets for open sharing, while complementary platforms such as PublicnEUro enable GDPR-compliant controlled access. Emerging open-source workflows and BIDS applications further support automated, reproducible PET preprocessing and quantitative analysis, promoting harmonized processing across centers. Together, these developments mark an important step toward an open molecular neuroimaging ecosystem in which datasets, software, and workflows can be transparently shared, reused, and scaled for collaborative research.

  bioinformatics2026-05-09v1

• Cross Dataset Transcriptomic Analysis Identifies Oxidative Stress Inflammation Gene Networks Modulated by Nutrigenomic Interventions in Parkinson Disease

  Rafiee, M.; Abaj, F.; Mahdevar, M.; Rashidian, A.; Ghaedi, K.; Ghiasvand, R.

  Abstract

  Inflammation and oxidative stress (OS) are key to Parkinson's disease (PD). We performed a cross-dataset integrative transcriptomic analysis to identify OS and inflammation-related hub genes persistently dysregulated in PD and to evaluate their response to nutrigenomic interventions using publicly available datasets. Four GEO datasets (GSE7621, GSE20141, GSE20146, GSE49036) were analysed to identify differentially expressed genes (DEGs), which were intersected with GeneCards OS inflammation gene sets. Functional enrichment analyses, including gene ontology (GO), pathway over-representation analysis (ORA), and protein-protein interaction (PPI) analysis, were used to identify key pathways and hub genes. Gene food bioactive compound (FBC) association was explored by integrating PD signatures with nutrigenomic profiles from NutriGenomeDB. We identified 183 DEGs in PD, enriched in synaptic, dopaminergic, OS, and inflammatory pathways. Intersection analysis yielded 26 OS-inflammation-related genes and 10 central regulators, including TH, DDC, SNCA, LRRK2, HSPB1, and HSPA1B. revealed opposing transcriptional patterns, with several FBCs suppressing stress related genes and upregulating dopaminergic markers such as TH, GCH1, and DDC. Overall, this integrative analysis highlights OS inflammation gene networks in PD and identifies candidate diet gene interactions that warrant further experimental validation

  bioinformatics2026-05-09v1

• Machine learning cross-platform proteomic imputation enables protein quality scoring and replication of epidemiological associations

  Li, L.; Alaa, A.; Tan, Y.; Demirel, I.; Friedman, S.; Zha, Q.; Trac, R. P.; Taylor, K. D.; Yu, B.; Ballantyne, C. M.; Deo, R.; Dubin, R.; Tsai, M. Y.; Peloso, G. M.; Brody, J.; Austin, T.; Psaty, B. M.; Nicholas, J.; Raffield, L. M.; Tahir, U.; Coresh, J.; Hornsby, W.; Chan, A.; Rich, S. S.; Rotter, J. I.; Ganz, P.; Gerszten, R.; Philippakis, A.; Natarajan, P.; Yu, Z.

  Abstract

  High-throughput affinity-based proteomics has advanced biomedical research, yet fundamental, persistent discordance between mainstream platforms (SomaScan and Olink) routinely undermines the replication of findings. This platform-driven non-replication complicates downstream biological validation and biomarker prioritization. Here, we develop a machine learning-based framework for cross-platform protein value imputation to resolve this translational bottleneck. Using paired proteomic data measured by both SomaScan and Olink from 5,325 participants of the Multi-Ethnic Study of Atherosclerosis, we developed models to impute cross-platform measurements and applied them to two independent and demographically distinct cohorts (Cardiovascular Health Study [N=3,171] and UK Biobank [UKB; N=41,405]) for external validation. Our bi-directional model 1) established an imputation performance-based protein fidelity index, validated against gold-standard measurements from Atherosclerosis Risk in Communities study (N=101) and Nurses' Health Study (N=54), 2) enabled imputation of platform-exclusive protein measurements, and 3) facilitated calibration of overlapping proteins. We demonstrate the utility of this framework through three applications: 1) fidelity-informed analyses enhanced the replication of biomarker discovery, 2) recovery of SomaScan signals that were previously inaccessible in UKB's original Olink measurements, and 3) improved replication performance for overlapping proteins. Our study offers a translational roadmap that allows researchers to achieve reliable epidemiological replication, target specific assays for future optimization, and prioritize biological signal over platform noise.

  bioinformatics2026-05-09v1

• A Fractal-Dimension Framework for Quantifying Self-Similarity in Chromatin Folding

  El-Yaagoubi, A.; Balubaid, A. O.; Chung, M. K.; tegner, j.; Ombao, H.

  Abstract

  The three-dimensional folding of DNA is essential for genome function, but its organization remains difficult to summarize quantitatively across genomic scales. Here, we study DNA folding from Hi-C contact data using a network-based notion of fractal dimension. In this representation, genomic loci are treated as nodes, and observed Hi-C contacts define weighted edges, so that frequently interacting loci are closer in the resulting network. We then estimate fractal dimension using two complementary graph-based methods: the correlation dimension and the sandbox dimension. Validation on synthetic networks shows that the proposed estimators detect clear scaling behavior in hierarchical fractal-like networks, while distinguishing them from networks with local clustering but no stable multiscale self-similarity. Applied to intrachromosomal Hi-C data from the IMR90 human cell line, the method reveals approximate linear scaling regimes on log-log plots, suggesting fractal-like organization in chromatin contact networks. At the chromosome level, estimated fractal dimension tends to increase with chromosome size: larger chromosomes often have dimensions closer to 3, consistent with more compact and space-filling organization, whereas shorter chromosomes tend to have lower dimensions, closer to 1, consistent with simpler and more open folding patterns. A sliding-window analysis at 5 kb resolution further shows that fractal organization varies substantially along chromosomes rather than remaining uniform across genomic position. These results suggest that graph-based fractal dimension provides an interpretable summary of DNA folding complexity at both global and local scales. More broadly, the proposed framework offers a quantitative way to study multiscale genome organization from Hi-C data using tools from network geometry.

  bioinformatics2026-05-09v1

• A structural grammar of truncation across the human homodimer landscape

  Karagöl, T.; Karagöl, A.

  Abstract

  Alternative splicing and proteolytic truncation generate tens of thousands of protein isoforms in the human proteome, but the structural consequences for quaternary state, the level at which most signaling, enzymatic and regulatory function operates, have largely been examined one molecule at a time. Leveraging the recent expansion of the AlphaFold Database to predicted human homodimers, we systematically compared 5,168 canonical-versus-truncated homodimer pairs across the human proteome. In high-confidence canonical homodimers, truncation is associated with predicted structural conservation in 56.4% of pairs (mean 85 residues lost), complete interface ablation in 26.1% (mean 178 residues lost), and partial destabilization in 17.5% (mean 134 residues lost); a distinct fourth class (4.0% of the dataset, n = 208) shows truncation-associated emergence of a predicted high-confidence interface from a sub-threshold canonical baseline. Two reproducible rules govern these transitions: a topological asymmetry in which N-terminal losses are preferentially enriched ~1.6-fold in interface preservation while C-terminal losses are rare overall (~6% of pairs) and modestly under-represented in the conservation class, and a biophysical rule in which emergence-class proteins show substantially elevated intrinsic disorder content relative to ablation-class proteins, as measured by both AlphaFold pLDDT-defined disorder of the canonical structure (Cohen's d {approx} 1.39) and AIUPred peak binding propensity of the truncated isoform (Cohen's d {approx} 0.65). Formal pathway enrichment recovered only a small nucleotide-metabolism signal, indicating that these rules operate across diverse gene-functional categories. Truncation-associated remodeling of homodimer architecture thus constitutes a structural grammar of the human proteome rather than a specialty of any single regulatory family.

  bioinformatics2026-05-09v1

• A novel phylogenomics pipeline reveals extensive topological conflict in the evolution of the angiosperm order Cucurbitales

  Ortiz, E. M.; Hoewener, A.; Shigita, G.; Raza, M.; Maurin, O.; Zuntini, A.; Forest, F.; Baker, W. J.; Schaefer, H.

  Abstract

  High-throughput sequencing data, such as target capture, RNA-Seq, genome skimming, and high-depth whole genome sequencing, are used for phylogenomic analyses. Integrating these mixed data types into a single phylogenomic dataset requires several bioinformatic tools and significant computational resources. Here, we present Captus, a novel pipeline to analyze mixed data efficiently. Captus assembles these data types, searches for loci of interest, and produces paralog-filtered alignments. If reference target loci are not available for the studied taxon, Captus can also be used to discover new putative homologs via sequence clustering. Compared to other software, Captus allows the recovery of a greater number of more complete loci across more species. We apply Captus to assemble a comprehensive dataset, comprising the four types of sequencing data for the angiosperm order Cucurbitales, a clade of about 3,100 species in eight mainly tropical plant families, including begonias (Begoniaceae) and gourds (Cucurbitaceae). Our phylogenomic results support the currently accepted circumscription of Cucurbitales except for the position of the holoparasitic Apodanthaceae, which group with Rafflesiaceae in Malpighiales. A subset of mitochondrial gene regions supports the earlier divergence of Apodanthaceae in Cucurbitales. However, the nuclear regions and majority of mitochondrial regions place Apodanthaceae in Malpighiales. Within Cucurbitaceae, we confirm the monophyly of all currently accepted tribes but also reveal hybridization and incomplete lineage sorting both in Cucurbitales and within Cucurbitaceae. We show that contradicting results among earlier phylogenetic studies in Cucurbitales can be reconciled when accounting for gene tree conflict and demonstrate the efficiency of Captus for complex datasets.

  bioinformatics2026-05-08v4

• Simple baselines rival protein language models in mutation-dense design of function tasks

  Talpir, I.; Fleishman, S. J.

  Abstract

  Computational protein design demands generally applicable models that reliably predict or generate unmeasured variants with superior functional properties. Although protein language models (pLMs) have been used in zero-shot and transfer-learning design studies, they have generally not been assessed in benchmarks that explicitly test combinatorial extrapolation from lower- to higher-order variants. Here we benchmark widely used pLMs against conventional baseline methods in recently described dense, experimentally validated multi-mutant landscapes. We find that regardless of architecture and parameter count, pLMs are statistically similar to one another, and none consistently outperforms conventional baseline methods. Furthermore, their ability to distinguish functional from non-functional variants in zero-shot prediction is comparable to that of conventional homology-based methods. We suggest that to contribute significantly to the design of protein function, pLMs may need to encode biophysical and structural priors or be combined with structure-based approaches.

  bioinformatics2026-05-08v2

• RiboPipe: efficient per-transcript codon-resolution ribo-seq coverage imputation for low-coverage transcripts

  Zhang, Y.-z.; Hashimoto, S.; Li, S.; Inada, T.; Imoto, S.

  Abstract

  Motivation: Ribosome profiling (Ribo-seq) provides codon-resolution measurements of translation; however, many transcripts exhibit sparse or low read coverage, which limits downstream quantitative analyses. Reliable prediction and imputation of codon-resolution coverage for low-coverage transcripts remain computationally challenging. Results: We present RiboPipe, an efficient framework for per-transcript codon-resolution Ribo-seq coverage imputation for low-coverage transcripts. RiboPipe is designed around three key principles. First, it jointly optimizes transcript-level mean ribosome load (MRL) prediction and codon-level coverage modeling within a unified objective, enabling consistent learning across both local and transcript-level scales. Second, it introduces a peak-weighted loss that emphasizes high-signal codon positions associated with translational pausing, improving the recovery of functionally relevant coverage peaks. Third, the framework is lightweight and data-efficient, achieving stable performance even when trained on only a small fraction of high-coverage transcripts. Using two publicly available Ribo-seq datasets (GSE233886 and GSE133393), we demonstrate stable convergence and consistent prediction accuracy across multiple train-test split ratios. Comparative evaluation of embedding strategies shows that simple one-hot representations achieve competitive or even superior performance compared with pre-trained language model embeddings under identical training conditions. Overall, RiboPipe provides a computationally efficient and scalable framework for Ribo-seq coverage imputation in low-coverage transcripts. Availability and Implementation: The source code and associated data can be accessed at https://github.com/yaozhong/riboPipe

  bioinformatics2026-05-08v2

• SPPIDER-seq: Sequence-based partner-aware predictor of protein-protein interaction sites

  Porollo, A.; Jadhav, O.; Alvarez, A.; Chen, J.

  Abstract

  Motivation: Sequence-based protein-protein interaction (PPI) site predictors typically analyze proteins in isolation, neglecting partner-specific context that is critical for interface specificity, particularly in transient and disordered interactions. Results: We introduce SPPIDER-seq, a partner-aware PPI site prediction framework that combines pretrained ESM-2 embeddings with a cross-attention architecture to enable residue-level conditioning on interacting partners. We curated non-redundant protein-peptide interaction datasets from BioLiP and used them to train and benchmark two complementary models: a receptor-centric model optimized for structured interfaces and a peptide-centric model tailored to disordered, motif-driven binding. On blind benchmarks, SPPIDER-seq achieved AUROC values up to 0.797 and MCC values up to 0.269, outperforming AlphaFold3 on peptide-mediated and disordered interfaces while remaining complementary on globular complexes. Application to 341 TP53 interaction partners revealed coherent, partner-specific interface patterns across both structured and intrinsically disordered regions. Availability and Implementation: SPPIDER-seq models, datasets, and the Python code are freely available on the web at: https://github.com/aporollo-lab/SPPIDER-seq

  bioinformatics2026-05-08v2

• Sample-level modeling of single-cell data at scale with tinydenseR

  Milanez-Almeida, P.; Schildknecht, D.; Linder, M.; Brachmann, S. M.; Weiss, A.; Adler, F.; Lenticchia, S. C.; Meistertzheim, M.; Wild, S.; Cuttat, R.; Jayaraman, P.; Lee, L. H.; Mulvey, T.; Hassounah, N.; Crafts, G.; Quinn, D. S.; Orlando, E. J.

  Abstract

  Single-cell studies now routinely encompass hundreds of samples and millions of cells, offering unprecedented opportunities to link sample-level phenotypes with cellular and molecular states. However, current workflows often depend on cell-level inference and rigid clustering, which can distort significance and obscure subtle, continuous variation, in particular for complex experimental designs. Here, we present tinydenseR, a clustering-independent framework that enables robust, scalable, and statistically sensitive detection of differential cell states, outperforming existing workflows in speed, memory usage, and biological resolution. Technology-agnostic at its core, tinydenseR works seamlessly on scRNA-seq, flow, mass and spectral cytometry. Across synthetic benchmarks, a preclinical xenograft model, two immuno-oncology trials and a multi-study atlas, tinydenseR uncovers disease and treatment history-associated effects, including subtle within-cluster heterogeneity. Designed to accelerate discovery in clinical, preclinical, and translational research, the open-source package is available at GitHub.com/Novartis/tinydenseR.

  bioinformatics2026-05-08v2

• scStudio: A User-Friendly Web Application Empowering Non-Computational Users with Intuitive scRNA-seq Data Analysis

  Bica, M.; Serre, K.; Barbosa-Morais, N. L.

  Abstract

  Background Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity by providing detailed insights into gene expression at the individual cell level. Despite its potential, the complexity of scRNA-seq data analysis often poses challenges for researchers without computational expertise. Findings To address this, we developed scStudio, a user-friendly, comprehensive, and modular web-based application designed to democratize scRNA-seq data analysis. scStudio is equipped with a suite of features designed to streamline data retrieval and analysis with both flexibility and ease, including automated dataset retrieval from the Gene Expression Omnibus. Users can also upload their own datasets in a variety of formats, integrate multiple datasets, and tailor their analyses using a wide range of flexible methods with options for parameter optimization. The application supports all the essential steps required for scRNA-seq data analysis, including in-depth quality control, normalization, dimensionality reduction, clustering, differential expression, and functional enrichment analysis. scStudio also tracks the history of analyses, supports session data storage and export, and facilitates collaboration through data sharing features. Conclusion By developing scStudio as a user-friendly interface and scalable architecture, we address the evolving needs of scRNA-seq research, making advanced data analysis accessible and manageable while accommodating future developments in the field. scStudio is freely available at https://compbio.imm.medicina.ulisboa.pt/app/scStudio.

  bioinformatics2026-05-08v2

• Predicting Enzyme pH Optima from Structure Using Equivariant Graph Neural Networks

  SinhaRoy, R.; Clauss, C.; Ivanikov, I.; Kuenze, G.

  Abstract

  Enzyme activity and stability are strongly modulated by pH, making the catalytic pH optimum (pH opt) a key parameter in enzyme development and biotechnological applications. Experimental determination of pH opt is, however, labor-intensive and time-consuming, motivating the development of accurate computational prediction methods. Here, we introduce pHoptNN, an E (n)-equivariant graph neural network designed to predict enzyme pH opt directly from three-dimensional protein structures. pHoptNN was trained on a curated dataset comprising nearly 12,000 enzymes with experimentally determined pH opt values and high-confidence structural models obtained from the Protein Data Bank and AlphaFold3. The model represents enzymes as atomic-level molecular graphs, integrating structural, chemical, and electrostatic features. Model development was guided by extensive hyperparameter optimization using genetic and Bayesian search strategies. On a held-out test set, pHoptNN achieved a root-mean-square error (RMSE) of 0.588 pH units, substantially outperforming the sequence-based method EpHod (RMSE = 0.879). Moreover, pHoptNN maintains robust predictive performance across different enzyme classes and pH ranges. These results demonstrate the utility of structure-based equivariant deep learning for enzyme pH opt prediction and highlight the potential of pHoptNN to accelerate enzyme discovery and engineering workflows.

  bioinformatics2026-05-08v2

• A Differentiable dFBA Simulator for Scalable Bayesian Inference over Microbial Metabolic Models

  Diederen, T.; Merzbacher, C.; Patz, M.

  Abstract

  Medium optimisation for bioprocess design remains challenging and costly: fermentation recipes typically contain ten or more components, the design space expands combinatorially as ingredients are added, and each batch experiment requires over 24 hours. High-throughput 96-well plate screening can reduce experimental cost, but extracting actionable predictions from growth curves requires a mechanistic model that links medium composition to cellular metabolism. In this paper, we present a differentiable simulator for dynamic flux balance analysis (dFBA) that enables scalable Bayesian inference over microbial metabolic models. A distinguishing feature is that inference is driven entirely by OD600 measurements, a simple optical proxy for biomass, without substrate or product assays; internal fluxes, substrate consumption, and secreted metabolite profiles are recovered as latent variables constrained by the metabolic network stoichiometry. We resolve the core differentiability barrier of classical dFBA by reformulating the per-step linear or quadratic programme (LP/QP) as a smooth continuous ODE (the Relaxed Interior-Point ODE, R-iODE), establishing the mathematical framework for end-to-end gradient propagation through long fermentation trajectories in JAX; full gradient validation is ongoing. The result is a framework for principled inference over thousands of batch fermentations, providing a path toward model-guided medium design, cross-strain parameter transfer, and scale-up prediction from plate data.

  bioinformatics2026-05-08v1

• TopoFuseNet: Hierarchical Graph Representation Learning with Multi-Scale Topological Features for Accurate Drug Synergy Prediction

  Wang, Q.; Shi, x.

  Abstract

  Accurate prediction of drug synergy is paramount for developing effective combination therapies and advancing personalized medicine. Although methods based on graph neural networks (GNNs) have become a prevalent approach, they often treat molecules as flat graphs of connected atoms, thus overlooking their inherent hierarchical structure (i.e., atoms forming functional groups) and the critical topological information that governs molecular interactions. To address this limitation, we introduce TopoFuseNet, a novel hierarchical graph representation learning framework that integrates multi-scale topological features. The core innovations of TopoFuseNet include: 1) The first-ever application of "Group Centrality" from network science to cheminformatics, enabling the identification and quantification of functional groups crucial to drug activity; 2) A systematic, multi-path strategy to seamlessly integrate node-level (atom) and group-level (functional group) topological features into a Graph Attention Network (GAT) via feature augmentation, attention biasing, and hierarchical pooling; 3) A Differential Transformer module to deeply fuse multi-modal features learned from sequences, fingerprints, and our proposed hierarchical graph representations.

  bioinformatics2026-05-08v1

• RAPID: an interactive R/Shiny platform for end-to-end 16S rRNA and ITS amplicon sequence analysis using DADA2

  Kapoor, B.; Cregger, M. A.; Ranjan, P.

  Abstract

  Abstract Motivation: Amplicon sequencing of 16S rRNA and internal transcribed spacer (ITS) gene regions is the most widely used approach for characterizing bacterial and fungal communities, respectively. The DADA2 pipeline has become a standard for inferring amplicon sequence variants (ASVs), offering single-nucleotide resolution over traditional OTU clustering. However, executing the full DADA2 workflow requires proficiency in R programming and manual coordination of multiple sequential steps, presenting a substantial barrier for researchers in clinical, environmental, and agricultural sciences who lack computational training. Results: We present RAPID (R-based Amplicon Pipeline for Interactive DADA2), a pair of R/Shiny applications providing complete graphical user interfaces for 16S rRNA and ITS amplicon sequence analysis. The 16S application implements a 10-step guided workflow from raw paired-end FASTQ files through quality filtering, error learning, dereplication, paired-read merging, chimera removal, taxonomy assignment (SILVA), phyloseq construction with data transformation (rarefaction, relative abundance, or CLR), interactive visualization (rarefaction curves, alpha diversity, NMDS, PCoA, taxonomic abundance), PERMANOVA, and ANCOM-BC2 differential abundance analysis. The ITS application extends this to an 11-step workflow, adding an automated primer removal step using cutadapt with support for multiple primers and length-variable amplicons, and uses the UNITE database for fungal taxonomy. Both applications feature asynchronous background processing, session persistence, real-time progress monitoring, publication-ready figure export, and comprehensive result downloads. Availability: RAPID is freely available at https://github.com/beantkapoor786/RAPID. Both applications can be installed locally on any system with R (version 4.0 or higher) and run as local web applications accessible through a standard browser. Keywords: 16S rRNA, ITS, amplicon sequencing, DADA2, microbiome, mycobiome, graphical user interface, Shiny, phyloseq, ASV, PERMANOVA, ANCOM-BC2

  bioinformatics2026-05-08v1

• BART-spatial unravels biologically significant transcriptional regulators from spatial omics data

  Wang, J.; Zhang, H.; Wang, Z.; Zang, C.

  Abstract

  Transcriptional regulators (TRs) are crucial regulators of cell fate decisions by activating or repressing lineage-specific genes and integrating environmental signals with intrinsic networks. Identifying functional TRs is essential for understanding development, tissue organization, and disease. Emerging spatial transcriptomics and epigenomics technologies now provide near-single-cell resolution mapping of genomic features while preserving information of each cell's physical location and microenvironment which influence TR activity. Despite these advances, identifying active TRs in spatial data remains challenging due to low TR expression and the fact that TR activity often does not correlate directly with mRNA levels. Moreover, existing tools mainly designed for non-spatial single-cell data overlook spatial heterogeneity. To bridge this gap, we developed BART-spatial (Binding Analysis for Regulation of Prediction for spatial omics), an innovative computational method to infer functional TRs from spatial omics data. BART-spatial integrates spatial variability and pseudo-temporal information with publicly available TR binding profiles. Applied to multiple spatial datasets from diverse platforms, including 10X Visium, Visium HD, Atera, and spatial RNA-ATAC-seq, BART-spatial consistently outperforms existing methods, identifying stage-specific TRs and revealing regulators undetectable by expression alone. Its compatibility with spatial epigenomics data further strengthens its utility and enables cross-validation. Overall, BART-spatial provides a powerful and robust tool for decoding spatially resolved gene regulatory programs.

  bioinformatics2026-05-08v1

• QuadStack: Specialized convolutional blocks enable in vivo BG4-binding motif prediction and highlight discrepancies with in vitro G-quadruplexes.

  Ulas, P. N.; Doluca, O.

  Abstract

  G-quadruplex (G4) prediction has been largely guided by in vitro biophysical rules, yet these models show limited agreement with in vivo measurements. Here, we present QuadStack, a deep learning model trained on a multi study BG4-ChIP-seq compendium. QuadStack introduces two biologically grounded convolutional modules-G4Stack Convolution, which captures G/C stacking patterns, and Reverse Complement Convolution, which enforces strand invariant representations consistent with ChIP-seq signals. QuadStack achieves strong predictive performance (AUC up to 0.94) and substantially outperforms widely used in vitro-based predictors on genomic test data. Beyond performance, our analyses reveal that BG4-associated sequence grammar is not solely governed by canonical isolated G-rich tracts, but also by patterns where G and C nucleotides are mixed. This suggests that cytosines are not simply disruptive in vivo, and raises the possibility that cytosines may play a context-dependent role or that guanines on the opposite strand contribute to the structure, which could explain the difference between in vivo and in vitro observations. Together these findings demonstrate a fundamental discrepancy between in vitro folding propensity and in vivo G4 biology, and establish QuadStack as both a predictive model and a framework for interpreting G4 formation in its native genomic context.

  bioinformatics2026-05-08v1

• Denoised MDS-UPDRS Part-III Scores Yield New Patterns of Progression Heterogeneity in Early Stage Parkinson's Disease

  Koss, J.; Tinaz, S.; Tagare, H.

  Abstract

  Parkinson's Disease (PD) Motor Scores (MDS-UPDRS Part III) are quite noisy. This paper proposes a new methodology for processing these scores by first denoising the scores to enhance the underlying progression signal, and then conducting a high-dimensional analysis which does not sum the scores into a total movement score. The analysis gives novel insights into PD progression heterogeneity: it reveals that the heterogeneity is continuously variable rather than clustered into "subtypes" and that the variability is along two easily understood axes. This analysis also resolves some of the discrepancies in previously reported progression subtypes. Finally, the analysis reveals that patient-specific progression cannot be predicted from baseline using only MDS-UPDRS Part III scores.

  bioinformatics2026-05-08v1

• Allosteric Protein Chemical Shift Perturbations are Ubiquitous

  Benavides, T. L.; Ramelot, T. A.; Montelione, G. T.

  Abstract

  While allosteric protein function has been appreciated for decades, the ubiquity of conformational shifts, particularly those distant from the interaction interface, has not been broadly characterized. For example, ligand binding frequently triggers allosteric effects far from the interaction interface, yet the prevalence of these conformational shifts underpinning protein function remain poorly documented. We systematically assessed the generality of allosteric effects as monitored by NMR Chemical Shift Perturbations (CSPs) distant from the interaction interface. In a set of 139 protein-protein complexes, a striking 74% of all significant CSPs are non-local to the binding site. Notably, more than 35% of significant CSPs outside the binding site occur in residues for which the shortest receptor-ligand interatomic distance is more than 10 [A]. Every protein analyzed exhibits a significant fraction (> 8%) of CSPs distant from the binding site. This analysis across a large number of protein structures demonstrates and documents that structural plasticity is a ubiquitous and fundamental property of proteins.

  bioinformatics2026-05-08v1

• LongAllele: a joint inference framework for allele-specific analysis on long-read bulk and single-cell RNA sequencing

  Xu, Z.; Wang, K.

  Abstract

  Allele-specific analysis from RNA-seq is a powerful approach to characterize cis-regulatory effects. However, existing methods remain limited in both haplotype inference and allelic testing. Their haplotype-inference workflows separate variant calling, haplotype phasing, and read-haplotype assignment into sequential steps, failing to fully exploit within-read SNV linkage information and propagating errors into downstream allelic analysis. At the testing stage, they ignore non-phasable reads lacking heterozygous SNVs, biasing calls and inflating false positives, and remain incomplete across gene-, isoform-, and local-event-level variant effects. Here, we present LongAllele, a statistical framework that employs an expectation-maximization algorithm to jointly infer heterozygous variants, haplotype structure, and read-haplotype assignments from long-read bulk and single-cell RNA sequencing. LongAllele further introduces phasability-aware testing that explicitly accounts for non-phasable reads, avoiding inflated false-positive calls when haplotype information is incomplete. It also enables comprehensive allelic testing across gene-level ASE, isoform-level allele-specific transcript usage (ASTU), and local-event-level haplotype-associated exon and junction usage (HAEU and HAJU), providing a multi-scale view of cis-regulation. We applied LongAllele to long-read RNA-seq datasets spanning GTEx (multi-tissue bulk), peripheral blood mononuclear cells (single-cell), and human hippocampus (single-nucleus). LongAllele consistently revealed greater tissue and cell-type variability in expression-level than isoform-level allelic regulation, pinpointed high-impact regulatory variants including rare splice-site mutations missed by standalone variant callers, and showed that purifying selection constrains allelic imbalance at both gene and isoform levels. LongAllele offers a unified framework for haplotype-resolved cis-regulatory analysis across diverse cellular contexts.

  bioinformatics2026-05-08v1

• Efficient Stochastic Trace Generation for Transcription

  Ferdowsi, A.; Fuegger, M.; Nowak, T.

  Abstract

  Bursty transcription in single cells typically produces over-dispersed, skewed, and sometimes heavy-tailed expression distributions that are explained by two-state Markov models of the promoters. While the gold standard for simulation is exact stochastic sampling with Gillespie's algorithm, obtaining thousands of timed traces is computationally costly. Surrogate models based on stochastic differential equations (SDEs) are widely used to speed up this simulation process. An example is the Chemical Langevin Equation based on Gaussian noise, which, however, does not capture heavy-tailed noise. In this work, we present a unified SDE framework that combines deterministic drift, Gaussian fluctuations, and additive sporadic jumps of arbitrary distributions, and provide an open-source Python implementation, bcrnnoise. The framework subsumes standard surrogate models and allows for vectorized generation of batches of transcription traces. We assess computational speed and accuracy of common surrogate models along with new models, showing that high accuracy can be obtained while reducing computational cost up to two orders of magnitude.

  bioinformatics2026-05-08v1

• SaVanache: indexing and visualizing pangenome variation graphs

  Mohamed, M.; Durant, E.; Rouard, M.; Muller, C.; Monat, C.; Conte, M.; Sabot, F.

  Abstract

  With the rapid increase in genome sequencing and the growing availability of genomic resources, genomics is shifting toward pangenome representations that capture intra- and inter-specific diversity by integrating multiple genomes into a single entity. These pangenomes are increasingly modeled as graphs, encoding complex genomic variations in structures such as de Bruijn or variation graphs. However, while genome browsers provide standard and effective solutions for visualizing single or limited numbers of genomes, equivalent interactive tools for graph-based pangenomes remain limited, particularly for variation graph models. We developed SaVanache, a multi-resolution visualization interface designed to explore pangenome variation graphs at various depths. SaVanache enables the exploration of both global diversity and structural variations (SVs) across genomes relative to a user-defined linear pivot genome. Unlike synteny viewers, SaVanache emphasizes variations by representing SV types through a dedicated set of glyphs, facilitating intuitive one-to-many comparisons. To support smooth exploration, SaVanache preprocesses a Graphical Fragment Assembly (GFA) pangenome file into optimized index and data structures, enabling fast, real-time queries on large pangenome graphs. By combining advanced visualization techniques with efficient data handling, SaVanache provides a robust tool for scientists to analyze and visualize genetic variation within genomes and pangenomes, facilitating the identification of genetic determinants associated with phenotypes of interest and fully exploiting current genomic resources.

  bioinformatics2026-05-08v1

• Metastatic Site Prediction in Breast Cancer using Kirchhoff's Law and Omics Knowledge Graph

  Jha, A.; Khan, Y.; Sahay, R.; d'Aquin, M.

  Abstract

  Predicting the anatomical site of metastasis from a primary tumour remains an unsolved problem in breast cancer (BRCA) and metastatic disease more broadly. The difficulty is structural: metastatic biology is multi-site (bone, lung, liver, brain), multi-omics (genomics, proteomics, methylomics, drug response), and multi-modal (CNV, gene expression, DNA methylation, pathways, clinical associations). Existing classifiers either collapse this heterogeneity into a single feature vector or rely on a single omics layer, both of which discard the mechanistic structure that drives metastatic tropism. We introduce Kirchhoff Knowledge Graphs (K-KG), a framework that imports the conservation laws of electrical-circuit theory into knowledge graph reasoning. Our contributions are: (1) a layered RDF Cancer Decision Network integrating 36 polyomics datasets across mutations, pathways, drugs, diseases, and reactions; (2) two novel conservation laws - the Knowledge-Graph Voltage Law (KGVL) and Knowledge-Graph Current Law (KGCL) - that govern information flow during traversal and yield a principled measure of graph completeness; (3) topological motif mining on the conserved graph, replacing expression-based feature selection by identifying triangular sub-structures whose rewiring marks metastatic transition; (4) a Graph Convolutional Neural Network whose hidden layers are the omics layers themselves, predicting site-specific metastasis as a continuous percentage rather than a binary label. On TCGA-BRCA training plus one validation and four independent test cohorts from GEO, K-KG achieves 83.8% AUC for relapse prediction and up to 0.87 AUC / 0.91 F1 for brain-site-specific prediction, outperforming Random Forest, Neural Network, and SVM baselines by 8-20 AUC points. To our knowledge this is the first application of Kirchhoff's laws (1845, 1847) to graph-based machine learning, and the first metastasis predictor that returns a per-site contribution profile rather than a single label.

  bioinformatics2026-05-07v3

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

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

  Abstract

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

  bioinformatics2026-05-07v3

• STAT: A multi-agent framework for integrated and interactive spatial transcriptomics analysis

  Chen, Y.; Han, S.; Chao, Z.; Liu, Y.; Zhang, F.; Chen, H.; Wang, J.; Xiao, J.; Yang, C.

  Abstract

  Spatial transcriptomics analysis often involves a myriad of computational methods across diverse platforms, leading analysts to spend excessive time on data assembly rather than deriving biological insights. Current AI solutions tend to either oversimplify spatial data into generic single-cell tables or operate autonomously without opportunities for intermediate review, thus hindering the visual and iterative analyses essential for spatial biology. In response to these challenges, we introduce STAT, a multi-agent framework, designed to make spatial analysis more conversational and user-friendly while maintaining transparency and control. STAT integrates a persistent session, a shared interactive tissue viewer, and a staged skill-aware pipeline, enabling a more intuitive analytical experience. In a comprehensive benchmark evaluation encompassing eleven analytical task categories across three spatial platforms and both cell- and spot-resolution data, STAT demonstrated superior performance compared to a baseline large language model and existing autonomous spatial analysis agents, excelling in task completion, analytical quality, and token efficiency. Notably, STAT enables multi-task spatial analysis of a mixed-resolution breast cancer cohort, successfully reproducing key findings from a published Visium HD colorectal cancer study based solely on natural language prompts. STAT thus facilitates trustworthy and scientifically rigorous spatial transcriptomics analysis, allowing researchers to focus more on biological interpretation.

  bioinformatics2026-05-07v2

• Better antibodies engineered with a GLIMPSE of human data

  Hepler, N. L.; Hill, A. J.; Jaffe, D. B.; Gibbons, M. C.; Pfeiffer, K. A.; Hilton, D. M.; Freeman, M.; McDonnell, W. J.

  Abstract

  GLIMPSE-1 is a protein language model trained solely on paired human antibody sequences. It captures immunological features and achieves best-in-class performance in humanization benchmarks. We demonstrate the utility of GLIMPSE-1 in humanization; engineering of antibodies for affinity, species cross-reactivity, and key developability parameters; and the creation of highly divergent functional variants with <90% sequence identity to a marketed antibody. Learning exclusively from human antibody data enables GLIMPSE-1 to enhance therapeutics and native antibodies based on patterns in the human repertoire.

  bioinformatics2026-05-07v2

• immuneKG: An Immune-Cell-Aware Knowledge Graph Framework for Target Discovery in Immune-Mediated Diseases

  Ye, Y.; PB-IDD Department, Pharmablock Sciences Inc.,

  Abstract

  Biomedical knowledge graphs have emerged as foundational infrastructure for AI-driven drug discovery, yet their translational impact on novel target identification in immune-mediated diseases remains limited. Here we present immuneKG, a multimodal knowledge graph centred on autoimmune diseases, constructed through biologically meaningful feature reprogramming of disease nodes to enable deep mechanistic modelling of immune-related disorders. immuneKG introduces a new entity class immune_cell, and four original directed relation types, together adding 9,105 novel triples absent from all existing biomedical KG schemas. Disease nodes are endowed with three novel modal feature sets quantifying immune homeostatic imbalance: autoantibody profiles, cytokine signatures, and HLA genotypes, complemented by systemic involvement scores and genetic features. The graph encompasses over 407,000 training triples across 7,287 entities and 32 relation types. Applied to inflammatory bowel disease (IBD), immuneKG combined with a HeteroPNA-Attn graph neural network achieves a Hits@100 of 0.99 against a Clarivate Phase II+ clinical pipeline, while a novelty-penalised scoring function surfaces high-potential dark targets. The framework shifts from conventional candidate-space screening to a development-oriented decision-support paradigm, providing actionable and interpretable guidance for downstream drug discovery. The immuneKG project is publicly available now on GitHub at https://github.com/YaowenYe/immuneKG.

  bioinformatics2026-05-07v2

• Scalable subclonal reconstruction of cancer cells in DNA sequencing data using a penalized likelihood model

  Jiang, Y.; Montierth, M. D.; Ding, Y.; Yu, K.; Tran, Q.; Wu, A.; Li, R.; Ji, S.; Liu, X.; Shin, S. J.; Cao, S.; Tang, Y.; Lesluyes, T.; Kimmel, M.; Wang, J. R.; Tarabichi, M.; Zhu, H.; Van Loo, P.; Wang, W.

  Abstract

  Tumor subclonal architecture shapes cancer evolution, yet subclonal reconstruction from bulk sequencing remains difficult to scale due to computational cost and model complexity. We present CliPP, a penalized-likelihood framework that jointly estimates cellular prevalence with pairwise fusion penalties, automatically identifying subclones without requiring extensive priors. Across simulations and 2,778 whole-genome tumors with external consensus reconstructions, CliPP achieves consistently good performances when compared to state-of-the-art approaches while providing substantial runtime reductions. Applied to 7,000+ tumors across >30 cancer types, CliPP quantifies pervasive subclonality and delineates cohort-level subclone landscapes. CliPP enables fast, reproducible large-scale subclonal analysis and is freely available to the community through GitHub and a shiny app.

  bioinformatics2026-05-07v2

• Pan-cancer virtual spatial transcriptomics from routine histology with Phoenix

  Tran, M.; Gindra, R. H.; Putze, P.; Senbai, K.; Palla, G.; Kos, T.; Falcomata, C.; Wang, C.; Guo, R.; Boxberg, M.; Berclaz, L. M.; Lindner, L. H.; Bergmayr, L.; Knoesel, T.; Jurmeister, P.; Klauschen, F.; Homicsko, K.; Gottardo, R.; Eckstein, M.; Matek, C.; Mock, A.; Theis, F. J.; Saur, D.; Peng, T.

  Abstract

  Spatial transcriptomics links gene expression to tissue architecture, providing a mechanistic view of cellular organization. Yet existing datasets cover few donors and miss the complexity of human disease. Experimental costs remain prohibitive, and large-scale profiling is impractically slow for population-level studies. Accurate computational methods are urgently needed. Predicting gene expression from standard histology, however, remains an open problem, as current approaches transfer poorly to unseen cohorts and diseases. Here, we present Phoenix, a latent flow matching generative model that infers pan-cancer spatially resolved single-cell gene expression with high accuracy. Phoenix analyzes treatment response in silico: Applied to 763 head and neck cancer patients, it identified three new spatial biomarkers that we validated across two cancers (breast cancer, n = 84; ovarian cancer, n = 157) and treatment regimens (platinum, trastuzumab). Phoenix generalizes beyond carcinomas: In a large sarcoma cohort (802 tissue microarray cores), it accurately predicted cell-type-specific signatures in held-out samples and captured chemotherapy-induced immune remodeling. Phoenix also extends across species: In a mouse model, it accurately predicted the expression of pancreatic cancer lineage markers and the mutant mKrasG12D allele in silico. Together, Phoenix establishes virtual spatial transcriptomics from routine histology as a scalable framework for studying tissue organization, therapeutic response, and disease mechanisms.

  bioinformatics2026-05-07v2

• SLiMNet: a deep learning model to detect short linear motifs using protein large language model representations and paired inputs

  McFee, M. C.; Kim, P. M.

  Abstract

  Short linear motifs (SLiMs) are short (3-15 amino acids in length) segments within intrinsically disordered regions (IDRs) that mediate transient protein-protein interactions as well as other functions such as stability and subcellular localization. Only a few thousand out of likely hundreds of thousands have been experimentally validated. SLiMs can be detected as conserved regions inside of IDRs using local alignments, though current approaches have limited sensitivity and specificity and are unable to functionally annotate their hits. Assigning function is hence a major outstanding issue in SLiM biology. Here we present SLiMNet, a deep learning model inspired by siamese networks and contrastive learning that predicts functional similarity in pairs of SLiMs. SLiMNet uses uses protein large language model embeddings and is trained on annotated sets of SLiMS. We show that it detects shared function in unseen, non-redundant motif pairs, and its scores correlate with experimental binding strengths from deep mutational scanning of cyclin-binding motifs. Using SLiMNet we provide repositories of putative SLiM pairs derived from annotated IDR regions for to help with hypothesis generation for the functional annotation of SLiMs. This includes an atlas generated from all-by-all scoring 16-mers from tiled IDRs from the DisProt database. We show that it captures a new nuclear localization motif recently added to MoMaP and a PRMT1 methylation motif in the literature. We also provided a repository of all IDRs scored with SLiMNet against against all MoMaP instances, and an atlas of potential functional pairs for 256 known orphan motifs (motifs with only a single known instance with essential function). Collectively, these atlases are useful resources for the SLiM biology community

  bioinformatics2026-05-07v1

• scLASER: a robust framework for simulating and detecting time-dependent single-cell dynamics in longitudinal studies

  Vanderlinden, L. A.; Vargas, J.; Inamo, J.; Young, J.; Wang, C.; Zhang, F.

  Abstract

  Longitudinal single-cell clinical studies enable tracking within-individual cellular dynamics, but methods for modeling temporal phenotypic changes and estimating power remain limited. We present scLASER, a framework detecting time-dependent cellular neighborhood dynamics and simulating longitudinal single-cell datasets for power estimation. Across benchmark experiments, scLASER shows consistently higher sensitivity than traditional cluster--based approaches, with particularly pronounced gains in rare cell types and non-linear temporal patterns. Applications to inflammatory bowel disease (95,813 cells, 38 patients) reveal treatment-responsive NOTCH3+ stromal trajectories with high cell type discrimination (AUC > 0.92), while analysis of COVID-19 data (188,181 cells, 84 patients) identifies three distinct axes of T cell activity (cytotoxic effector, NK immunoreceptor signaling, and interferon-stimulated gene programs) over disease progression. scLASER enables robust longitudinal single-cell analysis and optimization of study design.

  bioinformatics2026-05-07v1

• A lightweight codon-based DNA Transformer for Regulatory Region Identification in the Genome

  Karthik, A. S. P.; Das, A. B.

  Abstract

  We developed a lightweight codon-based DNA Transformer equipped with multi-head self-attention and an adaptive classifier head, which achieves exon intron classification with high accuracy and also has moderate accuracy in CDS classification and splice site recognition. We named this model as ExIT (Exon-Intron Transformer). We have implemented codon tokenization for this model. This has been validated on the human genome with external validation from the chimpanzee genome. Further benchmarking has implied that our model is better than the existing models in the above tasks.

  bioinformatics2026-05-07v1

• Bridging genomes and peptidomes: hybrid sequencing reveals conserved bioactive peptides in crustaceans

  Fields, L.; Qin, J.; Ibarra, A. E.; Selby, K. G.; Gao, T.; Dang, T. C.; Lu, H.; Li, L.

  Abstract

  Endogenous peptides are critical regulators of signaling and immunity but remain difficult to characterize in organisms with incomplete genomic annotation. We developed a hybrid discovery platform that integrates transformer-based de novo sequencing (Casanovo), neuropeptide-focused database searching (EndoGenius), and empirical false discovery rate estimation via NovoBoard. This pipeline enables confident identification of endogenous peptides while expanding coverage beyond conventional database-only or de novo-only approaches. Applied to neuroendocrine tissues from Callinectes sapidus and Cancer borealis, the workflow revealed numerous high-abundance novel peptides and provided structural and genomic support for their biological relevance. Notably, we report the first histone-2A-derived antimicrobial peptide in the C. sapidus and characterize naturally occurring sequence variants. We also identified unexpected peptide homologies between crustaceans and Rattus norvegicus, enabling annotation of conserved housekeeping proteins in sparsely annotated genomes. This hybrid platform establishes a scalable, open-source strategy for advancing neuropeptidomics and endogenous peptide discovery in emerging model organisms.

  bioinformatics2026-05-07v1

• ORBIT: Orthogonal Rotation for Biological Inter-species Transfer

  Wissenberg, P.; Lee, J. M.; Mutwil, M.

  Abstract

  Motivation. Cross-species gene embeddings are central to transferring functional annotations between species. A recent method demonstrated that species-specific STRING (PPI) network embeddings can be aligned across 1322 eukaryotes with autoencoders (FedCoder), but this approach is computationally expensive, depends on careful hyperparameter selection, leaves substantial room for improvement in cross-species retrieval quality, and has not been demonstrated on coexpression networks. Results. We introduce an alignment pipeline for cross-species coexpression network embeddings based on orthogonal Procrustes rotation. Species-specific Node2Vec embeddings of coexpression networks are aligned to a shared space using ortholog anchors from OrthoFinder, solved in closed form via Singular Value Decomposition (SVD). Applied to 153 plant species and 5.7 million genes, Procrustes alignment achieves four-fold higher cross-species Spearman correlation and consistently higher retrieval metrics than the SPACE autoencoder, while leaving within-species coexpression structure invariant (preservation ratio 1.000 against the unaligned baseline). The full alignment completes in under three minutes on a single CPU, and on downstream tasks, Procrustes embeddings improve within-species GO term prediction and outperform SPACE for cross-species GO transfer. Procrustes and sequence embeddings remain complementary for biological-process prediction, consistent with observations from SPACE. Availability. Code for producing the embeddings is made available at https://github.com/pwissenberg/orbit

  bioinformatics2026-05-07v1

• Image-Conditioned Diffusion for Privacy-Preserving Synthetic Medical Images

  Yaya-Stupp, D.; Lutsker, G.; Spiegel-Yerushalmi, O.; Segal, E.

  Abstract

  Medical imaging models depend on large, shareable datasets, yet privacy constraints limit data dissemination. Current text-conditioned diffusion models fail to preserve subtle, distributed clinical signals, such as continuous physiological biomarkers, rendering synthetic data insufficient for robust downstream physiological modeling. Here, we evaluate image-to-image (I2I) diffusion as a tunable, privacy-preserving transformation that produces a synthetic counterpart of real images while preserving downstream-relevant information. We fine-tune Stable Diffusion with low-rank adapters on retinal fundus photographs and chest radiographs, assessing fidelity, clinical signal preservation, cross-site transfer, and empirical re-identification risk. I2I consistently outperforms text-to-image generation in image fidelity and in preserving biomarker information. In cross-cohort transfer to an external retinal dataset from the UK Biobank, pretraining on I2I synthetic data performs comparably to real-image pretraining and surpasses it in the smallest fine-tuning sets. Varying I2I strength reveals that the privacy-utility tradeoff is highly modality-dependent: while retinal images achieve practical de-identification, chest X-rays exhibit structural combinatorics that leave them substantially re-identifiable even at high noise strengths, exposing critical boundaries for diffusion-based anonymization. These results position image-conditioned diffusion as a practical approach for generating shareable medical images with tunable de-identification.

  bioinformatics2026-05-07v1