Bioinformatics (Oxford, England)[JOURNAL]
Sun
200 papers
BioMedGraphica: an all-in-one platform for joint textual biomedical prior knowledge and numeric graph generation.
Zhang H, Liang S, Xu T … +16 more , Li W, Huang D, Dong Y, Li G, Miller P, Goedegebuure P, Sardiello M, Cooper J, Buchser W, Dickson P, Fields RC, Cruchaga C, Chen Y, Province M, Payne P, Li F
Bioinformatics · 2026 Jun · PMID 42246943 · Full text
MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challe...
MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challenge. Human experts must manually review analysis results and generate new hypotheses based on extensive and interconnected biomedical prior knowledge, which is subjective and not scalable. While large language models can accelerate the discovery, their reasoning improves when grounded in structured, auditable, and comprehensive biomedical prior knowledge. However, biomedical knowledge is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of artificial intelligence systems to fully leverage biomedical data for scientific discovery. RESULTS: We developed BioMedGraphica, a novel all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified textual prior knowledge graph containing 2 306 921 entities and 27 232 091 relations. In addition, we present a novel textual-numeric graph (TNG) data structure concept, where textual information captures prior biological knowledge (e.g. transcription start sites, functions, mechanisms), numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data structure for developing novel graph analysis models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph database can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.
A unified multimodal model for generalizable zero-shot and supervised protein function prediction.
Boadu F, Wang Y, Cheng J
Bioinformatics · 2026 Jun · PMID 42244114 · Full text
MOTIVATION: Predicting protein function is a fundamental and challenging task that requires integrating diverse biological data modalities to capture complex functional relationships. Traditional machine learning methods...
MOTIVATION: Predicting protein function is a fundamental and challenging task that requires integrating diverse biological data modalities to capture complex functional relationships. Traditional machine learning methods often rely on single modalities or combine only a limited number (typically two), without aligning them in a unified representation, thereby constraining predictive accuracy. Moreover, most existing machine learning approaches are limited to preselected subsets of Gene Ontology (GO) function terms with sufficient annotations, making the prediction of novel function terms a persistent challenge. RESULTS: Here, we present FunBind, a multimodal AI model that jointly learns from five modalities, i.e., protein sequences, textual descriptions, domain annotations, structures, and GO terms, to enhance prediction accuracy and infer previously unseen functions. FunBind operates in two modes: (1) self-supervised pretraining using contrastive learning to align the sequence modality with other heterogeneous modalities in a unified latent space, enabling unsupervised zero-shot function prediction, and (2) supervised fine-tuning of the pretrained model to leverage all non-function modalities for comprehensive and accurate function classification. Our results show that FunBind's zero-shot capabilities allow it to generalize effectively to novel function terms never encountered before, while its joint multimodal fine-tuning strategy outperforms single-modality models and current state-of-the-art deep learning methods in typical function prediction settings. AVAILABILITY: https://github.com/jianlin-cheng/FunBind.
A computational analysis of the glycoprotein LRP1 structure and the role of glycans as quaternary glue.
Tuveri GM, Basile M, Acosta Gutiérrez S … +6 more , Kausas M, Pujals S, Tian X, Franzese G, Ruiz Pérez L, Battaglia G
Bioinformatics · 2026 Jun · PMID 42244107 · Publisher ↗
MOTIVATION: The low-density lipoprotein receptor-related protein 1 (LRP1) plays a critical role in development and transport across the blood-brain barrier (BBB), yet its molecular architecture has remained unresolved du...
MOTIVATION: The low-density lipoprotein receptor-related protein 1 (LRP1) plays a critical role in development and transport across the blood-brain barrier (BBB), yet its molecular architecture has remained unresolved due to the absence of an experimentally determined structure. RESULTS: Using homology modelling and neural network-based structure prediction algorithms, complemented with molecular dynamics (MD) simulations, we propose atomistic models of both monomeric and dimeric LRP1 forms. The simulations reveal a plausible dimerisation mechanism and provide insight into the dynamic behaviour of its flexible domains under physiological conditions. We estimated the energy required to disrupt the non-covalent interactions linking LRP1's α and β chains to be 180±2 kBT. MD simulations further highlight the fundamental role of glycans in stabilising the dimeric quaternary structure by increasing intra-dimer contacts. The resulting structural models also provide experimentally testable estimates of LRP1 size, domain organisation, and interface stability that may guide future imaging and mutagenesis studies. This study enhances our molecular understanding of LRP1-mediated transport across the BBB and the role of glycosylation in protein-protein interactions, opening new avenues for targeted drug design strategies. AVAILABILITY AND IMPLEMENTATION: The monomeric and dimeric LRP1 models are available in ModelArchive under the accession codes ma-k8036 and ma-ubwf7, respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available online.
Batch correction for large-scale mass spectrometry imaging experiments.
Sparre AA, Jensen ON
Bioinformatics · 2026 Jun · PMID 42244101 · Full text
SUMMARY: We assess batch correction methods for MALDI mass spectrometry imaging experiments. ComBAT reduced batch-related technical variance, maintained biological variation, and improved the overall score by 19.4%. AVAI...
SUMMARY: We assess batch correction methods for MALDI mass spectrometry imaging experiments. ComBAT reduced batch-related technical variance, maintained biological variation, and improved the overall score by 19.4%. AVAILABILITY AND IMPLEMENTATION: Methods are available in R. comBAT is used through the "sva" package while Harmony, CCA, FastMNN are available in the "Seurat" package https://github.com/satijalab/seurat. scVI, scANVI are Scanorama are available in the Python programming language and through their github https://github.com/scverse/scvi-tools. Associated R code is found at DOI: https://doi.org/10.5281/zenodo.19730022.
Predicting enhancer-promoter interactions using a stacking-based ensemble strategy.
Xiao Z, Ji H, Zou Q … +2 more , Ding Y, Yu L
Bioinformatics · 2026 Jun · PMID 42244099 · Full text
MOTIVATION: Enhancer-promoter interactions (EPIs) are essential for gene regulation and disease progression. Recent studies have shown that distal enhancers can regulate target genes through interactions with nearby prom...
MOTIVATION: Enhancer-promoter interactions (EPIs) are essential for gene regulation and disease progression. Recent studies have shown that distal enhancers can regulate target genes through interactions with nearby promoters, providing important insights into transcriptional regulation mechanisms. Although high-throughput experimental techniques have enabled large-scale identification of EPIs, these methods are often costly and time-consuming. In addition, existing computational approaches still face challenges in effectively integrating heterogeneous feature representations from different cell lines. RESULTS: We propose a stacked ensemble framework for EPI prediction that integrates feature representations from diverse cell line datasets using multiple machine learning algorithms. The extracted complementary patterns are further combined by an XGBoost classifier to improve robustness against overfitting. Experiments on six independent datasets show that the proposed method achieves superior accuracy and generalization compared with existing EPI prediction models, with an average AUROC of 0.909 while maintaining computational efficiency. AVAILABILITY: The source code and its archived release are available at GitHub and Zenodo. The Zenodo archive provides a versioned snapshot of the repository: https://zenodo.org/records/19952998.
scTACL: a multitask topology-aware contrastive learning approach for single-cell transcriptomics analysis.
Zhou M, Lu X, Liang Y … +4 more , Kow AWC, Wang G, Liu Q, Zhao Y
Bioinformatics · 2026 Jun · PMID 42242168 · Full text
MOTIVATION: The advent of single-cell RNA sequencing (scRNA-seq) technology has allowed researchers to measure gene expression profiles at the single-cell level, providing valuable insights into cellular heterogeneity. H...
MOTIVATION: The advent of single-cell RNA sequencing (scRNA-seq) technology has allowed researchers to measure gene expression profiles at the single-cell level, providing valuable insights into cellular heterogeneity. However, due to the limitations of current sequencing platforms, scRNA-seq data often contain significant noise, particularly severe dropout events, which pose major challenges for subsequent analyses. RESULTS: In this study, we developed a new method called topology-aware contrastive learning (scTACL). This approach uses contrastive learning between a cell similarity graph and a cell embedding similarity graph, employing a zero-inflated negative binomial (ZINB) distribution to model the reconstructed data. This alignment helps the processed data better reflect true biological signals. It delivers superior results in key tasks such as data imputation, clustering, batch effect correction, and cell-cell interaction. Additionally, scTACL successfully identified two distinct subtypes of epithelial cells in lung adenocarcinoma tissues, further demonstrating its effectiveness and usefulness in complex biological settings. Notably, without relying on spatial location information, scTACL still effectively distinguished the epithelial and mesenchymal regions in the spatial transcriptome data of liver cancer and identified the COLLAGEN signaling pathway, which plays a crucial role in the epithelial-mesenchymal transition process through intercellular communication analysis.
EPIC: multi-objective guided diffusion for epitope design in TCR-pMHC complexes.
Huang Y, Yu G, Chen L … +2 more , Luan H, Yang Y
Bioinformatics · 2026 Jun · PMID 42241229 · Full text
MOTIVATION: T cell receptor (TCR) recognition of peptide-major histocompatibility complex (pMHC) complexes is central to adaptive immunity, yet rational design of immunogenic epitopes remains elusive due to complex tripl...
MOTIVATION: T cell receptor (TCR) recognition of peptide-major histocompatibility complex (pMHC) complexes is central to adaptive immunity, yet rational design of immunogenic epitopes remains elusive due to complex triplet binding constraints and data scarcity. No existing method can generate epitopes satisfying simultaneous requirements for antigenicity, MHC presentation, and TCR specificity. RESULTS: We present EPIC, a multi-objective diffusion framework that decomposes TCR-pMHC binding into three biologically grounded sub-tasks, enabling training-free gradient guidance without end-to-end retraining. By integrating ESM-based classifiers with a peptide diffusion generator, EPIC leverages heterogeneous immunological interaction datasets to generate diverse, context-aware epitopes. EPIC-designed top-three epitopes achieve lower predicted interface energies compared to ground-truth epitopes in 78.31% of test cases, while maintaining 80.1% sequence novelty and comparable structural confidence. Generated epitopes exhibit 100% uniqueness, high diversity (64.05%), and high antigenicity scores (0.4723). To our knowledge, EPIC is the first computational framework capable of de novo epitope design while explicitly integrating the triplet constraints of TCR-pMHC binding. This paradigm shift from discovery to design unlocks new potential for personalized cancer vaccines, precision adoptive T cell therapy, and rapid response to emerging infectious diseases. AVAILABILITY AND IMPLEMENTATION: The source code of EPIC is available at https://github.com/Octopus125/EPIC and archived on Zenodo (DOI: 10.5281/zenodo.18537646).
Evaluation of epistasis detection methods for quantitative phenotypes.
Listopad S, Renjith G, Peng Q
Bioinformatics · 2026 Jun · PMID 42241225 · Full text
MOTIVATION: Epistasis, or genetic interaction, plays a crucial role in shaping complex traits and has been increasingly recognized for its widespread influence in genetic architectures. While epistasis detection has been...
MOTIVATION: Epistasis, or genetic interaction, plays a crucial role in shaping complex traits and has been increasingly recognized for its widespread influence in genetic architectures. While epistasis detection has been extensively evaluated in case-control studies, its performance with quantitative phenotypes remains comparatively understudied. RESULTS: We identified and evaluated six epistasis detection methods applicable to quantitative trait analysis: EpiSNP, Matrix Epistasis, MIDESP, PLINK Epistasis, QMDR, and REMMA. Using the EpiGEN simulator, we generated synthetic datasets modeling four classes of pairwise SNP interactions-dominant, multiplicative, recessive, and XOR. We also assessed BOOST and MDR algorithms using discretized (case-control) versions of the same datasets. Performance varied notably by interaction type: REMMA achieved the highest overall detection rate (55%), particularly excelling with dominant interactions (100%). MDR excelled with multiplicative (57%) and XOR (69%) interactions. Meanwhile, EpiSNP attained the best performance for recessive interactions (67%). All methods except BOOST produced F1 scores below 0.05 for most interaction types. We further evaluated the methods using a real-world dataset. When applied to the Adolescent Brain Cognitive Development dataset to analyse the externalizing behavior phenotype, both PLINK Epistasis and PLINK BOOST identified SNPs within the DRD2 and DRD4 genes, consistent with previously reported genetic associations. Given the variability in tool performance across interaction types, no single method provides optimal detection across all scenarios. Leveraging multiple detection algorithms may therefore yield more comprehensive insights into epistatic effects in quantitative trait analyses. AVAILABILITY AND IMPLEMENTATION: All relevant code and simulated datasets can be found at github.com/staslist/Epistasis_Review repository.
momapy: a Python library to work with molecular maps.
Rougny A, Ostaszewski M, Sagatopam V
Bioinformatics · 2026 Jun · PMID 42236260 · Full text
MOTIVATION: Molecular maps are graphical representations of the molecular mechanisms underlying biological systems. They are a valuable tool for curating, exchanging, and understanding biological knowledge, and may serve...
MOTIVATION: Molecular maps are graphical representations of the molecular mechanisms underlying biological systems. They are a valuable tool for curating, exchanging, and understanding biological knowledge, and may serve as a backbone for data analysis and modelling. Molecular maps are supported by a rich software ecosystem. However, there are currently no tools that support advanced programmatic analysis and processing of maps, in particular the extraction of the biological concepts they represent or their comparison. RESULTS: We introduce momapy, a generic Python library to work with molecular maps programmatically. At its core, momapy allows users to extract and separate the data model of a map from its graphical representation, and perform a variety of base operations on them, including their manipulation and comparison. momapy currently supports the SBGN and CellDesigner formats, two of the main standards to represent molecular maps graphically, and can be easily extended to support additional formats and functionalities. AVAILABILITY: momapy is implemented in Python (RRID:SCR_008394) under a GPLv3 license. The code can be downloaded freely from https://github.com/adrienrougny/momapy and is archived on Zenodo (https://doi.org/10.5281/zenodo.19088611). Complete documentation and a user manual are available at https://adrienrougny.github.io/momapy.
Network methods for diagonal integration of unpaired single-cell multiomics data: a review.
Barylli M, Saha J, Buffart TE … +5 more , Koster J, Lenos KJ, Vermeulen L, Bumbuc RV, Sheraton VM
Bioinformatics · 2026 Jun · PMID 42234838 · Full text
MOTIVATION: Advances in single-cell sequencing have enabled multiomics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simu...
MOTIVATION: Advances in single-cell sequencing have enabled multiomics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simultaneous transcriptomic capture. Unlike antibody-based methods such as CITE-seq, which permit paired profiling but are restricted to targeted protein panels, scMS provides unbiased, genome-scale coverage of the intracellular proteome yet necessitates post hoc integration of unpaired datasets. This diagonal integration challenge, where transcriptomes and proteomes are measured in separate cells lacking shared anchors, remains underserved by existing reviews, which focus predominantly on vertical integration strategies enabled by non-destructive assays. RESULTS: We survey the complete computational pipeline for constructing mechanistic proteogenomic networks from unpaired single-cell data, covering: (i) unimodal network inference such as knowledge-based approaches, probabilistic graphical models, temporal directionality inference, and generative and foundation model strategies that establish the transcriptomic scaffold; (ii) cross-modal integration architectures such as network propagation, graph neural networks (scMRDR, scmFormer, scCotag), and consensus frameworks designed explicitly for the unpaired proteomics setting; and (iii) benchmarking paradigms spanning network reconstruction (BEELINE, GRETA, CausalBench) and multi-task integration evaluation (scMultiBench, SCMMIB), with guidance on metric selection under network sparsity and class imbalance. We identify three principal axes of future development: generative proteomic translation from transcriptomic precursors, inductive prior embedding in next-generation architectures, and perturbation-based causal benchmarking. AVAILABILITY AND IMPLEMENTATION: This is a review article; no novel software is distributed. A curated benchmark resource table, methods starter guide, and per-method bottleneck annotations are provided in the Supplementary Material.
Benchmarking deep learning methods for Cα atom prediction in cryo-EM density maps.
Zhang T, Liu Z, Ma Y … +2 more , Feng C, Han R
Bioinformatics · 2026 Jun · PMID 42234518 · Full text
MOTIVATION: With the advancement of cryo-electron microscopy (cryo-EM) into the atomic resolution era, accurate Cα atom modeling has become essential for macromolecular structure determination. However, existing evaluati...
MOTIVATION: With the advancement of cryo-electron microscopy (cryo-EM) into the atomic resolution era, accurate Cα atom modeling has become essential for macromolecular structure determination. However, existing evaluation systems overly rely on full-atom metrics and lack a dedicated, comprehensive benchmark for assessing Cα prediction modules within automated modeling tools. RESULTS: To address this gap, we establish a rigorous benchmark to evaluate the Cα prediction performance of four prominent deep learning-based methods (ModelAngelo, DeepMainMast, EModelX, and CryoAtom) across multiple dimensions. We construct a diverse dataset covering a wide range of resolutions (1-8 Å), molecular weights, and noise levels. A novel evaluation framework is introduced, incorporating multi-threshold RMSD-based metrics (1-3 Å) alongside advanced point-cloud similarity measures (Chamfer Distance, Earth Mover's Distance) for quantitative and nuanced assessment. Our results reveal that method performance is highly dependent on the chosen evaluation criteria and intrinsic data characteristics. ModelAngelo excels under loose thresholds with high-quality data but shows sensitivity to resolution degradation; CryoAtom demonstrates notable computational efficiency, however, its completeness-oriented design leads to a certain loss of precision; EModelX demonstrates balanced generalization across varied conditions; DeepMainMast achieves high localization accuracy under stringent criteria but incurs a high computational cost. AVAILABILITY AND IMPLEMENTATION: This work provides a reproducible, Cα-centric evaluation framework to guide method development and advance automated cryo-EM structure determination. The source code for the benchmark and evaluation metrics is freely available at https://github.com/zhtianz/Benchmarking\_CA.
NExON-Bayes: A Bayesian approach to network estimation informed by ordinal covariates.
Feest J, Ruffieux H, Lingjærde C … +1 more , Xi X
Bioinformatics · 2026 Jun · PMID 42233294 · Publisher ↗
MOTIVATION: In heterogeneous disease settings, accounting for intrinsic sample variability is crucial for obtaining reliable and interpretable omic network estimates. However, most graphical model analyses of biomedical...
MOTIVATION: In heterogeneous disease settings, accounting for intrinsic sample variability is crucial for obtaining reliable and interpretable omic network estimates. However, most graphical model analyses of biomedical data assume homogeneous conditional dependence structures, potentially leading to misleading conclusions. To address this, we propose a joint Gaussian graphical model that leverages sample-level ordinal covariates (e.g. disease stage) to account for heterogeneity and improve the estimation of partial correlation structures. RESULTS: Our modelling framework, called NExON-Bayes, extends the graphical spike-and-slab framework to account for ordinal covariates, jointly estimating their relevance to the graph structure and leveraging them to improve the accuracy of network estimation. To scale to high-dimensional omic settings, we develop an efficient variational inference algorithm tailored to our model. Through simulations, we demonstrate that our method outperforms the vanilla graphical spike-and-slab (with no covariate information), as well as other state-of-the-art network approaches which exploit covariate information. Applying our method to reverse phase protein array data from patients diagnosed with stage I, II or III breast carcinoma, we estimate the behaviour of proteomic networks as cancer progresses. Our model provides insights not only through inspection of the estimated proteomic networks, but also of the estimated ordinal covariate dependencies of key groups of proteins within those networks, offering a comprehensive understanding of how biological pathways shift across disease stages. AVAILABILITY AND IMPLEMENTATION: A user-friendly R package for NExON-Bayes with tutorials is available on Github at github.com/jf687/NExON, and archived at https://doi.org/10.5281/zenodo.20312938. The source of the dataset used is cited in the relevant section.
Barbell reveals and resolves demultiplexing and trimming issues in Nanopore data.
Beeloo R, Groot Koerkamp R, Jia X … +5 more , Broekhuizen-Stins MJ, van IJken L, Broens EM, Zomer A, Dutilh BE
Bioinformatics · 2026 Jun · PMID 42233292 · Full text
MOTIVATION: Oxford Nanopore sequencing enables long-read analysis for diverse applications, but artefacts introduced by Nanopore barcoding are poorly characterized and can compromise demultiplexing accuracy and downstrea...
MOTIVATION: Oxford Nanopore sequencing enables long-read analysis for diverse applications, but artefacts introduced by Nanopore barcoding are poorly characterized and can compromise demultiplexing accuracy and downstream analyses. RESULTS: Using a rapid barcoding experiment on 66 diagnostic samples, we found that only 83% of reads followed the expected single-barcode configuration, while 17% showed complex barcode attachments. We observed similar patterns in public datasets, and also in native barcoding datasets where only 30%-70% of the reads had barcodes on both ends. Widely used demultiplexers, including Dorado, fail to resolve these cases, leaving ∼10% of our rapid barcoding reads partially trimmed and contaminated with adapter fragments. We developed Barbell, a pattern-aware demultiplexer that is designed to detect complex barcode configurations. Barbell reduced contaminated reads from >400 000 (Dorado/Flexiplex) to 166 (99.96% reduction), minimized barcode bleeding, and supports custom experimental designs such as dual-end barcodes and shorter barcodes (e.g. Illumina barcodes). We further show that such contamination is widespread in public databases, with Nanopore sequences detected in hundreds of NCBI entries, some of which are responsible for artificial taxonomic connections. AVAILABILITY AND IMPLEMENTATION: Barbell is open source and available at https://github.com/rickbeeloo/barbell.
CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data.
Zhang Z, Han J, Song L … +1 more , Zhang X
Bioinformatics · 2026 Jun · PMID 42225598 · Full text
MOTIVATION: Single-cell sequencing technologies allow researchers to study cell-cell variation within a cell population. Variations between cells are driven by the underlying biological network, particularly gene regulat...
MOTIVATION: Single-cell sequencing technologies allow researchers to study cell-cell variation within a cell population. Variations between cells are driven by the underlying biological network, particularly gene regulatory networks (GRNs). GRNs rewire as cells evolve, and different cells can have different GRNs. However, while single-cell RNA-sequencing (scRNA-seq) and single-cell multi-omics data have been used to reconstruct GRNs, the output GRNs are rarely cell-specific, but rather, most existing methods infer population-level or cell-type-level GRNs. RESULTS: We propose CeSpGRN (Cell-Specific Gene Regulatory Network inference), a method that infers cell-specific GRNs from scRNA-seq, paired scRNA-seq and scATAC-seq, or spatial transcriptomic data. In particular, existing methods that use matching scRNA-seq and scATAC-seq data incorporate population-level region information in GRN inference, whereas CeSpGRN utilizes single-cell resolution region information. CeSpGRN infers cell-specific GRNs using a kernel-weighted Gaussian Copula Graphical Model, and incorporates multi-omic or spatial location information when constructing the objective function. We tested CeSpGRN on both simulated and real datasets, and the results show that CeSpGRN has a superior performance compared to baseline methods in reconstructing GRNs and detecting regulatory interactions that differ between cells. CeSpGRN uncovered regulatory interactions that rewire during biological processes on real datasets. AVAILABILITY AND IMPLEMENTATION: CeSpGRN is a Python package available at https://github.com/PeterZZQ/CeSpGRN.
HisCMCL: Cross-Modal Contrastive Learning with Hierarchical Multi-Scale Fusion for Spatial Expression Prediction.
Liu C, Zhu F, Min W
Bioinformatics · 2026 May · PMID 42213081 · Publisher ↗
MOTIVATION: High costs and operational complexity limit the clinical application of spatial transcriptomics (ST). Inferring ST from pathology images is a promising alternative, the core of which lies in effectively align...
MOTIVATION: High costs and operational complexity limit the clinical application of spatial transcriptomics (ST). Inferring ST from pathology images is a promising alternative, the core of which lies in effectively aligning image and gene expression features. However, existing models are mostly limited to single-scale and single-slice modeling. This not only fails to connect microscopic cells with macroscopic tissues but also restricts generalization due to the inability to extract cross-sample shared features. Furthermore, the inherent representational differences between modalities further exacerbate the difficulty of feature alignment. RESULTS: To address these challenges, we propose HisCMCL, a multimodal framework. The model combines multi-scale features with a cross-attention mechanism to jointly capture local morphology and global context. Additionally, it fuses spatial location information and utilizes a contrastive learning strategy to facilitate the effective alignment of image and transcriptomic features. Evaluations on four public datasets demonstrate that HisCMCL outperforms existing baseline methods in predictive performance. It exhibits good structural consistency in identifying cancer and immune markers and delineating tumor regions, offering new insights for spatial expression inference. AVAILABILITY AND IMPLEMENTATION: The code of HisCMCL is available at https://github.com/wenwenmin/HisCMCL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
VisPan: real-time visualisation of multiplex amplicon-based sequencing panels for rapid syndromic surveillance and pathogen detection.
Lechat P, Kwasiborski A, Vincent R … +4 more , Vanhomwegen J, Manuguerra JC, Caro V, Hourdel V
Bioinformatics · 2026 Jun · PMID 42213080 · Full text
MOTIVATION: Infectious diseases persist as a major global public health challenge. Diverse factors, including climate change, globalization, deforestation, human-animal interactions, lifestyle choices, and various biolog...
MOTIVATION: Infectious diseases persist as a major global public health challenge. Diverse factors, including climate change, globalization, deforestation, human-animal interactions, lifestyle choices, and various biological factors, can contribute to their emergence and reemergence. Rapid detection and characterization of (re)emerging pathogens are therefore critical for effective outbreak management and for enhancing our understanding of epidemics by monitoring the transmission, spread, evolution, and genomics of pathogens. In this context, next-generation sequencing technologies (NGS), particularly long-read platforms such as Oxford Nanopore Technologies (ONT), have opened new avenues for real-time pathogen monitoring. However, the bioinformatics bottleneck remains a challenge, emphasizing the need for efficient, accessible, and user-friendly analysis tools. RESULTS: Here, we present a tool adapted from the RAMPART software that enables real-time data visualisation of multiplex PCR syndromic panels combined with Oxford Nanopore sequencing. This real-time analysis enables rapid pathogen detection, from raw data acquisition to taxonomic assignment, within minutes. The interface offers dynamic visual tracking of the sequencing run and amplicon coverage, facilitating immediate insights during diagnostic workflows. Validation experiments confirmed the system's reliability, accurately identifying all pathogens present in complex clinical or environmental samples. This tool provides an integrated, user-friendly solution for genomic pathogen surveillance in field or clinical settings.
Genomics-informed approach identifies which cell types regulate the metabolome.
Krupkin H, Padhi EM, Nachun D … +3 more , Kain J, Long JZ, Montgomery SB
Bioinformatics · 2026 Jun · PMID 42213079 · Full text
MOTIVATION: Metabolism occurs in a cell type-specific manner, but which cells regulate metabolite levels remains unclear. RESULTS: Here, we integrate some of the largest metabolite quantitative trait loci datasets, TOPMe...
MOTIVATION: Metabolism occurs in a cell type-specific manner, but which cells regulate metabolite levels remains unclear. RESULTS: Here, we integrate some of the largest metabolite quantitative trait loci datasets, TOPMed and UK Biobank, with one of the most extensive single-cell RNA sequencing resources, Tabula Sapiens. This integration allows us to identify cell types that regulate metabolites body-wide. We find hepatocytes are the primary regulatory cell type for most metabolites, associating with 385/410 (94%) metabolites for whom an association is found. Additionally, our multi-gene approach reveals more metabolite associations with beta cells compared to those identified using a single-gene approach. For example, we identify novel metabolite-cell type associations, such as the association between phenylpropanoic acid and beta cells, this metabolite that was previously thought to be regulated by the microbiome. AVAILABILITY: Code used in this work is available via Github at https://github.com/haimkru/Metabolite-Cell-Type-Associations.
PathwayEmbed: a computational tool to quantify intracellular signaling transduction states from transcriptomic data.
Huang Y, Gerecht S, Kyriakides T … +1 more , Raredon MSB
Bioinformatics · 2026 Jun · PMID 42209442 · Full text
MOTIVATION: Intracellular signaling pathways regulate essential cellular functions and orchestrate complex biological processes, yet their dynamic activity remains challenging to quantify with precision. Advances in sing...
MOTIVATION: Intracellular signaling pathways regulate essential cellular functions and orchestrate complex biological processes, yet their dynamic activity remains challenging to quantify with precision. Advances in single-cell omics enable pathway activity inference at the transcriptional level; however, existing computational tools often overlook mechanistic features of signaling networks, failing to formally treat the expected directionality of transcriptional change due to signal transduction. To address this technological gap, we have engineered PathwayEmbed, an R-based computational framework for estimating intracellular signal transduction states from single-cell transcriptomic datasets. RESULTS: PathwayEmbed integrates KEGG pathway information with perturbation-derived RNA sequencing data to assign directional coefficients that capture gene-specific transcriptional responses to pathway activation, repression, and/or signal transduction. These coefficients, in combination with the input data, are used to compute hypothetic ON/OFF range for each pathway. Each cell is then mapped to a specific location between these ON/OFF states, and activity scores are then computed based on the distances to these reference states, providing a continuous and interpretable measure of signaling activity at single-cell resolution. This framework enables robust visualization and quantitative comparison of pathway activity across cell populations. Applied to spatial transcriptomic data, PathwayEmbed captures spatial variation in signaling transduction states and allows comparisons at both temporal and spatial scale. The framework takes tabular data as input and is broadly compatible with established single-cell analysis workflows, supports user-defined pathway ground-truths, and offers a flexible, mechanistically informed approach for quantifying and comparing intracellular signaling activity in a wide variety of contexts. AVAILABILITY: PathwayEmbed is an open-source R software under academic free license, and it is available at https://github.com/raredonlab/PathwayEmbed. Use-case vignettes are available at https://raredonlab.github.io/PathwayEmbed/.
MethyNano: supervised contrastive pretraining enables robust and generalizable methylation detection from nanopore sequencing.
Yan J, Chen Y, Gong Y … +2 more , Zhang C, Yang J
Bioinformatics · 2026 Jun · PMID 42209437 · Full text
MOTIVATION: 5-Methylcytosine (5mC) plays an important role in gene regulation and development. Although nanopore sequencing has enabled direct detection of 5mC, existing methods still face several limitations, including...
MOTIVATION: 5-Methylcytosine (5mC) plays an important role in gene regulation and development. Although nanopore sequencing has enabled direct detection of 5mC, existing methods still face several limitations, including poor generalization across species and sequence contexts (CpG/CHG/CHH), as well as suboptimal integration of sequence and current signals. RESULTS: Here, we present MethyNano, a deep learning framework incorporating a contrastive learning strategy to detect 5mC from nanopore reads. By encouraging more discriminative and stable representations, the contrastive objective improves the model's sensitivity to rare sequence contexts and reduces its prediction uncertainty in challenging regions. Across datasets from Arabidopsis thaliana, Oryza sativa, and Homo sapiens, our model achieves superior performance on key metrics compared with other existing methods. Extensive cross-species and cross-motif experiments demonstrate the robust generalization performance of MethyNano, while dimensionality-reduction visualizations of learned features provide an intuitive view of the model's efficient representation capability. Moreover, our ablation studies show that MethyNano's architecture enables more effective integration of critical features, leading to higher predictive accuracy. AVAILABILITY AND IMPLEMENTATION: The project code is available at https://github.com/baigeHUI/MethyNano and https://doi.org/10.5281/zenodo.19858400.
Integrated multi-omics strategies for identifying novel therapies in psoriasis.
Si S, Wang X, Zhan S
Bioinformatics · 2026 Jun · PMID 42209436 · Full text
MOTIVATION: Psoriasis is a chronic, immune-mediated disorder with an unmet need for effective treatments. To systematically prioritize therapeutic targets, we integrated proteome-wide Mendelian randomization (MR) with ex...
MOTIVATION: Psoriasis is a chronic, immune-mediated disorder with an unmet need for effective treatments. To systematically prioritize therapeutic targets, we integrated proteome-wide Mendelian randomization (MR) with expression validation in blood/skin, genetic susceptibility analysis, differential gene expression (DGE) from bulk and single-cell RNA sequencing (scRNA-seq), colocalization, pathway enrichment, and protein-protein interaction analyses. RESULTS: Proteome-wide MR identified 29 candidate protein targets (Bonferroni-corrected), all replicated in independent datasets. Fifteen targets showed significant expression associations in blood or skin. Eleven proteins-UBLCP1, IL23A, ASF1A, RARRES2, ICAM1, PRSS53, ICAM5, GCA, IL2RA, DBI, and NFKB1-exhibited consistent directional effects with their genes. Genetic susceptibility analysis confirmed 20 target-specific polygenic scores for psoriasis and five for psoriatic arthritis. DGE analysis identified 13 targets in bulk and 13 in scRNA-seq-primarily in keratinocytes and immune cells-with IL2RA, COMP, and A2ML1 dysregulated across both. Colocalization analysis implicated shared causal variants for psoriasis in ASF1A, CD8A, CTF1, IL7R, MMP12, RARRES2, XCL2, DBI, IL23A, IL2RA, SGSH, and TIMD4. Enrichment analyses highlighted involvement in cytotoxicity, immune regulation, and JAK-STAT signaling. Eighteen targets interacted with approved anti-psoriasis drugs. Notably, drugs targeting IL2RA, IL7R, CTF1, ICAM1, MMP12, NFKB1, CD8A, DDX58, IL12A, SGSH, and FAP are approved or in trials for other diseases, suggesting repurposing potential. Our integrative multi-omics approach prioritized 29 high-confidence targets, including 13 novel candidates (RARRES2, ASF1A, CTF1, DBI, B3GNT2, CD8A, TIMD4, CRTAM, SGSH, XCL2, DAPK2, A2ML1, and FAP). Several high-priority targets-such as IL2RA, IL23, MMP12, RARRES2, IL7R, and ICAM1-were supported across analytical layers. These findings provide a robust foundation for psoriasis drug development. AVAILABILITY AND IMPLEMENTATION: The code used for the analyses in this manuscript has been archived in Zenodo at [DOI: 10.5281/zenodo.19692128].
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