Bioinformatics (Oxford, England)最新文献

{"title":"Regulatory-prior-guided attention preserves biological structure during unpaired single-cell RNA-ATAC integration.","authors":"Zhenglong Cheng, Jiao Zhang, Shixiong Zhang","doi":"10.1093/bioinformatics/btag696","DOIUrl":"10.1093/bioinformatics/btag696","url":null,"abstract":"<p><strong>Motivation: </strong>Single-cell RNA sequencing and single-cell ATAC sequencing provide complementary views of transcriptional output and chromatin regulatory potential, but integrating unpaired profiles remains challenging because the modalities differ in feature space, sparsity and noise. Existing approaches often frame integration as distribution matching, which can over-align biologically distinct, condition-specific or modality-specific cell states. We present scHPGT, a single-cell Heterogeneous Prior-Guided Transformer for regulatory-prior-guided integration of unpaired RNA and chromatin accessibility profiles. scHPGT uses modality-specific encoders to model RNA and ATAC signals, a prior-guided cross-modal Transformer to constrain gene-peak attention using regulatory links, and a domain-adversarial objective to reduce modality-specific discrepancies in a shared latent space.</p><p><strong>Results: </strong>Across PBMC3k, mouse spleen, CITE-seq/ASAP-seq PBMC and PBMC10k benchmarks, scHPGT improves clustering agreement, label transfer and biological structure preservation while maintaining effective modality alignment. In partial-overlap and condition-shift settings, scHPGT aligns shared populations without forcing unmatched or condition-specific states into inappropriate correspondence. Attention-derived links recover regulatory relationships, highlight marker-gene regulatory regions, recover transcription factor programs and produce regulatory activity profiles consistent with cell-type-specific transcriptional programs.</p><p><strong>Availability and implementation: </strong>Code and datasets are released at https://github.com/zlCreator/scHPGT.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148993056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Convex approaches to isolate the shared and distinct genetic components of complex traits.","authors":"Saikat Banerjee, Shane O'Connell, Sarah M C Colbert, Niamh Mullins, David A Knowles","doi":"10.1093/bioinformatics/btag670","DOIUrl":"10.1093/bioinformatics/btag670","url":null,"abstract":"<p><strong>Motivation: </strong>Groups of complex diseases, such as coronary heart disease, neuropsychiatric disorders, and cancers, often display overlapping clinical symptoms and pharmacological responses. Genetic variants with shared associations across diseases have the potential to help explain their underlying biological processes, but this sharing remains poorly understood.</p><p><strong>Results: </strong>We model the matrix of summary statistics of trait-associated genetic variants as the sum of a low-rank component-representing shared biological processes-and a sparse component representing disease-unique processes and arbitrarily corrupted or contaminated components. We introduce Clorinn, an open-source Python library that uses convex optimization algorithms to recover these components by minimizing a weighted combination of nuclear norm and L1 terms. Clorinn provides two significant benefits: (i) convex optimization guarantees reproducibility of the components and (ii) the low-rank \"uncorrupted\" matrix allows robust singular value decomposition and principal component analysis, which are otherwise highly sensitive to outliers and noise in the input matrix. In extensive simulations, we observe that Clorinn is uniquely able to recover the disease-group structure while remaining competitive on factor-level reconstruction error. We apply Clorinn to estimate 200 latent factors from GWAS summary statistics for 2110 phenotypes from the Pan-UK Biobank (N = 420 531 European-ancestry individuals) and 10 latent factors from 14 psychiatric disorders.</p><p><strong>Availability and implementation: </strong>Clorinn is available at https://github.com/daklab/clorinn.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148993058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Katalyst: knowledge-guided semantic alignment via contrastive learning for enzyme turnover prediction.","authors":"Ruilin Li, Senyu Tang, Jiaqi Deng, Taixing Qiu, Fei Guo, Jijun Tang, Xiaoyi Liu","doi":"10.1093/bioinformatics/btag709","DOIUrl":"10.1093/bioinformatics/btag709","url":null,"abstract":"<p><strong>Motivation: </strong>The turnover number (kcat) is crucial for understanding enzyme kinetics, yet its experimental determination remains costly and low-throughput. Existing deep learning approaches often rely on single-substrate assumptions and overlook full reaction contexts or language-informed semantic representations, limiting their accuracy and generalization across diverse reactions.</p><p><strong>Results: </strong>To address these challenges, we propose Katalyst, a knowledge-guided semantic alignment framework for kcat prediction based on contrastive learning. Katalyst integrates enzyme sequences, protein textual descriptions, substrates and products into a shared latent space using multi-channel encoders and contrastive alignment. An interaction-aware fusion module is designed to capture complementary cross-modal information. Experimental results demonstrate that Katalyst achieved the best overall performance, with the RMSE of 0.9880, R2 of 0.5829, and SRCC of 0.7603. Compared with the State-Of-The-Art (SOTA) baselines, Katalyst reduced RMSE by 3.01% and improved R2 and SRCC by 4.72% and 2.31%, respectively. Moreover, ablation studies and evaluations across sequence scales further confirmed the effectiveness of semantic alignment and multimodal feature fusion. Overall, Katalyst provides a robust and accurate framework for kcat prediction.</p><p><strong>Availability and implementation: </strong>The code and data are available at https://github.com/liruilin-SUAT/Katalyst.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13649181/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149120119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Taming the reference genome jungle: the refget sequence collection standard.","authors":"Donald R Campbell, Timothee Cezard, Sveinung Gundersen, Andrew D Yates, Robert M Davies, John Marshall, Sang-Hoon Park, Alex H Wagner, Michael I Love, Reggan Thomas, Oliver Hofmann, Nathan C Sheffield","doi":"10.1093/bioinformatics/btag554","DOIUrl":"10.1093/bioinformatics/btag554","url":null,"abstract":"<p><strong>Motivation: </strong>Reference genomes are foundational to genomics but suffer from widespread ambiguity and incompatibility due to inconsistent naming, undocumented differences, and lack of formal mechanisms for comparison.</p><p><strong>Results: </strong>To address this, we introduce the GA4GH refget Sequence Collections (seqcol) standard. Refget seqcol is a framework for unambiguous representation, retrieval, and comparison of sequence collections such as reference genomes and transcriptomes. The seqcol standard comprises four components: a structured data schema, a canonical encoding algorithm that produces content-based, globally unique identifiers, a retrieval API, and a comparison protocol. This standard enables precise identification of sequence collections, even across decentralized or private systems, and allows compatibility assessments beyond exact identity, such as order-relaxed matches or shared coordinate systems. We applied the refget seqcol standard to 60 human and 36 mouse reference genomes sourced from major providers. Using digest-based comparisons, we quantified levels of similarity across attributes including sequence names, lengths, coordinate systems, and actual sequence content. Our analysis revealed some consistent subsets of sequences or coordinate systems, as well as substantial incompatibility among references and duplicate references under different names. This work offers a scalable, reproducible solution to the reference genome compatibility crisis, enabling improved transparency, reuse, and integration in genomic analyses. Refget seqcol enhances interoperability across tools and datasets, making genomic research more robust and reproducible.</p><p><strong>Availability and implementation: </strong>To support adoption of refget seqcol, we provide a Python package implementing the full standard, a web API, and a comparison interface allowing users to assess local references against a curated database. The formal specification is hosted at https://ga4gh.github.io/refget/ and the reference implementation can be found at https://github.com/refgenie/refget.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148633151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"scLDM: a conditional diffusion framework for single-cell perturbation prediction.","authors":"Boyang Wu, Yuhang Liu, Yue Cheng, Xiangrong Liu, Quan Zou, Leyi Wei, Lei Xu","doi":"10.1093/bioinformatics/btag727","DOIUrl":"10.1093/bioinformatics/btag727","url":null,"abstract":"<p><strong>Motivation: </strong>Accurate prediction of single-cell responses to external stimuli is pivotal for deciphering gene regulatory mechanisms and accelerating data-driven drug discovery. However, effectively capturing the complex, non-linear mapping between intrinsic cell states and external stimuli remains an open problem.</p><p><strong>Results: </strong>We propose scLDM, a generative framework based on latent diffusion models for predicting single-cell perturbation responses. scLDM first compresses high-dimensional gene expression into a compact latent space via a variational autoencoder, followed by a conditional diffusion process to generate post-perturbation states, explicitly guided by pre-perturbation cellular state, cell type, and perturbation type. Systematic evaluations on six datasets across diverse biological settings, spanning pharmacological stimulation, viral infection, helminth infection, genetic perturbations, and multi-species immune response contexts, demonstrate that scLDM achieves superior predictive accuracy compared to state-of-the-art methods. Furthermore, the model exhibits strong interpretability, as the learned perturbation embeddings show high functional alignment with known biological mechanisms. Overall, scLDM provides a robust and biologically consistent strategy for in silico perturbation screening.</p><p><strong>Availability: </strong>The code is available at https://github.com/samrogers1233/scLDM and archived on Zenodo at https://doi.org/10.5281/zenodo.22658164.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149077007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NoiBic: a noise-tolerant biclustering algorithm for high-throughput gene expression data analysis.","authors":"Chaoyi Long, Jingxian Li, Bingqiang Liu, Duanchen Sun, Guojun Li","doi":"10.1093/bioinformatics/btag743","DOIUrl":"10.1093/bioinformatics/btag743","url":null,"abstract":"<p><strong>Motivation: </strong>High-throughput transcriptomic technologies generate large-scale gene expression datasets for functional module discovery. However, technical and biological noise can obscure coherent expression patterns and compromise module detection. Noise-tolerant methods are therefore needed to recover meaningful biclusters.</p><p><strong>Results: </strong>We developed NoiBic, a noise-tolerant biclustering algorithm for high-throughput transcriptomic data. NoiBic integrates a longest approximate common subsequence-based seed identification strategy with noise-aware column expansion to improve bicluster detection under noisy conditions. Across simulated datasets with controlled settings for bicluster coherency, matrix size, bicluster number, noise level, and overlap level, NoiBic accurately recovered planted biclusters, particularly under noisy and overlapping conditions. Evaluation on bulk and single-cell RNA-seq datasets demonstrated its ability to identify biologically meaningful gene modules and cell type-associated biclusters.</p><p><strong>Availability: </strong>NoiBic is implemented in C++17 and available at https://github.com/lcyi0208/NoiBic. The software version used in this study has been permanently archived on Zenodo at https://doi.org/10.5281/zenodo.22732377. Benchmark datasets are available at https://doi.org/10.5281/zenodo.21919392.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13681661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149129338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: massMatchR: a Shiny application for glycomics mass spectrometry data analysis.","authors":"","doi":"10.1093/bioinformatics/btag770","DOIUrl":"10.1093/bioinformatics/btag770","url":null,"abstract":"","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":"42 10","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13679382/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"smiMAD: adaptive multi-view graph learning for paired spatial omics integration.","authors":"Yuhao Tao, Fei Wang","doi":"10.1093/bioinformatics/btag758","DOIUrl":"10.1093/bioinformatics/btag758","url":null,"abstract":"<p><strong>Motivation: </strong>Paired spatial omics technologies measure transcriptomic profiles together with epigenomic or proteomic profiles while preserving tissue context. Integration remains challenging because modalities differ in feature distributions, sparsity, and spatial dependencies. Existing methods may not fully model complementary graph views or adapt spatial constraints, limiting preservation of spatial organization and modality-specific information.</p><p><strong>Results: </strong>We present smiMAD for paired spatial omics integration. smiMAD models spatial-neighborhood and molecular-feature graphs through graph-view-specific and common-information branches. An adaptive gate integrates graph-view and modality information, while an alignment-decorrelation objective promotes common-information embedding consistency and reduces branch redundancy. A dynamic spatial-consistency strategy progressively relaxes spatial constraints during training. Across 17 simulated and 15 real RNA-ATAC (assay for transposase-accessible chromatin) and RNA-protein datasets, smiMAD achieved competitive clustering accuracy, spatial coherence, and multimodal integration performance. Molecular marker patterns provided support for the inferred spatial regions.</p><p><strong>Availability and implementation: </strong>The source code is available at https://github.com/fdu-wangfeilab/smiMAD, with the version used in this study archived at https://zenodo.org/records/22931624. Data are available at https://zenodo.org/records/17207079.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149168388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coolsecture: an easy-to-use and improved framework for cross-species Hi-C contact map comparison.","authors":"Peng-Kai Zhu, Jiang-Qi Pan, Zhan-Chao Cheng, Jian Gao","doi":"10.1093/bioinformatics/btag683","DOIUrl":"10.1093/bioinformatics/btag683","url":null,"abstract":"<p><strong>Summary: </strong>Cross-species Hi-C comparison remains challenging because existing workflows often rely on multiple scripts, heterogeneous I/O formats, and limited diagnostic support. We present coolsecture, a Python 3 command-line toolkit that integrates multiple contact-matrix and synteny formats with bidirectional lift-over and reciprocal consistency assessment, multi-resolution percentile-based comparison, diagnostic visualization, and cross-sample similarity analysis, providing a streamlined and reproducible framework for comparative Hi-C analysis.</p><p><strong>Availability and implementation: </strong>coolsecture is distributed under the GPL-3 license. Source code, Snakemake workflows, and documentation are freely available at https://github.com/pk-zhu/Coolsecture and are archived on Figshare at https://doi.org/10.6084/m9.figshare.30158440.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148993008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying distribution shifts in single-cell data with scXMatch.","authors":"Anna Möller, Miriam Schnitzerlein, Eric Greto, Vasily Zaburdaev, Stefan Uderhardt, David B Blumenthal","doi":"10.1093/bioinformatics/btag706","DOIUrl":"10.1093/bioinformatics/btag706","url":null,"abstract":"<p><strong>Motivation: </strong>A basic task that frequently arises when analyzing single-cell data is to assess if there is a global distribution shift between the data profiles of cells from two different conditions. Widely used approaches to address this task such as visual inspection of two-dimensional representations or clustering-based workflows lack a solid statistical underpinning and are notoriously unstable and prone to confirmation bias.</p><p><strong>Results: </strong>To promote more rigorous analysis, we present scXMatch. scXMatch is based on a non-parametric graph-based test to quantify distribution shifts in arbitrary data spaces for which a suitable distance measure is available. We evaluated scXMatch on single-cell gene expression, chromatin accessibility, and imaging-derived cell morphology data, showing that it can robustly detect distribution shifts for different types of single-cell data. scXMatch thus aims to set a new standard in the single-cell biology field, replacing easy-to-manipulate semi-manual distribution shift quantification workflows by principled statistical testing.</p><p><strong>Availability and implementation: </strong>Python source code of scXMatch is available at https://github.com/bionetslab/scxmatch, a packaged version at https://anaconda.org/bioconda/scxmatch, and scripts to reproduce our results at https://github.com/bionetslab/scXMatch_paper (stable releases at https://doi.org/10.5281/zenodo.20665675).</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13645430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149120191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}