Briefings in bioinformatics最新文献

{"title":"Benchmarking methods for inferring single-cell transcription factor activity using large-scale perturbation sequencing data.","authors":"Yuehui Zhu, Dongmei Han, Zhen Wang","doi":"10.1093/bib/bbag513","DOIUrl":"10.1093/bib/bbag513","url":null,"abstract":"<p><p>Transcription factor activity (TFA) is determined not solely by the expression level of the transcription factor (TF) gene itself, but is also modulated by a series of post-transcriptional regulatory processes. Although numerous computational methods have been developed to infer TFA from single-cell transcriptomic data by constructing gene regulatory networks (GRNs), a systematic and unified evaluation of these methods using high-quality experimental data remains lacking in the field. In this study, we conducted a comprehensive evaluation of eight mainstream TFA inference methods spanning three categories-prior GRN-based, de novo GRN-based, and integrated GRN-based approaches-using large-scale, high-quality single-cell perturbation sequencing (Perturb-seq) datasets. Our results demonstrate that metaTF, which employs an integrated GRN, achieves the best performance across multiple metrics, including TF coverage, predictive accuracy for perturbed cells, and accuracy for perturbed TFs. Among de novo GRN-based methods, pySCENIC exhibits predictive accuracy second only to metaTF but with lower TF coverage; meanwhile, decoupleR, a prior GRN-based method, ranks highly across all evaluated metrics. Further investigation reveals that the enrichment of reconstructed regulons within differentially expressed genes, the selection of prior GRNs and TFA scoring algorithms, and the perturbation types of target TFs are all critical factors influencing the accuracy of TFA inference. This study provides practical recommendations for the application and development of TFA inference methods.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13584069/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148988099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"scASprofiler: profiling single-cell RNA splicing with a deep convolutional generative network.","authors":"Pengwei Hu, Pengcheng Song, Bingjie Dai, Chunshen Long, Hanshuang Li, Yongchun Zuo, Yongqiang Xing","doi":"10.1093/bib/bbag497","DOIUrl":"10.1093/bib/bbag497","url":null,"abstract":"<p><p>Single-cell RNA sequencing (scRNA-seq) enables the investigation of alternative splicing (AS) at cellular resolution. However, the analysis of AS in scRNA-seq data is constrained by sparse splice-junction coverage, a consequence of low sequencing depth per cell. This limitation is particularly pronounced in 3'-biased, droplet-based protocols. To overcome this, we developed scASprofiler, a tailored deep convolutional generative network designed to decipher AS with single-cell resolution. scASprofiler performs missing-value imputation of junction read counts by leveraging cells generated by a mask-aware variational autoencoder-generative adversarial network (VAE-GAN), reducing oversmoothing of imputed values and preserving biologically meaningful heterogeneity. Across benchmarks, scASprofiler enhances delineation of cell populations and recovery of splicing signals. When applied to datasets generated using plate- and droplet-based platforms, scASprofiler uncovers cryptic AS events and reveals cell-type-specific AS patterns that complement and extend insights derived from gene expression. Together, our study establishes scASprofiler as a robust and versatile tool for dissecting AS landscapes from scRNA-seq data.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13563674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148944317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeOPUS: cellular deconvolution via optimized power-transformed unmixing with shrinkage.","authors":"Ha Nguyen, Khoi Nguyen, Phi Bya, Tarik Alafif, Tho T Quan, Tin Nguyen","doi":"10.1093/bib/bbag524","DOIUrl":"10.1093/bib/bbag524","url":null,"abstract":"<p><p>Single-cell RNA sequencing provides high-resolution insights into cellular heterogeneity but its widespread application is often constrained by high costs and technical complexity. Cellular deconvolution serves as a cost-effective alternative by computationally estimating cell-type proportions from bulk RNA-seq data. However, the extreme dynamic range and inherent heteroscedasticity of transcriptomic data pose significant challenges for accurate estimation. Here, we present deconvolution via optimized power-transformed unmixing with shrinkage (DeOPUS), a reference-based deconvolution method that introduces a hierarchical shrinkage transformation (HST) to robustly estimate cellular compositions. DeOPUS integrates multi-level adaptive priors, variance-stabilizing power transformations, and rank-based quantile normalization to mitigate the influence of outliers and high-variance technical noise. We systematically benchmark DeOPUS against eight state-of-the-art methods across 122 tissues and 12 organ systems. DeOPUS consistently outperforms all eight competitors by having mean Pearson ($r = 0.82$) and Spearman ($rho = 0.77$) correlations. DeOPUS significantly surpasses the second-best method ($r = 0.75, rho = 0.68$) while maintaining the lowest mean squared error (MSE = 0.007). Notably, DeOPUS ranks first in the vast majority of tissues and organ systems using all three metrics, demonstrating unrivaled robustness to increasing cellular complexity. More in-depth validation on 18 bulk datasets with experimentally determined cell-type proportions further confirms DeOPUS's strong performance. DeOPUS is the sole method to achieve positive correlations across all datasets, and achieves the highest accuracy for dominant cell-type identification. DeOPUS is available as an open-source R package at https://github.com/tinnlab/DeOPUS.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149058557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MCOD: a memory-constrained deep learning framework for robust outlier detection in quantitative proteomics.","authors":"Jinze Huang, Huanyue Liao, Bo Meng, Guangkui Fan, Dong An, Xinhua Dai, Xiang Fang, Yang Zhao","doi":"10.1093/bib/bbag507","DOIUrl":"10.1093/bib/bbag507","url":null,"abstract":"<p><p>Ensuring robust quality control (QC) remains a major challenge in quantitative proteomics, particularly in detecting and managing outliers. Deep learning offers powerful representational capacity for ultra-high-dimensional data but often suffers from overfitting in small-sample scenarios. To address this, we propose memory-constrained outlier detection (MCOD), a deep anomaly detection framework that directly processes MaxQuant outputs and achieves competitive recall at high precision levels, which suggests a reduced risk of overlooking true outliers. MCOD integrates two innovations: (i) a memory-constrained module (MC module) that mitigates over-representation of samples via prototype-based regularization, and (ii) an adaptive steady-aware regulator that dynamically adjusts the per-sample loss weights in the MC module according to the estimated overfitting risk. Across two simulation settings based on a human cervical cancer cell line (HeLa) proteomics dataset and two real-world cancer proteomics datasets, MCOD consistently outperformed 18 statistical, machine learning, and deep learning baselines, achieving superior area under the receiver operating characteristic curve and area under the precision-recall curve scores. Functional enrichment analyses on the two real-world datasets showed that MCOD performed favorably compared to the three domain-specific models. Furthermore, feature-level visualization provided insights into the rationale behind the model's anomaly assignments. Collectively, MCOD establishes a robust and scalable framework for data QC in quantitative proteomics.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149058542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BRIDGE-KO: auditable transfer learning with bridged knockoffs improves cross-cohort microbiome prediction of immunotherapy response.","authors":"Jie Sun, Zhenjiang Fan, Rebecca Caldino Bohn, Soyeon Kim, Hyun Jung Park","doi":"10.1093/bib/bbag545","DOIUrl":"10.1093/bib/bbag545","url":null,"abstract":"<p><p>Transfer learning promises to reuse AI models across biomedical studies, but it often fails when differences between datasets introduce cohort-specific artifacts and 'negative transfer.' We present BRIDGE-KO, an interpretable deep-learning framework that turns transfer learning into a testable design process by asking: where to transfer from (source-target coverage and proximity), what to transfer (which representation level is most portable), and how to harmonize data to reduce technical confounding. BRIDGE-KO creates source-preserving knockoffs-synthetic 'shadow' features that capture the source dependency structure-and bridges them to a fixed target cohort, providing built-in baselines that flag non-portable signal. Applied to predicting immune checkpoint inhibitor response, transferring gut microbiome profiles from MicroBioMap (n = 168,464) to melanoma immune checkpoint inhibitor cohorts (n = 154), naive baselines performed near chance (AUC ≈ 0.52), whereas batch correction plus proximity-aware transfer improved performance (AUC = 0.67, 10 splits). BRIDGE-KO also enables an interpretable responder-specific audit of microbiome remodeling via correlation-shift spread.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13660471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149179438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepU-PRS: nonlinear and uncertainty-aware SNP heritability modeling for annotation-informed polygenic risk scoring.","authors":"Junhyeong Lee, Dokyoon Kim, Seunggeun Lee","doi":"10.1093/bib/bbag563","DOIUrl":"10.1093/bib/bbag563","url":null,"abstract":"<p><p>Polygenic risk scores (PRSs) are widely used to quantify genetic predisposition to complex traits, and their predictive accuracy can be enhanced by incorporating functional annotations into single-nucleotide polymorphism (SNP) effect-size estimation. Existing approaches typically assume linear relationships between annotations and SNP effects, neglecting predictive aleatoric variance and potential nonlinear regulatory interactions. To evaluate the gains of nonlinear and uncertainty-aware modeling, we developed DeepU-PRS, a deep ensemble framework that extends the linear BLD-LDAK model with a neural network trained on functional annotations to jointly estimate SNP heritability and its aleatoric uncertainty, which are then integrated into a Bayesian PRS model. Across 28 continuous and 8 disease phenotypes in the UK Biobank, DeepU-PRS improved the mean continuous R2 by approximately 10% over linear BLD-LDAK, with the largest gains for traits whose association signals were concentrated, whereas the more diffuse disease traits favored BLD-LDAK. These patterns were reproduced in individual-level data from All of Us. Against a panel of established methods (C + T, Lassosum, PRS-CS, LDpred-funct, and SBayesRC), DeepU-PRS was among the strongest single methods on continuous traits, and ensembles combining DeepU-PRS with the linear baseline and SBayesRC further improved accuracy, indicating complementary predictive signal. These findings highlight the value of modeling nonlinear and uncertain regulatory effects to advance PRS prediction.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13671236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of bioinformatics competency framework to address labor market demands in Thailand.","authors":"Pathacha Suksakit, Dumnoensun Pruksakorn, Kamonnut Singkhamanan, Surasak Sangkhathat, Yotsawat Pomyen, Tanaporn Uengwetwanit, Saowalak Kalapanulak, Treenut Saithong, Weerayuth Kittichotirat, Teeraphan Laomettachit, Kanthida Kusonmano, Raphatphorn Navakanitworakul, Arinthip Thamchaipenet, Kanphavarith Pongthanatbutsadeewon, Sivamoke Dissook, Patcharawadee Thongkumkoon, Supapon Cheevadhanarak, Jutarop Phetcharaburanin, Peerut Chienwichai, Nuttinee Teerakulkittipong, Piroon Jenjaroenpun, Teerasak E-Kobon, Wanna Chetruengchai, Monnat Pongpanich, Apiwat Sangphukieo","doi":"10.1093/bib/bbag561","DOIUrl":"10.1093/bib/bbag561","url":null,"abstract":"<p><p>Bioinformatics plays a pivotal role in advancing healthcare, agriculture, and biotechnology. However, in Thailand, expectations regarding workforce demand and the core competencies of bioinformatics graduates remain insufficiently defined. Survey findings reveal that the demand for bioinformatics graduates is predominantly concentrated in the public sector, with comparatively limited demand from the private sector. Furthermore, there are differences between the public and private sectors in terms of the skill sets expected of bioinformaticians. To address these gaps, the Bioinformatics Association in Thailand, supported by Hub of Talents for Thailand Bioinformatics Research Network, National Research Council of Thailand, has developed a national competency framework for bioinformatics. This framework is intended to standardize graduate development and align educational outcomes with labor market needs. It encompasses four core domains: knowledge, skills, ethics, and character. This framework is informed by the Ministry of Higher Education, Science, Research and Innovation, together with expert consultations and public hearings involving key stakeholders. The proposed framework provides a structured foundation for cultivating a workforce equipped with both theoretical expertise and practical capabilities. Its implementation is expected to strengthen the national bioinformatics ecosystem and enhance the global competitiveness of Thai bioinformatics graduates.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149164290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in spatial transcriptomics: a review of methods, datasets, and directions.","authors":"Neelam Sharma, Rabia Şen, Cemal Erdem","doi":"10.1093/bib/bbag569","DOIUrl":"10.1093/bib/bbag569","url":null,"abstract":"<p><p>Spatial transcriptomics (STs) measures gene expression while retaining the location of transcripts or cells within tissue sections. Unlike bulk RNA sequencing, which averages expression across cell populations, and single-cell RNA sequencing, which generally requires tissue dissociation, ST links molecular profiles to tissue architecture. This spatial information supports the analysis of tissue heterogeneity, cellular organization, and potential intercellular interactions. The spatially resolved nature of ST has unlocked diverse applications in fields such as cancer, neuroscience, reproductive biology, and plant biology. In parallel, the development of publicly accessible databases and repositories has facilitated large-scale, multi-cohort comparisons and benchmarking. The rapid expansion and increasing complexity of these datasets have driven the adoption of machine learning (ML), deep learning (DL), and artificial intelligence (AI) techniques. These computational tools support a range of tasks, including data pre-processing, image segmentation, spatial domain identification, and pattern recognition. Moreover, they are instrumental in integrative multi-omics analyses and predictive modeling. This AI-focused review provides necessary background on ST technologies and databases and evaluates the use of statistical, ML, and DL methods across the analytical workflow, from image and sequencing-data preprocessing to spatial analysis and LLM-assisted interpretation. We compile key resources related to datasets, AI-driven analytical frameworks, and ST data analysis pipelines, and discuss current challenges, limitations, and future directions for AI-based tools in spatial omics.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149219801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: STORM: spatial transcriptomics optimization by resolution via matrix factorization.","authors":"","doi":"10.1093/bib/bbag604","DOIUrl":"10.1093/bib/bbag604","url":null,"abstract":"","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13681174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149227320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WDGTA: a wavelet-denoised graph attention framework for spatial transcriptomics deconvolution.","authors":"Hao Zhu, Chenggong Ma, Zhenchun Ran, Xumeng Liu, Haobo Tang, Tongquan Wu, Xinglong Wu, Yicheng Xie","doi":"10.1093/bib/bbag584","DOIUrl":"10.1093/bib/bbag584","url":null,"abstract":"<p><p>Spatial transcriptomics (ST) facilitates the spatial resolution of gene expression profiling; however, many widely used spot-based sequencing platforms capture transcripts from multiple neighboring cells at each location, producing mixed profiles that require accurate cell-type deconvolution. The wavelet-denoised graph attention framework for spatial transcriptomics deconvolution (WDGTA), a graph attention-based framework for ST cell-type deconvolution, was developed and validated on five datasets from two species and four organs. Through the integration of real with pseudo-ST spots, WDGTA introduces a wavelet-based multiscale denoising and reconstruction module, constructs a weighted topological graph using random projection forests, and applies multi-head graph attention to infer cell-type proportions in spatial spots. WDGTA achieved optimal performance in comparison with seven established deconvolution methods. Ablation and discrete wavelet transform (DWT)-specific analyses confirmed the contributions of wavelet denoising, weighted graph construction, and semi-supervised learning. Furthermore, DWT performance remained stable after feature-order perturbation. In mouse brain, cerebellum and ultraviolet radiation (UVR) skin data, WDGTA recovered biologically consistent spatial cell-type patterns and demonstrated robust cross-platform applicability and improved resolution of low-abundance melanocyte signals. Furthermore, it accurately captured melanocyte-associated spatial changes under conditions of UVR exposure and drug intervention. WDGTA provides a unified framework for ST deconvolution across the evaluated tissue contexts. In UVR-exposed skin, it resolved localized melanocyte-associated patterns that were concordant with marker-gene evidence and detected changes associated with UVR exposure and drug treatment. These findings support its use for characterizing spatial cell-type organization and low-abundance cell populations in heterogeneous tissues.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13679754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}