Briefings in bioinformatics最新文献

{"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":"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":"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}

{"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":"Data processing in proteomics and metabolomics: liquid chromatography-mass spectrometry-based omics with divergent computational paradigms.","authors":"Amy Zheng, Bin Liao, Liang Zhang, Tao Huan","doi":"10.1093/bib/bbag591","DOIUrl":"https://doi.org/10.1093/bib/bbag591","url":null,"abstract":"<p><p>Proteomics and metabolomics are two foundational pillars of omics research. Proteomics focuses on the large-scale, comprehensive studies of the entire set of proteins encoded by the genome. Metabolomics targets the systematic study of chemically diverse small molecules that represent the integrated biochemical output of cellular processes and organismal physiology. Although both fields rely heavily on liquid chromatographic separation coupled with mass spectrometry (LC-MS), their fundamental chemical and biological differences shape every stage of data processing and interpretation. In this review, we move beyond a descriptive comparison of existing pipelines and develop a task-level framework to understand why proteomics and metabolomics have evolved different bioinformatic paradigms despite sharing the same analytical platform. We examine how the degree of prior molecular knowledge shapes feature extraction, identification, confidence assessment, and quantification. We show that the constrained, sequence-defined nature of the proteome enables database-driven peptide identification, rigorous false discovery rate control, and increasingly standardized quantitative workflows. In contrast, we highlight how the open, chemically heterogeneous, and incompletely enumerated metabolome has required peak-shape-based feature detection, retention-time and mass-to-charge ratio-based alignment, extensive signal correction, and confidence-tiered structural annotation. By examining these workflows side by side, we identify shared challenges and field-specific bottlenecks and discuss opportunities for cross-fertilization between proteomics and metabolomics. Ultimately, this review provides a framework for an in-depth understanding of why two LC-MS-based omics fields have developed divergent computational solutions and how future bioinformatic advances may benefit both communities.</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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149262787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phage bioinformatics tools: a review of computational approaches for bacteriophage research.","authors":"Sean Jia Le Pang, Soon Keong Wee, Eric Peng Huat Yap","doi":"10.1093/bib/bbag494","DOIUrl":"10.1093/bib/bbag494","url":null,"abstract":"<p><p>Rising clinical interest in phage therapy and the exponential growth of metagenomic sequence catalogues have driven a rapid expansion of bacteriophage bioinformatics. More than 80 dedicated tools, mostly published since 2020, now span identification, assembly, annotation, taxonomy, lifestyle prediction, defence-system detection, and host prediction. Aimed at experienced practitioners and developers, this review synthesizes the field through the lens of three successive computational paradigms: sequence homology, bounded by database completeness; machine learning, constrained by labelled training data; and foundation models, which now achieve Matthews correlation coefficients above 0.95 in identification tasks and, through structure-informed prediction, raise functional annotation to over half of phage genes. Furthermore, we map the upstream components, namely, gene callers, homology engines, protein language models, and structural search tools, that underpin most downstream pipelines, exposing shared infrastructure and ecosystem-level fragility when dependencies change. To translate this into practice, we propose web-based and command-line reference workflows calibrated to user expertise and sample types. Finally, we set an agenda for the next wave of tool development. Roughly half of phage genes still resist functional annotation despite structural methods; no broadly generalizable strain-level host predictor exists for phage therapy; varying true-positive rates (0%-97%) underscore the absence of standardized community benchmarks analogous to Critical Assessment of Structure Prediction or Critical Assessment of Metagenome Interpretation. As generative genome models begin designing synthetic phages, progress will depend less on producing standalone tools than on rigorous evaluation, interoperable infrastructure, and clinically meaningful prediction targets.</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/PMC13577670/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148975790","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":"lagCI enables inference of temporal causal relationships from dense multi-omic time series.","authors":"Yifei Ge, Shunpeng Bai, Zirui Qiang, Yijiang Liu, Yitong Wu, Xiaotao Shen","doi":"10.1093/bib/bbag464","DOIUrl":"10.1093/bib/bbag464","url":null,"abstract":"<p><p>Inferring causal relationships from time-series data is critical for uncovering the dynamics of biological regulation. However, in multi-omics studies, this task is often hampered by sparse temporal sampling and the limitations of existing methods. To address this, we developed Lagged-Correlation Based Causal Inference (lagCI), a computational framework designed to identify time-lagged associations by combining comprehensive lag-correlation profiling with a robust statistical filtering scheme. Rather than relying on simple cross-correlation, lagCI analyzes the entire correlation profile and applies a quality-scoring system to filter out spurious associations that often plague high-dimensional datasets. We first tested lagCI on wearable physiological data, where it successfully captured the well-known causal link between physical activity and heart rate, even accounting for variations in lag times between individuals. Moving to high-frequency human multi-omics, we used lagCI to build a directed network of 1624 molecules connected by over 157 000 predicted interactions. This network recapitulated established biological relationships, including cytokine-hormone crosstalk, and highlighted molecular hubs that may coordinate the timing of metabolic and immune responses. Overall, lagCI provides a data-driven way to extract temporal insights from dense longitudinal omics. The tool is available as an R package with multiple interfaces to support use by both bioinformaticians and clinical researchers.</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/PMC13557713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148918664","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":"PGS-GS: a framework integrating polygenic scores and genomic selection in animal breeding.","authors":"Jinbu Wang, Lili Du, Zhida Zhao, Li Qian, Keanning Li, Shiyuan Qiu, Meng Mao, Mang Liang, Zezhao Wang, Hongwei Li, Yan Chen, Bo Zhu, Caihong Zheng, Xue Gao, Lingyang Xu, Lupei Zhang, Junya Li, Huijiang Gao","doi":"10.1093/bib/bbag397","DOIUrl":"10.1093/bib/bbag397","url":null,"abstract":"<p><p>Genomic prediction has become a central paradigm in biology, enabling quantitative inference of genetic contributions to complex traits across humans, animals, and plants. Although genomic research in human genetics and animal breeding shares a highly homologous methodological foundation, significant barriers persist in their analytical paradigms and application scenarios. This study aims to promote cross-disciplinary integration by introducing human-derived polygenic scores (PGS) algorithms into animal genomic selection (GS) and proposing a PGS-GS framework with a preliminary weighting-based implementation. We systematically benchmarked the predictive performance and computational efficiency of 20 algorithms, including classical linear models, machine learning, PGS, and PGS-GS using both array and whole-genome sequencing (WGS) data across four major agricultural species: beef cattle, sheep, pigs, and chickens. Our results demonstrate that PGS and PGS-GS algorithms achieve predictive accuracy competitive with genomic best linear unbiased prediction (GBLUP) while offering markedly higher computational efficiency. Moreover, incorporating PGS-derived prior information into weighted linear and non-linear models outperformed conventional weighted GBLUP. The results provide empirical evidence to inform algorithm selection and highlight the potential of integrating human-derived PGS methodologies into animal genomic prediction frameworks.</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/PMC13533199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148873146","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}