Genomics, proteomics & bioinformatics最新文献

APR-Score: Identification and Scoring of Aggregation-prone Regions in Protein Sequences. APR-Score：蛋白质序列中易聚集区域的鉴定和评分。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-10-19 DOI: 10.1093/gpbjnl/qzag104

Rahul Kaushik, Thomas Launey

{"title":"APR-Score: Identification and Scoring of Aggregation-prone Regions in Protein Sequences.","authors":"Rahul Kaushik, Thomas Launey","doi":"10.1093/gpbjnl/qzag104","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag104","url":null,"abstract":"Protein aggregation is a critical phenomenon associated with numerous neurodegenerative and systemic diseases. Understanding the propensity of proteins to aggregate is essential for unraveling the molecular basis of these disorders and for the design and engineering of novel proteins, as well as for modulating the activity/stability of enzymatic proteins. Here, we present APR-Score, a novel machine learning-based computational method designed to identify aggregation-prone regions within protein sequences. APR-Score leverages a combination of sequence-based features to predict regions of proteins that are prone to aggregation. It harnessed the information embedded in compiled sequence and structural features to achieve state-of-the-art accuracy. The method was evaluated through rigorous cross-validation on the training dataset and further validated on an independent test dataset. APR-Score demonstrated robustness and reliability in distinguishing aggregation-prone regions from non-aggregating ones, achieving a Matthews correlation coefficient (MCC) of 0.77, precision of 0.89, and F1-Score of 0.87. This tool offers significant value for researchers investigating protein aggregation-related diseases, as it can expedite the identification of aggregation-prone regions, thereby aiding the development of targeted therapies and diagnostic tools. Moreover, computational protein design and engineering efforts can be facilitated by APR-Score-based identification and screening of aggregation-prone protein sequences. APR-Score is available at https://github.com/TII-BRC/APR-Score.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149221295","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}

引用次数: 0

Genomic Insights into Cold Adaptation and Demographic History of Amur-Heilong River Basin Populations. 黑龙江-黑龙江流域种群冷适应与人口历史的基因组学研究

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-10-14 DOI: 10.1093/gpbjnl/qzag106

Siqin Chen, Hao Chen, Zhaoqing Yang, Yan Lu, Yang Gao, Kuiting Tao, Yan Zheng, Jingchun Luo, Li Jin, Jiayou Chu, Shuhua Xu

{"title":"Genomic Insights into Cold Adaptation and Demographic History of Amur-Heilong River Basin Populations.","authors":"Siqin Chen, Hao Chen, Zhaoqing Yang, Yan Lu, Yang Gao, Kuiting Tao, Yan Zheng, Jingchun Luo, Li Jin, Jiayou Chu, Shuhua Xu","doi":"10.1093/gpbjnl/qzag106","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag106","url":null,"abstract":"Human adaptation to extreme cold has been studied predominantly in Arctic populations, leaving the genetic basis of cold resilience in non-Arctic high-latitude regions insufficiently understood. The Amur-Heilong River Basin (AR) in Northeast Asia represents one of the coldest inhabited regions in East Asia, yet its long-term resident populations remain underexplored at the genomic level. Here, we compiled high-coverage whole-genome sequences for 78 individuals representing three indigenous AR populations (Daur, Evenki, and Oroqen) to investigate their demographic history and genetic adaptation to cold environments. Population genomic analyses revealed distinct ancestral compositions, with Evenki and Oroqen showing closer genetic ties to Siberian populations, while Daur exhibited greater admixture with northern East Asians. Demographic reconstructions identified shared and prolonged bottlenecks across AR populations between ∼ 10,000 and 3000 years ago. Genome-wide selection scans uncovered candidate loci associated with cold adaptation, including SORBS2, PRKAG2, KCNMA1, PRKG1, NTM, CHST11, OCA2, RYR3, OTOA, and PKIG, many showing strong correlations between allele frequency and cold-related climatic variables. Notably, a Neanderthal-derived allele at CHST11 (rs11112181) likely contributed to cold-adaptive variation. Functional enrichment analyses suggest that cold adaptation in AR populations may preferentially involve neuro-sensory regulation and tissue integrity, rather than the classical metabolic thermogenesis pathways emphasized in Arctic populations. Integrating population history, selection signals, and archaic introgression, our results indicate that cold resilience in the AR reflects a composite of ancestral adaptation, demographic processes, and limited archaic contributions. These findings expand current understanding of human cold adaptation beyond the Arctic and highlight alternative evolutionary strategies in extreme environments.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187069","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}

引用次数: 0

Evaluating AlphaFold Tools and Related Scoring Functions for Protein-peptide Complex Prediction. 评估AlphaFold工具和相关评分功能的蛋白质-肽复合物预测。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-10-10 DOI: 10.1093/gpbjnl/qzag026

Negin Manshour, Jarett Zida Ren, Farzaneh Esmaili, Erik Bergstrom, Dong Xu 许东

{"title":"Evaluating AlphaFold Tools and Related Scoring Functions for Protein-peptide Complex Prediction.","authors":"Negin Manshour, Jarett Zida Ren, Farzaneh Esmaili, Erik Bergstrom, Dong Xu 许东","doi":"10.1093/gpbjnl/qzag026","DOIUrl":"10.1093/gpbjnl/qzag026","url":null,"abstract":"Determining the three-dimensional structures of protein-peptide complexes is crucial for elucidating biological processes and designing peptide-based drugs. Protein-peptide docking has become essential for predicting complex structures. AlphaFold-Multimer, ColabFold, and AlphaFold3 are groundbreaking tools that enhance protein-peptide docking accuracy. In this study, we evaluated these three tools for predicting protein-peptide complex structures in both template-based (TB) and template-free (TF) modes. AlphaFold-Multimer excels in TB predictions and performs moderately in TF scenarios in the prediction pool; however, TF outperforms TB in the first-ranked models. ColabFold demonstrates versatility in both TB and TF settings. AlphaFold3 generates high-quality structures for more proteins, but its medium-accuracy predictions are not as good as those of AlphaFold-Multimer using a large model pool. We also assessed the performance of various scoring functions in ranking predicted protein-peptide complex structures. While the scoring function built in AlphaFold demonstrates the best performance, some other scoring functions, e.g., FoldX-Stability and HADDOCK-mdscore, provide complementary information for model ranking. These findings suggest the potential for enhancing scoring functions targeting AlphaFold-based predictions by combining multiple scoring functions or using a consensus approach from many prediction models.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13653374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147517575","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}

引用次数: 0

Immunosuppressant-refractory Multifaceted Immune Dysregulations for Systemic Lupus Erythematosus. 系统性红斑狼疮的免疫抑制难治性多方面免疫失调。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-10-10 DOI: 10.1093/gpbjnl/qzag105

Chen-Rui Xia, Jun-Yan Qian, Yang Ding, Jiu-Liang Zhao, Qian Wang, Xin-Ping Tian, Xiao-Feng Zeng, Ge Gao, Meng-Tao Li

{"title":"Immunosuppressant-refractory Multifaceted Immune Dysregulations for Systemic Lupus Erythematosus.","authors":"Chen-Rui Xia, Jun-Yan Qian, Yang Ding, Jiu-Liang Zhao, Qian Wang, Xin-Ping Tian, Xiao-Feng Zeng, Ge Gao, Meng-Tao Li","doi":"10.1093/gpbjnl/qzag105","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag105","url":null,"abstract":"While the systemic lupus erythematosus (SLE), a severe autoimmune disease, is known to relapse frequently after treatment, none of the previous single-cell studies have examined the immune dynamics and their potential contribution to this relapse in the real clinical setting. To address this issue, here we employed single-cell multi-omics profiling to delineate immune repertoire dynamics during the immunosuppressant-based standard of care. Further analysis showed that the canonical therapy fails to restore various critical abnormalities, including the drop in mutational load of B and plasma cells, and the clonal expansion of CD4+ Th1 and CD8+ memory T cells. We also integrated our data with published SLE datasets to compile a comprehensive SLE immune atlas of about 1.6 million cells. Our study provides valuable data and sheds light on the underlying multifaceted unresolved dysregulation upon SLE immunosuppressant therapy under the real clinical setting; such dysregulation may further contribute to the high post-treatment relapse rate and thus call for additional target-specific treatment strategies.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149170094","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}

引用次数: 0

Distinct Genetic Determinants of Plasma Inflammatory Proteins in Tanzanian and Dutch Populations. 坦桑尼亚和荷兰人群血浆炎症蛋白的独特遗传决定因素。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-10-07 DOI: 10.1093/gpbjnl/qzag100

Caroline Amour, Collins K Boahen, Godfrey S Temba, Nadira Vadaq, Vesla Kullaya, Blandina T Mmbaga, Leo A B Joosten, Reginald A Kavishe, Mihai G Netea, Quirijn de Mast, Vinod Kumar

{"title":"Distinct Genetic Determinants of Plasma Inflammatory Proteins in Tanzanian and Dutch Populations.","authors":"Caroline Amour, Collins K Boahen, Godfrey S Temba, Nadira Vadaq, Vesla Kullaya, Blandina T Mmbaga, Leo A B Joosten, Reginald A Kavishe, Mihai G Netea, Quirijn de Mast, Vinod Kumar","doi":"10.1093/gpbjnl/qzag100","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag100","url":null,"abstract":"Understanding the genetic regulation of plasma inflammatory proteins is essential for bridging the gap between genetics and disease biology. While studies in European populations have highlighted significant genetic influences on inflammatory protein levels, African populations remain underrepresented in immunogenetic research. Here, we compare the genetic determinants of 72 inflammatory proteins quantified using the Olink Target 96 Inflammation panel in two cohorts from the Human Functional Genomics Project. The study included 286 healthy Tanzanians (146 males and 140 females) and 340 Dutch participants (175 males and 165 females). Genotyping was conducted using the Illumina Global Screening Array (GSA) for the Tanzanian cohort and the HumanOmniExpressExome-8 for the Dutch cohort. Our genome-wide protein quantitative trait locus (pQTL) analysis identified 10 genome-wide significant FDR-corrected pQTLs in each population, including a missense variant affecting cluster of differentiation 6 (CD6) concentrations, which is implicated in multiple autoimmune and inflammatory diseases. We identified three trans- and seven cis-pQTLs in the Tanzanian cohort, while three cis- and seven trans-pQTLs were identified in the Dutch cohort. However, we observed minimal overlap (< 2%) in pQTLs between the two populations, with colocalization analysis confirming only nine shared loci. Additionally, we replicated several pQTLs identified in the UK-BIOBANK study across African and European ancestries. These findings underscore the genetic diversity underlying inflammatory protein regulation and highlight the importance of population-specific studies to better understand immune function, disease susceptibility, and tailored health interventions across diverse ancestries.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149129776","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}

引用次数: 0

MTB-KB: A Curated Knowledgebase of Mycobacterium tuberculosis Related Studies. MTB-KB：结核分枝杆菌相关研究的策划知识库。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-10-01 DOI: 10.1093/gpbjnl/qzag102

Pan Li, Cuidan Li, Rongxi Zhu, Wenjing Sun, Hengyu Zhou, Zhuojing Fan, Liya Yue, Sijia Zhang, Xiaoyuan Jiang, Quan Luo, Jinying Han, Hairong Huang, Adong Shen, Bahetibieke Tuohetaerbaike, Jing Wang, Wenbao Zhang, Hao Wen, Haitao Niu, Congfan Bu, Zhang Zhang, Jingfa Xiao, Renjun Gao, Fei Chen

{"title":"MTB-KB: A Curated Knowledgebase of Mycobacterium tuberculosis Related Studies.","authors":"Pan Li, Cuidan Li, Rongxi Zhu, Wenjing Sun, Hengyu Zhou, Zhuojing Fan, Liya Yue, Sijia Zhang, Xiaoyuan Jiang, Quan Luo, Jinying Han, Hairong Huang, Adong Shen, Bahetibieke Tuohetaerbaike, Jing Wang, Wenbao Zhang, Hao Wen, Haitao Niu, Congfan Bu, Zhang Zhang, Jingfa Xiao, Renjun Gao, Fei Chen","doi":"10.1093/gpbjnl/qzag102","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag102","url":null,"abstract":"Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), has regained its position as the world's leading killer among infectious diseases. Despite extensive research progress across epidemiology, diagnosis, drug development, treatment regimens, vaccines, drug resistance, virulence factors, and immune mechanisms, MTB-related knowledge remains fragmented across thousands of publications, limiting its application in research and clinical practice. To address this gap, we present MTB-KB, a literature-curated knowledgebase that systematically integrates high-impact findings from eight major sections of TB research. The current release contains 75,170 associations from 1246 publications, covering 18,439 entities standardized using authoritative databases and WHO-endorsed classifications. A central feature is the interactive knowledge graph, which links cross-section associations to reveal and visualize MTB-host interactions, treatment strategies, and vaccine development opportunities. MTB-KB also provides a user-friendly interface with browsing, advanced search, and statistical visualization. Overall, by consolidating dispersed MTB knowledge into a structured and accessible platform, MTB-KB provides a valuable resource for researchers, clinicians, and policymakers, supporting both basic and clinical TB research, enabling evidence-based TB prevention, diagnosis, and treatment, and contributing to global elimination efforts. MTB-KB is accessible at https://ngdc.cncb.ac.cn/mtbkb/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149120821","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}

引用次数: 0

miRNA Isoform Spectrum Expansion by Overexpression May Affect Target Gene Silencing. 过表达miRNA亚型谱扩展可能影响靶基因沉默。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-09-30 DOI: 10.1093/gpbjnl/qzag101

Fabian Kern, Martin Hart, Lena Krammes, Caroline Diener, Ernesto Aparicio-Puerta, Pascal Hirsch, Nicole Ludwig, Tobias Fehlmann, Annika Engel, Claudia Fecher-Trost, Bastian Fromm, Veit Flockerzi, Eckart Meese, Andreas Keller

{"title":"miRNA Isoform Spectrum Expansion by Overexpression May Affect Target Gene Silencing.","authors":"Fabian Kern, Martin Hart, Lena Krammes, Caroline Diener, Ernesto Aparicio-Puerta, Pascal Hirsch, Nicole Ludwig, Tobias Fehlmann, Annika Engel, Claudia Fecher-Trost, Bastian Fromm, Veit Flockerzi, Eckart Meese, Andreas Keller","doi":"10.1093/gpbjnl/qzag101","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag101","url":null,"abstract":"MicroRNAs (miRNAs) regulate gene expression primarily through translational inhibition and mRNA degradation. Their functional complexity is increased by the presence of various mature isoforms (isomiRs). These isomiRs add diversity to miRNA targeting and regulation, yet their impact on cellular systems remains insufficiently understood. In this study, we systematically assessed the effects of miRNA overexpression on the isomiR repertoire of five miRNAs (mir-34a, mir-129, mir-873, mir-133b, mir-7) across four different human cell lines (HeLa, HEK293T, LUHMES, and SH-SY5Y). Our results reveal a pronounced heterogeneity of isomiR profiles, which expanded significantly under overexpression conditions, with up to 845 unique isoforms detected per miRNA. Through time-course and dose-response experiments, we observed that the isomiR diversity increased rapidly within 8 hours following transfection and was sensitive to the amount of miRNA precursor transfected. Proteomic analysis indicated that changes in the isomiR repertoire might affect the regulatory capacity of miRNAs, altering the amplitude and frequency of target repression. Our findings underscore the complexity of miRNA regulation and highlight potential pitfalls in interpreting overexpression experiments, which may artificially expand the isomiR landscape and consequently modify the targetome. This study calls for careful consideration of isomiR dynamics in miRNA research and therapeutic development.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149120893","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}

引用次数: 0

DEFB1 + Cancer Cell-IFI30+ Macrophage Crosstalk Drives Immunosuppressive Niche in Intrahepatic Cholangiocarcinoma. DEFB1 +癌细胞- ifi30 +巨噬细胞串扰驱动肝内胆管癌免疫抑制生态位

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-09-28 DOI: 10.1093/gpbjnl/qzag099

Guoliang Wang, Hang Meng, Meng Li, Xiangdong Fang, Weilong Zou, Hongzhu Qu

{"title":"DEFB1 + Cancer Cell-IFI30+ Macrophage Crosstalk Drives Immunosuppressive Niche in Intrahepatic Cholangiocarcinoma.","authors":"Guoliang Wang, Hang Meng, Meng Li, Xiangdong Fang, Weilong Zou, Hongzhu Qu","doi":"10.1093/gpbjnl/qzag099","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag099","url":null,"abstract":"Intrahepatic cholangiocarcinoma (ICC) is a rare but highly aggressive primary liver malignancy distinguished by a profoundly heterogeneous tumor microenvironment, which underlies its limited response to targeted and immune-based therapies. Deciphering the cellular composition and intercellular signaling within this complex ecosystem is critical to understanding ICC progression and to identifying actionable therapeutic targets. Here, we integrated single-cell RNA sequencing and spatial transcriptomics to construct a comprehensive cellular and transcriptional atlas of human ICC. Our analyses revealed a continuous trajectory of T-cell state transition from activation to exhaustion, with CD8 + proliferating T cells exhibiting two distinct exhaustion programs, namely terminal and progenitor-like exhaustion. Notably, DEFB1 + cholangiocytes and IFI30 + macrophages displayed a strong positive correlation across independent ICC cohorts and were found in close spatial proximity within the tumor microenvironment. Mechanistically, their interaction was predicted to involve the TGFB1-TGFBR1 signaling axis, which may contribute to tumor progression and immunotherapy resistance. Together, these findings delineate the cellular architecture and spatial organization of the ICC microenvironment, uncovering a key cholangiocyte-macrophage axis that shapes immune dysfunction and offers a potential therapeutic entry point to enhance immunotherapy efficacy in ICC.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149077126","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}

引用次数: 0

STCGCar: Graph Contrastive Learning with Reliable Augmentation for Spatial Transcriptomics Clustering. STCGCar：基于可靠增强的空间转录组聚类图对比学习。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-09-17 DOI: 10.1093/gpbjnl/qzag098

Lihong Peng, Long Yang, Min Chen, Geng Tian, Xin Liu, Zongzheng Bai, Jialiang Yang

{"title":"STCGCar: Graph Contrastive Learning with Reliable Augmentation for Spatial Transcriptomics Clustering.","authors":"Lihong Peng, Long Yang, Min Chen, Geng Tian, Xin Liu, Zongzheng Bai, Jialiang Yang","doi":"10.1093/gpbjnl/qzag098","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag098","url":null,"abstract":"Accurately identifying spatial domains based on spatial transcriptomics (ST) data can greatly promote our understanding of cellular composition and tissue organization. While graph neural networks (GNNs) have shown significant advancements in spatial clustering, they tend to be insensitive to noisy edges, leading to intersections among identified spatial domains. Here, we introduce a GNN-based ST Clustering framework, called STCGCar, utilizing a Graph Contrastive learning model with reliable augmentation and redundancy reduction strategies. The framework begins by creating an enhanced view through a reversible network after data preprocessing. Subsequently, low-dimensional embeddings of spots are learned using a multi-head attention mechanism. Moreover, a redundancy reduction strategy is employed to reduce information redundancy in potential feature space. Finally, spatial domains are delineated through K-means clustering, followed by downstream analysis. STCGCar was benchmarked against six state-of-the-art clustering methods (i.e., Seurat, conST, CCST, STAGATE, DeepST, and GraphST) using five 10x Visium datasets, a STARmap dataset, and two Stereo-seq mouse embryo datasets. Through evaluation with adjusted rand index (ARI), normalized mutual information (NMI), and four internal indicators, it demonstrated outstanding clustering performance compared to other methods on four labeled and four unlabeled datasets. Additionally, STCGCar accurately identified spatial domains and discovered three potential differentially expressed genes (AZGP1, CD24, and CCND1) in human breast cancer tissues. Furthermore, it effectively delineated layer structures in human DLPFC and adult mouse brain tissues. STCGCar is a powerful tool for spatial domain identification, showcasing its effectiveness and scalability on diverse datasets. It is freely available at https://github.com/plhhnu/STCGCar.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148993181","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}

引用次数: 0

MACS3: A Peak-calling Platform for Bulk and Single-cell Regulatory Genomics. MACS3：大规模和单细胞调控基因组学的峰值调用平台。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-09-16 DOI: 10.1093/gpbjnl/qzag097

Philippa Doherty, Qiang Hu, Zihan Zhuang, Hong Zhang, Sai Charan Penikalapati, Song Liu, Tao Liu

{"title":"MACS3: A Peak-calling Platform for Bulk and Single-cell Regulatory Genomics.","authors":"Philippa Doherty, Qiang Hu, Zihan Zhuang, Hong Zhang, Sai Charan Penikalapati, Song Liu, Tao Liu","doi":"10.1093/gpbjnl/qzag097","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag097","url":null,"abstract":"Since the original publication of Model-based Analysis for ChIP-Seq (MACS), the software has been widely used to identify enriched genomic regions in ChIP-seq, ATAC-seq, CUT&RUN, DNase-seq, and related regulatory genomics assays. Over the years, MACS has evolved substantially, with MACS version 3 (MACS3) now serving as the actively maintained implementation. MACS3 preserves the core MACS framework for fragment pileup, dynamic local background noise, statistical enrichment testing, and peak refinement, while adding functionality needed for contemporary bulk and single-cell workflows. It supports conventional bulk peak calling, paired-end and fragment-based file formats, modular signal processing, direct analysis of single-cell ATAC-seq fragment files, barcode-restricted pseudobulk and cluster-level peak calling, specialized ATAC-seq and variant-calling modules, as well as command-line and programmatic interfaces. MACS3 is distributed through standard software channels and supported by continuous testing across operating systems, Python versions, and CPU architectures. Here we describe the architecture, current capabilities, and recommended use of MACS3, providing an updated reference for applying the MACS framework in contemporary bulk and single-cell regulatory genomics workflows. MACS3 is open-source software available at https://github.com/macs3-project/MACS.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148979096","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}

引用次数: 0