Genomics, proteomics & bioinformatics最新文献_第5页

TRAIT: A Comprehensive Database for T-cell Receptor-antigen Interactions. TRAIT: t细胞受体-抗原相互作用的综合数据库。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2025-07-11 DOI: 10.1093/gpbjnl/qzaf033

Mengmeng Wei 魏蒙蒙, Jingcheng Wu 吴静成, Shengzuo Bai 白晟佐, Yuxuan Zhou 周宇轩, Yichang Chen 陈弈菖, Xue Zhang 张雪, Wenyi Zhao 赵文艺, Ying Chi 迟颖, Gang Pan 潘纲, Feng Zhu 朱峰, Shuqing Chen 陈枢青, Zhan Zhou 周展

{"title":"TRAIT: A Comprehensive Database for T-cell Receptor-antigen Interactions.","authors":"Mengmeng Wei 魏蒙蒙, Jingcheng Wu 吴静成, Shengzuo Bai 白晟佐, Yuxuan Zhou 周宇轩, Yichang Chen 陈弈菖, Xue Zhang 张雪, Wenyi Zhao 赵文艺, Ying Chi 迟颖, Gang Pan 潘纲, Feng Zhu 朱峰, Shuqing Chen 陈枢青, Zhan Zhou 周展","doi":"10.1093/gpbjnl/qzaf033","DOIUrl":"10.1093/gpbjnl/qzaf033","url":null,"abstract":"Comprehensive and integrated resources on interactions between T-cell receptors (TCRs) and antigens are still lacking for adoptive T-cell-based immunotherapies, highlighting a significant gap that must be addressed to fully understand the mechanisms of antigen recognition by T cells. In this study, we present the T-cell receptor-antigen interaction database (TRAIT), a comprehensive database that profiles the interactions between TCRs and antigens. TRAIT stands out due to its comprehensive description of TCR-antigen interactions by integrating sequences, structures, and affinities. It provides millions of experimentally validated TCR-antigen pairs, resulting in an exhaustive landscape of antigen-specific TCRs. Notably, TRAIT emphasizes single-cell omics as a major reliable data source for TCR-antigen interactions and includes millions of reliable non-interactive TCRs. Additionally, it thoroughly demonstrates the interactions between mutations of TCRs and antigens, thereby benefiting affinity optimization of engineered TCRs as well as vaccine design. TCRs on clinical trials are innovatively provided. With the significant efforts made toward elucidating the complex interactions between TCRs and antigens, TRAIT is expected to ultimately contribute superior algorithms and substantial advancements in the field of T-cell-based immunotherapies. TRAIT is freely accessible at https://pgx.zju.edu.cn/traitdb.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055122","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

LCORL and STC2 Variants Increase Body Size and Growth Rate in Cattle and Other Animals. LCORL和STC2变异增加了牛和其他动物的体型和生长速度。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2025-07-11 DOI: 10.1093/gpbjnl/qzaf025

Fengting Bai 白凤庭, Yudong Cai 蔡钰东, Min Qiu 邱敏, Chen Liang 梁晨, Linqian Pan 潘麟茜, Yayi Liu 刘雅怡, Yanshuai Feng 冯衍帅, Xuesha Cao 曹雪莎, Qimeng Yang 杨启蒙, Gang Ren 任刚, Shaohua Jiao 焦少华, Siqi Gao 高思祺, Meixuan Lu 卢美轩, Xihong Wang 王喜宏, Rasmus Heller, Johannes A Lenstra, Yu Jiang 姜雨

{"title":"LCORL and STC2 Variants Increase Body Size and Growth Rate in Cattle and Other Animals.","authors":"Fengting Bai 白凤庭, Yudong Cai 蔡钰东, Min Qiu 邱敏, Chen Liang 梁晨, Linqian Pan 潘麟茜, Yayi Liu 刘雅怡, Yanshuai Feng 冯衍帅, Xuesha Cao 曹雪莎, Qimeng Yang 杨启蒙, Gang Ren 任刚, Shaohua Jiao 焦少华, Siqi Gao 高思祺, Meixuan Lu 卢美轩, Xihong Wang 王喜宏, Rasmus Heller, Johannes A Lenstra, Yu Jiang 姜雨","doi":"10.1093/gpbjnl/qzaf025","DOIUrl":"10.1093/gpbjnl/qzaf025","url":null,"abstract":"Natural variants can significantly improve growth traits in livestock and serve as safe targets for gene editing, thus being applied in animal molecular design breeding. However, such safe and large-effect mutations are severely lacking. Using ancestral recombination graphs, we investigated recent selection signatures in beef cattle breeds, pinpointing sweep-driving variants in the LCORL and STC2 loci with notable effects on body size and growth rate. The ACT-to-A frameshift mutation in LCORL occurs mainly in central-European cattle, and stimulates growth. Remarkably, convergent truncating mutations were also found in commercial breeds of sheep, goats, pigs, horses, dogs, rabbits, and chickens. In the STC2 gene, we identified a missense mutation (A60P) located within the conserved region across vertebrates. We validated the two natural mutations in gene-edited mouse models, where both variants in homozygous carriers significantly increase the average weight by 11%. Our findings provide insights into a seemingly recurring gene target of body size enhancing truncating mutations across domesticated species, and offer valuable targets for gene editing-based breeding in animals.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448305/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652871","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

Characterization of Tumor Antigens from Multi-omics Data: Computational Approaches and Resources. 基于多组学数据的肿瘤抗原表征：计算方法和资源。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2025-07-11 DOI: 10.1093/gpbjnl/qzaf001

Yunzhe Wang 王韫哲, James Wengler, Yuzhu Fang 房钰竹, Joseph Zhou, Hang Ruan, Zhao Zhang, Leng Han

{"title":"Characterization of Tumor Antigens from Multi-omics Data: Computational Approaches and Resources.","authors":"Yunzhe Wang 王韫哲, James Wengler, Yuzhu Fang 房钰竹, Joseph Zhou, Hang Ruan, Zhao Zhang, Leng Han","doi":"10.1093/gpbjnl/qzaf001","DOIUrl":"10.1093/gpbjnl/qzaf001","url":null,"abstract":"Tumor-specific antigens, also known as neoantigens, have potential utility in anti-cancer immunotherapy, including immune checkpoint blockade (ICB), neoantigen-specific T cell receptor-engineered T (TCR-T), chimeric antigen receptor T (CAR-T), and therapeutic cancer vaccines (TCVs). After recognizing presented neoantigens, the immune system becomes activated and triggers the death of tumor cells. Neoantigens may be derived from multiple origins, including somatic mutations (single nucleotide variants, insertions/deletions, and gene fusions), circular RNAs, alternative splicing, RNA editing, and polymorphic microbiomes. An increasing amount of bioinformatics tools and algorithms are being developed to predict tumor neoantigens derived from different sources, which may require inputs from different multi-omics data. In addition, calculating the peptide-major histocompatibility complex (MHC) affinity can aid in selecting putative neoantigens, as high binding affinities facilitate antigen presentation. Based on these approaches and previous experiments, many resources have been developed to reveal the landscape of tumor neoantigens across multiple cancer types. Herein, we summarize these tools, algorithms, and resources to provide an overview of computational analysis for neoantigen discovery and prioritization, as well as the future development of potential clinical utilities in this field.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143018219","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

Deciphering Complex Interactions Between LTR Retrotransposons and Three Papaver Species Using LTR_Stream. 利用LTR_Stream破译LTR反转录转座子与三种木瓜物种之间的复杂相互作用。

Genomics, proteomics & bioinformatics Pub Date : 2025-07-08 DOI: 10.1093/gpbjnl/qzaf061

Tun Xu, Stephen J Bush, Yizhuo Che, Huanhuan Zhao, Tingjie Wang, Peng Jia, Songbo Wang, Peisen Sun, Pengyu Zhang, Shenghan Gao, Yu Xu, Chengyao Wang, Ningxin Dang, Yong E Zhang, Xiaofei Yang, Kai Ye

{"title":"Deciphering Complex Interactions Between LTR Retrotransposons and Three Papaver Species Using LTR_Stream.","authors":"Tun Xu, Stephen J Bush, Yizhuo Che, Huanhuan Zhao, Tingjie Wang, Peng Jia, Songbo Wang, Peisen Sun, Pengyu Zhang, Shenghan Gao, Yu Xu, Chengyao Wang, Ningxin Dang, Yong E Zhang, Xiaofei Yang, Kai Ye","doi":"10.1093/gpbjnl/qzaf061","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf061","url":null,"abstract":"Long terminal repeat retrotransposons (LTR-RTs), a major type of class I transposable elements, are the most abundant repeat element in plants. The study of the interactions between LTR-RTs and the host genome relies on high-resolution characterization of LTR-RTs. However, for non-model species, this remains a challenge. To address this, we developed LTR_Stream for sub-lineage clustering of LTR-RTs in specific or closely related species, providing higher precision than current database-based lineage-level clustering. Using LTR_Stream, we analysed Retand LTR-RTs in three Papaver species. Our findings show that high-resolution clustering reveals complex interactions between LTR-RTs and the host genome. For instance, we found autonomous Retand elements could spread among the ancestors of different subgenomes, like retroviruses pandemics, enriching genetic diversity. Additionally, we identified that specific truncated fragments containing transcription factors motifs such as TCP and bZIP may contribute to generation of novel topologically associating domain like (TAD-like) boundaries. Notably, our pre-allopolyploidization and post-allopolyploidization comparisons show that these effects diminished after allopolyploidization, suggesting that allopolyploidization may be one of the mechanisms by which Papaver species cope with LTR-RTs. We demonstrated the potential application of LTR_Stream and provided a reference case for studying the interactions between LTR-RTs and the host genome in non-model plant species.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144677056","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

ScReNI: Single-cell Regulatory Network Inference Through Integrating scRNA-seq and scATAC-seq Data. 通过整合scRNA-seq和scATAC-seq数据推断单细胞调节网络。

Genomics, proteomics & bioinformatics Pub Date : 2025-07-01 DOI: 10.1093/gpbjnl/qzaf060

Xueli Xu, Yanran Liang, Miaoxiu Tang, Jiongliang Wang, Xi Wang, Yixue Li, Jie Wang

{"title":"ScReNI: Single-cell Regulatory Network Inference Through Integrating scRNA-seq and scATAC-seq Data.","authors":"Xueli Xu, Yanran Liang, Miaoxiu Tang, Jiongliang Wang, Xi Wang, Yixue Li, Jie Wang","doi":"10.1093/gpbjnl/qzaf060","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf060","url":null,"abstract":"Each cell possesses a unique gene regulatory network. However, limited methods exist for inferring cell-specific regulatory networks, particularly through the integration of single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data. Herein, we develop a novel algorithm, named single-cell regulatory network inference (ScReNI), for inferring gene regulatory networks at the single-cell level. In ScReNI, the nearest neighbors algorithm is utilized to establish the neighboring cells for each cell, where nonlinear regulatory relationships between gene expression and chromatin accessibility are inferred through a modified random forest. ScReNI is designed to analyze both paired and unpaired datasets for scRNA-seq and scATAC-seq. ScReNI demonstrates more accurate regulatory relationships and outperforms existing cell-specific network inference methods in network-based cell clustering. ScReNI also shows superior performance in inferring cell type-specific regulatory networks through integrating gene expression and chromatin accessibility. Importantly, ScReNI offers the unique function of identifying cell-enriched regulators based on each cell-specific network. Overall, ScReNI facilitates the inferences of cell-specific regulatory networks and cell-enriched regulators, providing insights into single-cell regulatory mechanisms of diverse biological processes. ScReNI is available at https://github.com/Xuxl2020/ScReNI.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546648","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

LEGEND: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data. LEGEND：在多模态转录组测序数据中识别共表达基因。

Genomics, proteomics & bioinformatics Pub Date : 2025-07-01 DOI: 10.1093/gpbjnl/qzaf056

Tao Deng, Mengqian Huang, Kaichen Xu, Yan Lu, Yucheng Xu, Siyu Chen, Nina Xie, Qiuyue Tao, Hao Wu, Xiaobo Sun

{"title":"LEGEND: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data.","authors":"Tao Deng, Mengqian Huang, Kaichen Xu, Yan Lu, Yucheng Xu, Siyu Chen, Nina Xie, Qiuyue Tao, Hao Wu, Xiaobo Sun","doi":"10.1093/gpbjnl/qzaf056","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf056","url":null,"abstract":"Identifying co-expressed genes across tissue domains and cell types is essential for revealing co-functional genes involved in biological or pathological processes. While both single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomic (SRT) data offer insights into gene co-expression patterns, current methods typically utilize either data type alone, potentially diluting the co-functionality signals within co-expressed gene groups. To bridge this gap, we introduce muLtimodal co-Expressed GENes finDer (LEGEND), a novel computational method that integrates scRNA-seq and SRT data for identifying groups of co-expressed genes at both cell type and tissue domain levels. LEGEND employs an innovative hierarchical clustering algorithm designed to maximize intra-cluster redundancy and inter-cluster complementarity, effectively capturing more nuanced patterns of gene co-expression and spatial coherence. Enrichment and co-function analyses further showcase the biological relevance of these gene clusters, and their utilities in exploring context-specific novel gene functions. Notably, LEGEND can reveal shifts in gene-gene interactions under different conditions, furnishing insights for disease-associated gene crosstalk. Moreover, LEGEND can be utilized to enhance the annotation accuracy of both spatial spots in SRT and single cells in scRNA-seq, and pioneers in identifying genes with designated spatial expression patterns. LEGEND is available at https://github.com/ToryDeng/LEGEND.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546647","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

Lineage-associated Human Divergently-paired Genes Exhibit Structural and Regulatory Characteristics. 谱系相关的人类差异配对基因表现出结构和调控特征。

Genomics, proteomics & bioinformatics Pub Date : 2025-06-26 DOI: 10.1093/gpbjnl/qzaf058

Guangya Duan, Sisi Zhang, Bixia Tang, Jingfa Xiao, Zhang Zhang, Peng Cui, Jun Yu, Wenming Zhao

{"title":"Lineage-associated Human Divergently-paired Genes Exhibit Structural and Regulatory Characteristics.","authors":"Guangya Duan, Sisi Zhang, Bixia Tang, Jingfa Xiao, Zhang Zhang, Peng Cui, Jun Yu, Wenming Zhao","doi":"10.1093/gpbjnl/qzaf058","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf058","url":null,"abstract":"Divergently-paired genes (DPGs) are minimal co-transcriptional units of clustered genes, representing over 10% of human genes. Our previous studies have shown that vertebrate DPGs are highly conserved compared to those from invertebrates. Three critical questions remain: (1) which DPGs are conserved across vertebrates, especially among mammals and primates? (2) to what extent and precision do these paired promotors share their sequences mechanistically and stringently? and (3) how are human DPGs distributed over selected primate lineages, and what are their possible biological functional consequences? There are 1399 human DPGs (approximately 12% of all human protein-coding genes), of which 1136, 1118, 925, and 830 human DPGs show conservation when compared to selected primates, mammals, avians, and fishs, respectively. DPGs are not only functionally enriched toward direct protein-DNA interactions and cell cycle synchronization, but also exhibit lineage association, narrow in principle toward synchronization of certain core molecular mechanisms and cellular processes. Second, the inter-transcription start sites (inter-TSS) distances affect both co-expression strength and disparity between the two genes of a DPG. Finally, among primates, human-associated DPGs exhibit diversification in their co-expression patterns and gene duplication events, and are obviously involved in neural development. Comparing high-quality human reference genomes from European (T2T-CHM13) and Chinese (T2T-YAO) populations, we identified 55 and 357 DPGs unique to the former and the latter, respectively. Our findings offer novel insights into the regulatory characteristics between neighboring genes and their structure-function selection among functionally conserved gene clusters.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499939","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

An Integrative Polygenic and Epigenetic Risk Score for Overweight-related Hypertension in Chinese Population. 中国人群超重相关高血压的综合多基因和表观遗传风险评分。

Genomics, proteomics & bioinformatics Pub Date : 2025-06-16 DOI: 10.1093/gpbjnl/qzaf048

Yaning Zhang 张亚宁, Qiwen Zheng 郑启文, Qili Qian 钱其溧, Na Yuan 苑娜, Tianzi Liu 刘天资, Xingjian Gao 高行健, Xiu Fan 凡秀, Youkun Bi 毕友坤, Guangju Ji 姬广聚, Peilin Jia 贾佩林, Sijia Wang 汪思佳, Fan Liu 刘凡, Changqing Zeng 曾长青

{"title":"An Integrative Polygenic and Epigenetic Risk Score for Overweight-related Hypertension in Chinese Population.","authors":"Yaning Zhang 张亚宁, Qiwen Zheng 郑启文, Qili Qian 钱其溧, Na Yuan 苑娜, Tianzi Liu 刘天资, Xingjian Gao 高行健, Xiu Fan 凡秀, Youkun Bi 毕友坤, Guangju Ji 姬广聚, Peilin Jia 贾佩林, Sijia Wang 汪思佳, Fan Liu 刘凡, Changqing Zeng 曾长青","doi":"10.1093/gpbjnl/qzaf048","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf048","url":null,"abstract":"Overweight-related hypertension (OrH), defined by the coexistence of excess body weight and hypertension (HTN), is an increasing health concern elevating cardiovascular disease risks. This study evaluated the prediction performance of polygenic risk scores (PRS) and methylation risk scores (MRS) for OrH in 7605 Chinese participants from two cohorts: the Chinese Academy of Sciences (CAS) and the National Survey of Physical Traits (NSPT). In CAS cohort, which predominantly consists of academics, males showed significantly higher prevalence of obesity, HTN, and OrH, along with worse metabolic syndrome indicators, compared to females. This disparity was less pronounced in NSPT cohort and in broader Chinese studies. Among ten PRS methods, PRS-CSx was the most effective, enhancing prediction accuracy for obesity [area under the curve (AUC) = 0.75], HTN (AUC = 0.74), and OrH (AUC = 0.75), compared to baseline models using only age and sex (AUC = 0.55-0.71). Similarly, least absolute shrinkage and selection operator (LASSO)-based MRS models improved prediction accuracies for obesity (AUC = 0.70), HTN (AUC = 0.73), and OrH (AUC = 0.78). Combining PRS and MRS further boosted prediction accuracy to the AUC of 0.77, 0.76, and 0.80, respectively. These models stratified individuals into high (> 0.6) or low (< 0.1) risk categories, covering 59.95% for obesity, 31.75% for HTN, and 43.89% for OrH, respectively. Our findings highlight a higher OrH risk among male academics, emphasize the influence of metabolic and lifestyle factors on MRS predictions, and highlight the value of multi-omics approaches in enhancing risk stratification.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510061","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

Deciphering Haploid Chromosome Conformation Alternation in Down Syndrome by Multiple Haploid Omics Analysis. 通过多重单倍体组学分析解读唐氏综合征的单倍体染色体构象变化。

Genomics, proteomics & bioinformatics Pub Date : 2025-06-12 DOI: 10.1093/gpbjnl/qzaf054

Chengchao Wu, Tianshu Zhou, Wenfu Ke, Wei Xiong, Zhihui Zhang, Siheng Zhang, Jinyue Wang, Lulu Deng, Keji Yan, Man Wang, Shenglong He, Qi Gong, Chao Ma, Xiaping Chen, Yan Li, He Long, Chong Guo, Gang Cao, Zhijun Zhang

{"title":"Deciphering Haploid Chromosome Conformation Alternation in Down Syndrome by Multiple Haploid Omics Analysis.","authors":"Chengchao Wu, Tianshu Zhou, Wenfu Ke, Wei Xiong, Zhihui Zhang, Siheng Zhang, Jinyue Wang, Lulu Deng, Keji Yan, Man Wang, Shenglong He, Qi Gong, Chao Ma, Xiaping Chen, Yan Li, He Long, Chong Guo, Gang Cao, Zhijun Zhang","doi":"10.1093/gpbjnl/qzaf054","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf054","url":null,"abstract":"For chromosome abnormalities (CA), such as Down syndrome (DS), the influence of genomic variations to chromosome conformation and gene transcription remains elusive. Based on the complete genomic sequence from the parents of the DS trisomy patient, we systematically delineated an atlas of parental-specific haploid single nucleotide polymorphism (SNP), copy number variation (CNV), three-dimensional (3D) genome, and RNA expression profiles of the diencephalon in the DS patient. The integrated haploid multi-omics analysis demonstrated that one-dimensional genomic variations including SNPs and CNVs in the DS patient are highly correlated with the alterations of the 3D genome and the subsequent gene transcription. The correlation relationship remains valid in haploid-levels. Moreover, we revealed the 3D genome alteration associated mis-regulation of DS-related genes, which facilitates to understanding the pathogenesis of CA. Together, our study contributes to decipher the coding from one-dimensional genomic variations to 3D genomic architecture and the subsequent gene transcription in healthy and diseases.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144277053","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

SRPS: Survival Reinforced Transfer Learning for Multicentric Proteomic Subtyping and Biomarker Discovery. SRPS：多中心蛋白质组学亚型和生物标志物发现的生存强化迁移学习。

Genomics, proteomics & bioinformatics Pub Date : 2025-06-10 DOI: 10.1093/gpbjnl/qzaf052

Linhai Xie, Pei Jiang, Cheng Chang

{"title":"SRPS: Survival Reinforced Transfer Learning for Multicentric Proteomic Subtyping and Biomarker Discovery.","authors":"Linhai Xie, Pei Jiang, Cheng Chang","doi":"10.1093/gpbjnl/qzaf052","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf052","url":null,"abstract":"Omics-based molecular subtyping in large-scale and multicentric cohort studies is a prerequisite for proteomics-driven precision medicine (PDPM). However, keeping the subtypes with robust molecular features and significant associations with prognosis across different cohorts is challenging due to the biological heterogeneity and technical inconsistency. Herein, we propose a subtyping algorithm, named Survival Reinforced Patient Stratification (SRPS), to adapt the known subtypes from a discovery cohort to another by simultaneously preserving the distinct prognosis and molecular characteristics of each subtype. SRPS has been benchmarked on simulated and real-world datasets, where it shows a 12% increase in classification accuracy and possesses the best prognostic discrimination. Moreover, based on the calculated subtype significance score, an \"unpopular\" protein, Peptidylprolyl Isomerase C (PPIC), was identified as the top-1 remarkable protein for subtyping the hepatocellular carcinoma (HCC) patients with the worst prognosis. Eventually, PPIC was experimentally proved to be a pro-cancer protein in HCC, confirming our work as a demonstration of interpretable machine learning guided biological discovery in PDPM research. SRPS is publicly available at https://github.com/PHOENIXcenter/SRPS and https://ngdc.cncb.ac.cn/biocode/tool/BT007770.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268240","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