Genomics, proteomics & bioinformatics最新文献

DeRR: A Unique Detecting Method and the First Landscape for T Cells with Dual T Cell Receptors from Large-scale Single Cell Data. DeRR：一种独特的检测方法和大规模单细胞数据中具有双T细胞受体的T细胞的第一个景观。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2025-10-06 DOI: 10.1093/gpbjnl/qzaf090

Si-Yi Chen, Lingzi Mao, Xin Fu, Wen-Kang Shen, Tao Yue, Qian Lei, An-Yuan Guo

{"title":"DeRR: A Unique Detecting Method and the First Landscape for T Cells with Dual T Cell Receptors from Large-scale Single Cell Data.","authors":"Si-Yi Chen, Lingzi Mao, Xin Fu, Wen-Kang Shen, Tao Yue, Qian Lei, An-Yuan Guo","doi":"10.1093/gpbjnl/qzaf090","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf090","url":null,"abstract":"While most T cells exclusively express a single T cell receptor (TCR), a distinct subpopulation exhibits dual types of TCR expression (dual-TCR). Although the functional implications of dual-TCR T cells in autoimmunity and immune protection have been documented, their isolation and characterization remain technically challenging, resulting in incomplete characterization of dual-TCR properties. To address this gap, we developed DeRR (Detection of dual T cell Receptors), a computational pipeline specifically designed to identify dual-TCRs in both single-cell TCR and RNA sequencing data (scTCR-seq and scRNA-seq, respectively). Evaluation of extensive datasets validated DeRR's robust performance. Analysis of over 600,000 single T cells from 147 samples revealed the first systematic characterization of dual-TCR T cells, with approximately 17% carrying dual TCR α-chains and 12% displaying dual TCR β-chains. Notably, dual-TCR frequency in cancer was elevated compared to other conditions and demonstrated a positive association with disease duration in autoimmune disorders. However, dual-TCR T cells were uniformly distributed across all T cell subtypes and exhibited greater cross-reactivity than conventional single-TCR T cells, particularly through their secondary TCR chains. Interestingly, the relative expression levels of the two TCRs varied dynamically within dual-TCR T cells. This study provides the first dedicated tool for dual-TCR detection and offers a comprehensive landscape of dual-TCR, significantly advancing our understanding of T cell immunology. The DeRR source code is publicly accessible under the following repositories: GitHub (https://github.com/GuoBioinfoLab/DeRR) and BioCode (https://ngdc.cncb.ac.cn/biocode/tool/7789).","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234789","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

Spatial Transcriptomics of Human Decidua Identifies Molecular Signatures in Recurrent Pregnancy Loss. 人类蜕膜空间转录组学鉴定复发性妊娠丢失的分子特征。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2025-10-01 DOI: 10.1093/gpbjnl/qzaf080

Qing Sha, Qiaoni Yu, Kaixing Chen, Junyu Wang, Feiyang Wang, Chen Jiang, Yuanzhe Li, Meifang Tang, Yanbing Hou, Ke Liu, Kun Chen, Zongcheng Yang, Shouzhen Li, Jingwen Fang, Sihui Luo, Xueying Zheng, Jianping Weng, Kun Qu, Chuang Guo

{"title":"Spatial Transcriptomics of Human Decidua Identifies Molecular Signatures in Recurrent Pregnancy Loss.","authors":"Qing Sha, Qiaoni Yu, Kaixing Chen, Junyu Wang, Feiyang Wang, Chen Jiang, Yuanzhe Li, Meifang Tang, Yanbing Hou, Ke Liu, Kun Chen, Zongcheng Yang, Shouzhen Li, Jingwen Fang, Sihui Luo, Xueying Zheng, Jianping Weng, Kun Qu, Chuang Guo","doi":"10.1093/gpbjnl/qzaf080","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf080","url":null,"abstract":"The human decidua establishes immune tolerance at the maternal-fetal interface and is essential for successful embryo implantation and development. Here, we conducted a spatial transcriptomic analysis of human decidua from early pregnancies in both healthy donors and patients with recurrent pregnancy loss (RPL). Our analysis revealed two distinct spatial domains, named implantation zone (IZ) and glandular-secretory zone (GZ), corresponding to the layers of decidua compacta and spongiosa, respectively. The decidual natural killer cell subset (dNK1) and the macrophage subset (dM2), both associated with growth promotion and immune regulation, were predominantly localized in the healthy IZ but were significantly reduced in RPL patients. In contrast, cytotoxic CD8+ T cells, sparsely distributed in the healthy decidual domains, were elevated in both domains under RPL conditions. Spatial cell-cell interaction analysis indicated a broad exhibition but a marked downregulation of immunoregulatory interactions in the IZ of RPL patients. Through integrated single-cell chromatin accessibility and transcription factor occupancy analyses, we identified FOSL2 as a pivotal regulator orchestrating the spatial transformation of dNK1 cells. Decreased FOSL2 expression correlated with compromised IL-15-induced dNK1 cell transformation and diminished immunoregulatory capabilities. Our findings delineate the intricate spatial and regulatory architecture of immune tolerance within the human decidua, providing new insights into immune tolerance dysregulation in RPL.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202555","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

Histo-LCM-Hi-C Reveals 3D Chromatin Conformations of Spatially Localized Rare Cells in Tissues at High Resolution. 组织- lcm - hi - c高分辨率揭示组织中空间定位的稀有细胞的三维染色质构象。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2025-09-30 DOI: 10.1093/gpbjnl/qzaf091

Yixin Liu, Min Chen, Xin Liu, Zeqian Xu, Xinhui Li, Yan Guo, Daniel M Czajkowsky, Zhifeng Shao

{"title":"Histo-LCM-Hi-C Reveals 3D Chromatin Conformations of Spatially Localized Rare Cells in Tissues at High Resolution.","authors":"Yixin Liu, Min Chen, Xin Liu, Zeqian Xu, Xinhui Li, Yan Guo, Daniel M Czajkowsky, Zhifeng Shao","doi":"10.1093/gpbjnl/qzaf091","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf091","url":null,"abstract":"It is now well established that an understanding of the chromatin structure is essential to delineate the mechanisms underlying genomic processes. However, while methods to obtain this information from cells in vitro are widely available, there is presently a significant lack of techniques that can acquire this data from cells in the tissue. Such a capability is critical to determine the dependence of the local tissue environment on cell functioning. Further, this ability is particularly necessary for cells that are a significant minority of the total tissue population, which are often obscured in data dominated by more abundant tissue cells. Here we have developed Histological Laser Capture Microdissection Hi-C (Histo-LCM-Hi-C) to enable the characterization of chromatin architecture of phenotype-defined, spatially localized cells within intact tissue sections from as few as about 300 cells. We demonstrate the effectiveness of this approach with the generation of the first 3D Hi-C map of the tissue-resident macrophages of the liver, the Kupffer cells (KC), which are a minor cell population in the normal liver. As expected, owing to their relative rarity, these KC maps are significantly different from those obtained from whole liver, revealing distant contacts between putative enhancers and genes involved in key KC functions as well as significant differences with that of in vitro induced bone-marrow derived macrophages. We anticipate that this method will prove to be an indispensable technique in the growing repertoire of methodologies used for the characterization of the genomic properties of cells within their native environment.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202558","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

Two Isatis Genomes Reveal the Biosynthesis and Evolutionary Origin of Indigo in Plants. 两个板蓝花基因组揭示靛蓝在植物中的生物合成和进化起源。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2025-09-27 DOI: 10.1093/gpbjnl/qzaf088

Junfeng Chen, Hexin Tan, Jun Yang, Kaijian Zhang, Rongrong Li, Shi Qiu, Doudou Huang, Zongyou Lv, Zhichao Xu, Qing Li, Zhongmin Xu, Ping Zhao, Jingxian Feng, Yajing Li, Wei Sun, Fei Yang, Rufeng Wang, Lei Zhang, Ying Xiao, Wansheng Chen

{"title":"Two Isatis Genomes Reveal the Biosynthesis and Evolutionary Origin of Indigo in Plants.","authors":"Junfeng Chen, Hexin Tan, Jun Yang, Kaijian Zhang, Rongrong Li, Shi Qiu, Doudou Huang, Zongyou Lv, Zhichao Xu, Qing Li, Zhongmin Xu, Ping Zhao, Jingxian Feng, Yajing Li, Wei Sun, Fei Yang, Rufeng Wang, Lei Zhang, Ying Xiao, Wansheng Chen","doi":"10.1093/gpbjnl/qzaf088","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf088","url":null,"abstract":"Indigo, a plant-originated blue dye, has a long and well-documented history of extensive human use. The Isatis genus has long been a key source for indigo production, however, the biosynthetic pathway responsible for indigo within Isatis has remained elusive. Here, we conducted phylogenetic and metabolic analyses of various Isatis taxa, revealing that the capacity to produce indigo was apparently lost in some of these taxa. Following de novo genome sequencing, assembly, and comparative genomic analysis between Isatis indigotica and Isatis cappadocica, we delved into the origins and evolution of indigo biosynthesis. Homologous expression of candidate genes in Nicotiana benthamiana identified multiple oxidase families, including flavin-containing monooxygenase (FMO) and cytochrome P450 (CYP) protein that catalyze the oxidation steps leading to the indigo biosynthesis, indicating a metabolic innovation derived from the oxime pathway in plants. The evolutionary aspects concerning the neofunctionalization of CYPs-catalyzed biosynthesis of glucosides and FMOs-catalyzed oxime in Isatis taxa provide new insights into the evolution of these metabolic pathways in plants.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145180785","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

A Co-essentiality Network of Cancer Driver Genes Better Prioritizes Anticancer Drugs. 癌症驱动基因的共通性网络更好地优先考虑抗癌药物。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2025-09-26 DOI: 10.1093/gpbjnl/qzaf070

Kwanghwan Lee, Donghyo Kim, Inhae Kim, Juhee Lee, Doyeon Ha, Seongsu Lim, Eunjee Kim, Sin-Hyeog Im, Kunyoo Shin, Sanguk Kim

{"title":"A Co-essentiality Network of Cancer Driver Genes Better Prioritizes Anticancer Drugs.","authors":"Kwanghwan Lee, Donghyo Kim, Inhae Kim, Juhee Lee, Doyeon Ha, Seongsu Lim, Eunjee Kim, Sin-Hyeog Im, Kunyoo Shin, Sanguk Kim","doi":"10.1093/gpbjnl/qzaf070","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf070","url":null,"abstract":"Diverse molecular networks have been extensively studied to discover therapeutic targets and repurpose approved drugs. However, it is necessary to select a suitable network since the performance of network medicine relies heavily on the completeness and characteristics of the selected network. Although a network using gene essentiality from cancer cells could be an effective platform for identifying anticancer targets, efforts to apply these networks in therapeutic applications have been limited. We constructed a phenotype-level network using the co-essentiality relationship between genes in CRISPR screens across 769 cancer cells to discover therapeutic targets for diverse cancer types. Leveraging cancer driver genes and network propagation on the networks, we found that the co-essentiality network better prioritized anticancer targets and biomarkers and predicted more precise drug responses in cancer cells than other molecular networks. The co-essentiality network outperformed conventional molecular networks in drug repurposing, which were validated in silico by clinical trial records. Notably, the co-essentiality network provided 30 repurposed drugs that the other networks have yet to cover, and we showcased three approved drugs repurposed for lung adenocarcinoma (atovaquone, eflornithine, and teriflunomide). Our study provides a novel network for precision oncology to improve the identification of therapeutic targets in specific cancers.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152353","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

DisConST: Distribution-aware Contrastive Learning for Spatial Domain Identification. DisConST：空间域识别的分布感知对比学习。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2025-09-24 DOI: 10.1093/gpbjnl/qzaf085

Peimeng Zhen, Xiaofeng Wang, Han Shu, Jialu Hu, Yongtian Wang, Jiajie Peng, Xuequn Shang, Jing Chen, Tao Wang

{"title":"DisConST: Distribution-aware Contrastive Learning for Spatial Domain Identification.","authors":"Peimeng Zhen, Xiaofeng Wang, Han Shu, Jialu Hu, Yongtian Wang, Jiajie Peng, Xuequn Shang, Jing Chen, Tao Wang","doi":"10.1093/gpbjnl/qzaf085","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf085","url":null,"abstract":"Spatial transcriptomics (ST) is a cutting-edge technology that provides comprehensive insights into gene expression patterns from a spatial perspective. A key research focus within this field is spatial domain identification, which is essential for exploring tissue organization, biological development, and disease mechanisms. Although methods have been developed, they still face challenges in modeling the gene expression information together with the spatial locations, resulting in suboptimal accuracy. We introduce Distribution-aware Contrastive Learning for Spatial Transcriptomics (DisConST), a novel deep learning method designed to improve spatial domain detection within ST datasets. DisConST addresses key challenges, such as the high dropout rates and the complex integration of spatial and gene expression data, by incorporating contrastive learning strategies that are aware of the underlying data distributions. It employs the zero-inflated negative binomial (ZINB) distribution, along with graph contrastive learning, to generate more informative latent representations. These representations efficiently integrate spatial positions, transcriptomic profiles, and cell-type proportions within spots. We validated DisConST across diverse ST datasets of tissues, organs, and embryos from various sequencing platforms in both normal and disease states. Our results consistently demonstrated that DisConST achieves superior spatial domain recognition accuracy compared to existing state-of-the-art methods. Furthermore, our experiments highlighted the utility of DisConST in advancing research on tissue organization, embryonic development, and tumor immune microenvironment dissection. The source code for DisConST is freely available at https://github.com/Zhenpm/DisConST/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133225","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

PASSpedia: A Polyadenylation Site Database Across Different Species at Single Cell Resolution. PASSpedia：在单细胞分辨率下跨不同物种的聚腺苷化位点数据库。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2025-09-23 DOI: 10.1093/gpbjnl/qzaf089

Pei-Hong Zhang, Hua Feng, Xu-Kai Ma, Fang Nan, Li Yang

引用次数: 0

A Model for the Development of Alzheimer's Disease. 阿尔茨海默病发展的模型。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2025-09-23 DOI: 10.1093/gpbjnl/qzaf087

Zhenyu Huang, Xuechen Mu, Qiufen Chen, Lingli Zhong, Jun Xiao, Chunman Zuo, Ye Zhang, Bocheng Shi, Yingwei Qu, Renbo Tan, Long Xu, Renchu Guan, Ying Xu

{"title":"A Model for the Development of Alzheimer's Disease.","authors":"Zhenyu Huang, Xuechen Mu, Qiufen Chen, Lingli Zhong, Jun Xiao, Chunman Zuo, Ye Zhang, Bocheng Shi, Yingwei Qu, Renbo Tan, Long Xu, Renchu Guan, Ying Xu","doi":"10.1093/gpbjnl/qzaf087","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf087","url":null,"abstract":"Intracellular alkalosis and extracellular acidosis are well-established characteristics of Alzheimer's disease (AD). We present a computational analysis and modeling of transcriptomic data of AD tissues, aiming to understand their causes and consequences. Our analyses have revealed that (1) persistent mitochondrial alkalization is due to chronic inflammation coupled with elevated iron and copper metabolisms; (2) the affected cells activate multiple acid-producing metabolisms to keep the mitochondrial pH stable for survival; (3) the most significant one is the continuous import and hydrolysis of glutamine to glutamate, NH3 and H+, resulting in persistent release of glutamates, an excitatory neurotransmitter, into the extracellular space; (4) this leads to persistent hyperexcitability of the nearby neurons, resulting in their continuous firing and release of H+-rich synaptic vesicles; (5) these H+s are neutralized by bicarbonates released by the neighboring astrocytes in normal tissues, which could not keep up with the increased H+-release in their discharge rates of bicarbonates in AD tissues, leading to progressively increased extracellular acidosis and ultimately cell death; and (6) multiple extensively studied AD-associated phenotypes, including Aβ aggregates and Tau fibers, are induced to help to alleviate the pH imbalances and beneficial to cell survival in the early phase of AD, which gradually become contributors to the AD development. Each step in this model is largely supported by published studies. Overall, we have developed a fundamentally novel and systems-level view of how AD may have developed.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126891","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

CTTIME: A Database for Analyzing Cancer Therapy's Impact on Tumor Immune Microenvironment. CTTIME：分析肿瘤治疗对肿瘤免疫微环境影响的数据库。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2025-09-22 DOI: 10.1093/gpbjnl/qzaf086

Chengjie Zhang, Yu Dong, Yuan Liu, Jintong Shi, Leng Han, Youqiong Ye

{"title":"CTTIME: A Database for Analyzing Cancer Therapy's Impact on Tumor Immune Microenvironment.","authors":"Chengjie Zhang, Yu Dong, Yuan Liu, Jintong Shi, Leng Han, Youqiong Ye","doi":"10.1093/gpbjnl/qzaf086","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf086","url":null,"abstract":"Tumor immune microenvironment (TIME) significantly affects the regulation of immune checkpoint blockade (ICB)- based therapy. Anti-cancer drugs also have the potential to modify the TIME, which in turn affects the potency of ICB and combined therapies. Therefore, identifying the prediction of therapeutic efficacy and developing combination therapy strategies will be significantly enhanced by placing the dynamic change of TIME during anti-cancer therapies in cancer types. A computational pipeline and database was designed to identify dynamic molecular profiles under various anti-cancer therapies. This database comprised 92 transcriptomic datasets, including 5311 pre- and post-treatment biopsies from 20 cancer types and 9 treatment strategies. A systematic approach was used to identify differentially expressed genes (DEGs) and functional enrichment, significant associations among immune cell populations, and dynamic alteration of immune cell abundance and ICB-related immune features during various therapeutic strategies. Finally, a user-friendly database, Impact of Cancer Therapy on TIME (CTTIME), was developed to browse, search, visualize, and download data of interest which will serve as a valuable resource for investigating innovative anti-cancer therapies. CTTIME is available at https://www.cttime.yelab.site/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115637","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

The GSA Family in 2025: A Broadened Sharing Platform for Multi-omics and Multimodal Data. 2025年的GSA家族：一个扩展的多组学和多模式数据共享平台。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2025-09-22 DOI: 10.1093/gpbjnl/qzaf072

Sisi Zhang 张思思, Xu Chen 陈旭, Enhui Jin 金恩惠, Anke Wang 王安可, Tingting Chen 陈婷婷, Xiaolong Zhang 张小龙, Junwei Zhu 朱军伟, Lili Dong 董丽莉, Yanling Sun 孙艳玲, Caixia Yu 俞彩霞, Yubo Zhou 周榆博, Zhuojing Fan 范卓静, Huanxin Chen 陈焕新, Shuang Zhai 翟爽, Yubin Sun 孙玉彬, Qiancheng Chen 陈乾成, Jingfa Xiao 肖景发, Shuhui Song 宋述慧, Zhang Zhang 章张, Yiming Bao 鲍一明, Yanqing Wang 王彦青, Wenming Zhao 赵文明

{"title":"The GSA Family in 2025: A Broadened Sharing Platform for Multi-omics and Multimodal Data.","authors":"Sisi Zhang 张思思, Xu Chen 陈旭, Enhui Jin 金恩惠, Anke Wang 王安可, Tingting Chen 陈婷婷, Xiaolong Zhang 张小龙, Junwei Zhu 朱军伟, Lili Dong 董丽莉, Yanling Sun 孙艳玲, Caixia Yu 俞彩霞, Yubo Zhou 周榆博, Zhuojing Fan 范卓静, Huanxin Chen 陈焕新, Shuang Zhai 翟爽, Yubin Sun 孙玉彬, Qiancheng Chen 陈乾成, Jingfa Xiao 肖景发, Shuhui Song 宋述慧, Zhang Zhang 章张, Yiming Bao 鲍一明, Yanqing Wang 王彦青, Wenming Zhao 赵文明","doi":"10.1093/gpbjnl/qzaf072","DOIUrl":"10.1093/gpbjnl/qzaf072","url":null,"abstract":"The Genome Sequence Archive family (GSA family) provides a comprehensive suite of database resources for archiving, retrieving, and sharing multi-omics data for the global academic and industrial communities. It currently comprises four distinct database members: the Genome Sequence Archive (GSA, https://ngdc.cncb.ac.cn/gsa), the Genome Sequence Archive for Human (GSA-Human, https://ngdc.cncb.ac.cn/gsa-human), the Open Archive for Miscellaneous Data (OMIX, https://ngdc.cncb.ac.cn/omix), and the Open Biomedical Imaging Archive (OBIA, https://ngdc.cncb.ac.cn/obia). Compared to its 2021 version, the GSA family has expanded significantly by introducing a new repository, the OBIA, and by comprehensively upgrading the existing databases. Notable enhancements to the existing members include broadening the range of accepted data types, strengthening quality control systems, improving the data retrieval system, and refining data-sharing management mechanisms.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12451262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144983976","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