Genomics, proteomics & bioinformatics最新文献_第10页

Scm6A: A Fast and Low-cost Method for Quantifying m6A Modifications at the Single-cell Level. Scm6A：单细胞水平 m6A 修饰定量的快速低成本方法。

Genomics, proteomics & bioinformatics Pub Date : 2024-10-15 DOI: 10.1093/gpbjnl/qzae039

Yueqi Li, Jingyi Li, Wenxing Li, Shuaiyi Liang, Wudi Wei, Jiemei Chu, Jingzhen Lai, Yao Lin, Hubin Chen, Jinming Su, Xiaopeng Hu, Gang Wang, Jun Meng, Junjun Jiang, Li Ye, Sanqi An

{"title":"Scm6A: A Fast and Low-cost Method for Quantifying m6A Modifications at the Single-cell Level.","authors":"Yueqi Li, Jingyi Li, Wenxing Li, Shuaiyi Liang, Wudi Wei, Jiemei Chu, Jingzhen Lai, Yao Lin, Hubin Chen, Jinming Su, Xiaopeng Hu, Gang Wang, Jun Meng, Junjun Jiang, Li Ye, Sanqi An","doi":"10.1093/gpbjnl/qzae039","DOIUrl":"10.1093/gpbjnl/qzae039","url":null,"abstract":"It is widely accepted that N6-methyladenosine (m6A) exhibits significant intercellular specificity, which poses challenges for its detection using existing m6A quantitative methods. In this study, we introduced Single-cell m6A Analysis (Scm6A), a machine learning-based approach for single-cell m6A quantification. Scm6A leverages input features derived from the expression levels of m6A trans regulators and cis sequence features, and offers remarkable prediction efficiency and reliability. To further validate the robustness and precision of Scm6A, we first applied Scm6A to single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and calculated the m6A levels in CD4+ and CD8+ T cells. We also applied a winscore-based m6A calculation method to conduct N6-methyladenosine sequencing (m6A-seq) analysis on CD4+ and CD8+ T cells isolated through magnetic-activated cell sorting (MACS) from the same samples. Notably, the m6A levels calculated by Scm6A exhibited a significant positive correlation with those quantified through m6A-seq in different cells isolated by MACS, providing compelling evidence for Scm6A's reliability. Additionally, we performed single-cell-level m6A analysis on lung cancer tissues as well as blood samples from patients with coronavirus disease 2019 (COVID-19), and demonstrated the landscape and regulatory mechanisms of m6A in different T cell subtypes from these diseases. In summary, Scm6A is a novel, dependable, and accurate method for single-cell m6A detection and has broad applications in the realm of m6A-related research.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12016562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484017","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

eRNA-IDO: A One-stop Platform for Identification, Interactome Discovery, and Functional Annotation of Enhancer RNAs. eRNA-IDO: Enhancer RNAs 鉴定、交互组发现和功能注释的一站式平台。

Genomics, proteomics & bioinformatics Pub Date : 2024-10-15 DOI: 10.1093/gpbjnl/qzae059

Yuwei Zhang, Lihai Gong, Ruofan Ding, Wenyan Chen, Hao Rong, Yanguo Li, Fawziya Shameem, Korakkandan Arshad Ali, Lei Li, Qi Liao

{"title":"eRNA-IDO: A One-stop Platform for Identification, Interactome Discovery, and Functional Annotation of Enhancer RNAs.","authors":"Yuwei Zhang, Lihai Gong, Ruofan Ding, Wenyan Chen, Hao Rong, Yanguo Li, Fawziya Shameem, Korakkandan Arshad Ali, Lei Li, Qi Liao","doi":"10.1093/gpbjnl/qzae059","DOIUrl":"10.1093/gpbjnl/qzae059","url":null,"abstract":"Growing evidence supports the transcription of enhancer RNAs (eRNAs) and their important roles in gene regulation. However, their interactions with other biomolecules and their corresponding functionality remain poorly understood. In an attempt to facilitate mechanistic research, this study presents eRNA-IDO, the first integrative computational platform for the identification, interactome discovery, and functional annotation of human eRNAs. eRNA-IDO comprises two modules: eRNA-ID and eRNA-Anno. Functionally, eRNA-ID can identify eRNAs from de novo assembled transcriptomes. eRNA-ID includes eight kinds of enhancer makers, enabling users to customize enhancer regions flexibly and conveniently. In addition, eRNA-Anno provides cell-/tissue-specific functional annotation for both new and known eRNAs by analyzing the eRNA interactome from prebuilt or user-defined networks between eRNAs and protein-coding genes. The prebuilt networks include the Genotype-Tissue Expression (GTEx)-based co-expression networks in normal tissues, The Cancer Genome Atlas (TCGA)-based co-expression networks in cancer tissues, and omics-based eRNA-centric regulatory networks. eRNA-IDO can facilitate research on the biogenesis and functions of eRNAs. The eRNA-IDO server is freely available at http://bioinfo.szbl.ac.cn/eRNA_IDO/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044264","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

Cancer Stemness Online: A Resource for Investigating Cancer Stemness and Associations with Immune Response. 癌症干细胞在线：研究癌症干性及其与免疫反应关系的资源。

Genomics, proteomics & bioinformatics Pub Date : 2024-10-15 DOI: 10.1093/gpbjnl/qzae058

Weiwei Zhou, Minghai Su, Tiantongfei Jiang, Yunjin Xie, Jingyi Shi, Yingying Ma, Kang Xu, Gang Xu, Yongsheng Li, Juan Xu

{"title":"Cancer Stemness Online: A Resource for Investigating Cancer Stemness and Associations with Immune Response.","authors":"Weiwei Zhou, Minghai Su, Tiantongfei Jiang, Yunjin Xie, Jingyi Shi, Yingying Ma, Kang Xu, Gang Xu, Yongsheng Li, Juan Xu","doi":"10.1093/gpbjnl/qzae058","DOIUrl":"10.1093/gpbjnl/qzae058","url":null,"abstract":"Cancer progression involves the gradual loss of a differentiated phenotype and the acquisition of progenitor and stem cell-like features, which are potential culprits of immunotherapy resistance. Although the state-of-the-art predictive computational methods have facilitated the prediction of cancer stemness, there remains a lack of efficient resources to accommodate various usage requirements. Here, we present the Cancer Stemness Online, an integrated resource for efficiently scoring cancer stemness potential at both bulk and single-cell levels. This resource integrates eight robust predictive algorithms as well as 27 signature gene sets associated with cancer stemness for predicting stemness scores. Downstream analyses were performed from five distinct aspects: identifying the signature genes of cancer stemness; exploring the associations with cancer hallmarks and cellular states; exploring the associations with immune response and the communications with immune cells; investigating the contributions to patient survival; and performing a robustness analysis of cancer stemness among different methods. Moreover, the pre-calculated cancer stemness atlas for more than 40 cancer types can be accessed by users. Both the tables and diverse visualizations of the analytical results are available for download. Together, Cancer Stemness Online is a powerful resource for scoring cancer stemness and expanding downstream functional interpretation, including immune response and cancer hallmarks. Cancer Stemness Online is freely accessible at http://bio-bigdata.hrbmu.edu.cn/CancerStemnessOnline.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11522875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141984186","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

Laws of Genome Nucleotide Composition. 基因组核苷酸组成规律

Genomics, proteomics & bioinformatics Pub Date : 2024-10-15 DOI: 10.1093/gpbjnl/qzae061

Zhang Zhang

引用次数: 0

Whole-genome Sequencing Association Analysis of Quantitative Platelet Traits in A Large Cohort of β-thalassemia. 大样本β地中海贫血患者血小板定量特征的全基因组测序关联分析

Genomics, proteomics & bioinformatics Pub Date : 2024-09-27 DOI: 10.1093/gpbjnl/qzae065

Xingmin Wang, Qianqian Zhang, Xianming Chen, Yushan Huang, Wei Zhang, Liuhua Liao, Xinhua Zhang, Binbin Huang, Yueyan Huang, Yuhua Ye, Mengyang Song, Jinquan Lao, Juanjuan Chen, Xiaoqin Feng, Xingjiang Long, Zhixiang Liu, Weijian Zhu, Lian Yu, Chengwu Fan, Deguo Tang, Tianyu Zhong, Mingyan Fang, Caiyun Li, Chao Niu, Li Huang, Bin Lin, Xiaoyun Hua, Xin Jin, Zilin Li, Xiangmin Xu

{"title":"Whole-genome Sequencing Association Analysis of Quantitative Platelet Traits in A Large Cohort of β-thalassemia.","authors":"Xingmin Wang, Qianqian Zhang, Xianming Chen, Yushan Huang, Wei Zhang, Liuhua Liao, Xinhua Zhang, Binbin Huang, Yueyan Huang, Yuhua Ye, Mengyang Song, Jinquan Lao, Juanjuan Chen, Xiaoqin Feng, Xingjiang Long, Zhixiang Liu, Weijian Zhu, Lian Yu, Chengwu Fan, Deguo Tang, Tianyu Zhong, Mingyan Fang, Caiyun Li, Chao Niu, Li Huang, Bin Lin, Xiaoyun Hua, Xin Jin, Zilin Li, Xiangmin Xu","doi":"10.1093/gpbjnl/qzae065","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae065","url":null,"abstract":"Platelet acts as a crucial monitoring indicator for hypercoagulability and thrombosis and a key target for drug regulation. Genotype-phenotype association studies have confirmed that platelet traits are quantitatively regulated by multiple genes. However, there is currently a lack of genetic studies on the heterogeneity of platelet traits in β-thalassemia under hypercoagulable state. Here, we studied the phenotypic heterogeneity of platelet count (PLT) and mean platelet volume (MPV) in 1020 β-thalassemia patients. We further performed a functionally informed whole genome sequencing association analysis of common variants and rare variants (RVs) for PLT and MPV in 916 patients through integrative analysis of whole-genome sequencing data and functional annotation data. Extreme phenotypic heterogeneity of platelet traits was observed in β-thalassemia patients. Additionally, the common variant based gene-level analysis identified the novel gene of RNF144B associated with MPV. The RV analysis identified several novel associations in both coding and noncoding genome, including missense RVs of PPP2R5C associated with PLT and missense RVs of TSSK1B associated with MPV. In conclusion, we performed a comprehensive and systematic whole genome scan of platelet traits in the β-thalassemia cohort, demonstrating the specificity of genetic regulation of platelet traits in the context of β-thalassemia, providing potential targets for intervention.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142335112","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

Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data. 推断空间转录组学数据的细胞组成的计算策略和算法。

Genomics, proteomics & bioinformatics Pub Date : 2024-09-13 DOI: 10.1093/gpbjnl/qzae057

Xiuying Liu, Xianwen Ren

引用次数: 0

CBioProfiler: A Web and Standalone Pipeline for Cancer Biomarker and Subtype Characterization. CBioProfiler：用于癌症生物标记物和亚型特征描述的网络和独立管道。

Genomics, proteomics & bioinformatics Pub Date : 2024-09-13 DOI: 10.1093/gpbjnl/qzae045

Xiaoping Liu, Zisong Wang, Hongjie Shi, Sheng Li, Xinghuan Wang

{"title":"CBioProfiler: A Web and Standalone Pipeline for Cancer Biomarker and Subtype Characterization.","authors":"Xiaoping Liu, Zisong Wang, Hongjie Shi, Sheng Li, Xinghuan Wang","doi":"10.1093/gpbjnl/qzae045","DOIUrl":"10.1093/gpbjnl/qzae045","url":null,"abstract":"Cancer is a leading cause of death worldwide, and the identification of biomarkers and subtypes that can predict the long-term survival of cancer patients is essential for their risk stratification, treatment, and prognosis. However, there are currently no standardized tools for exploring cancer biomarkers or subtypes. In this study, we introduced Cancer Biomarker and subtype Profiler (CBioProfiler), a web server and standalone application that includes two pipelines for analyzing cancer biomarkers and subtypes. The cancer biomarker pipeline consists of five modules for identifying and annotating cancer survival-related biomarkers using multiple survival-related machine learning algorithms. The cancer subtype pipeline includes three modules for data preprocessing, subtype identification using multiple unsupervised machine learning methods, and subtype evaluation and validation. CBioProfiler also includes CuratedCancerPrognosisData, a novel R package that integrates reviewed and curated gene expression and clinical data from 268 studies. These studies cover 43 common blood and solid tumors and draw upon 47,686 clinical samples. The web server is available at https://www.cbioprofiler.com/ and https://cbioprofiler.znhospital.cn/CBioProfiler/, and the standalone app and source code can be found at https://github.com/liuxiaoping2020/CBioProfiler.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11464420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141312596","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

Nphos: Database and Predictor of Protein N-phosphorylation. Nphos：蛋白质 N-磷酸化数据库和预测器。

Genomics, proteomics & bioinformatics Pub Date : 2024-09-13 DOI: 10.1093/gpbjnl/qzae032

Ming-Xiao Zhao, Ruo-Fan Ding, Qiang Chen, Junhua Meng, Fulai Li, Songsen Fu, Biling Huang, Yan Liu, Zhi-Liang Ji, Yufen Zhao

{"title":"Nphos: Database and Predictor of Protein N-phosphorylation.","authors":"Ming-Xiao Zhao, Ruo-Fan Ding, Qiang Chen, Junhua Meng, Fulai Li, Songsen Fu, Biling Huang, Yan Liu, Zhi-Liang Ji, Yufen Zhao","doi":"10.1093/gpbjnl/qzae032","DOIUrl":"10.1093/gpbjnl/qzae032","url":null,"abstract":"Protein N-phosphorylation is widely present in nature and participates in various biological processes. However, current knowledge on N-phosphorylation is extremely limited compared to that on O-phosphorylation. In this study, we collected 11,710 experimentally verified N-phosphosites of 7344 proteins from 39 species and subsequently constructed the database Nphos to share up-to-date information on protein N-phosphorylation. Upon these substantial data, we characterized the sequential and structural features of protein N-phosphorylation. Moreover, after comparing hundreds of learning models, we chose and optimized gradient boosting decision tree (GBDT) models to predict three types of human N-phosphorylation, achieving mean area under the receiver operating characteristic curve (AUC) values of 90.56%, 91.24%, and 92.01% for pHis, pLys, and pArg, respectively. Meanwhile, we discovered 488,825 distinct N-phosphosites in the human proteome. The models were also deployed in Nphos for interactive N-phosphosite prediction. In summary, this work provides new insights and points for both flexible and focused investigations of N-phosphorylation. It will also facilitate a deeper and more systematic understanding of protein N-phosphorylation modification by providing a data and technical foundation. Nphos is freely available at http://www.bio-add.org/Nphos/ and http://ppodd.org.cn/Nphos/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12016571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142396284","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

SCancerRNA: Expression at the Single-cell Level and Interaction Resource of Non-coding RNA Biomarkers for Cancers. SCancerRNA：癌症非编码 RNA 生物标志物的单细胞水平表达和交互资源。

Genomics, proteomics & bioinformatics Pub Date : 2024-09-13 DOI: 10.1093/gpbjnl/qzae023

Hongzhe Guo, Liyuan Zhang, Xinran Cui, Liang Cheng, Tianyi Zhao, Yadong Wang

{"title":"SCancerRNA: Expression at the Single-cell Level and Interaction Resource of Non-coding RNA Biomarkers for Cancers.","authors":"Hongzhe Guo, Liyuan Zhang, Xinran Cui, Liang Cheng, Tianyi Zhao, Yadong Wang","doi":"10.1093/gpbjnl/qzae023","DOIUrl":"10.1093/gpbjnl/qzae023","url":null,"abstract":"Non-coding RNAs (ncRNAs) participate in multiple biological processes associated with cancers as tumor suppressors or oncogenic drivers. Due to their high stability in plasma, urine, and many other fluids, ncRNAs have the potential to serve as key biomarkers for early diagnosis and screening of cancers. During cancer progression, tumor heterogeneity plays a crucial role, and it is particularly important to understand the gene expression patterns of individual cells. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering gene expression in different cell types for human cancers has become feasible by profiling transcriptomes at the cellular level. However, a well-organized and comprehensive online resource that provides access to the expression of genes corresponding to ncRNA biomarkers in different cell types at the single-cell level is not available yet. Therefore, we developed the SCancerRNA database to summarize experimentally supported data on long ncRNA, microRNA, PIWI-interacting RNA, small nucleolar RNA, and circular RNA biomarkers, as well as data on their differential expression at the cellular level. Furthermore, we collected biological functions and clinical applications of biomarkers to facilitate the application of ncRNA biomarkers to cancer diagnosis, as well as the monitoring of progression and targeted therapies. SCancerRNA also allows users to explore interaction networks of different types of ncRNAs, and build computational models in the future. SCancerRNA is freely accessible at http://www.scancerrna.com/BioMarker.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12016560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142335113","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

GenBase: A Nucleotide Sequence Database. GenBase：核苷酸序列数据库。

Genomics, proteomics & bioinformatics Pub Date : 2024-09-13 DOI: 10.1093/gpbjnl/qzae047

Congfan Bu, Xinchang Zheng, Xuetong Zhao, Tianyi Xu, Xue Bai, Yaokai Jia, Meili Chen, Lili Hao, Jingfa Xiao, Zhang Zhang, Wenming Zhao, Bixia Tang, Yiming Bao

{"title":"GenBase: A Nucleotide Sequence Database.","authors":"Congfan Bu, Xinchang Zheng, Xuetong Zhao, Tianyi Xu, Xue Bai, Yaokai Jia, Meili Chen, Lili Hao, Jingfa Xiao, Zhang Zhang, Wenming Zhao, Bixia Tang, Yiming Bao","doi":"10.1093/gpbjnl/qzae047","DOIUrl":"10.1093/gpbjnl/qzae047","url":null,"abstract":"The rapid advancement of sequencing technologies poses challenges in managing the large volume and exponential growth of sequence data efficiently and on time. To address this issue, we present GenBase (https://ngdc.cncb.ac.cn/genbase), an open-access data repository that follows the International Nucleotide Sequence Database Collaboration (INSDC) data standards and structures, for efficient nucleotide sequence archiving, searching, and sharing. As a core resource within the National Genomics Data Center (NGDC) of the China National Center for Bioinformation (CNCB; https://ngdc.cncb.ac.cn), GenBase offers bilingual submission pipeline and services, as well as local submission assistance in China. GenBase also provides a unique Excel format for metadata description and feature annotation of nucleotide sequences, along with a real-time data validation system to streamline sequence submissions. As of April 23, 2024, GenBase received 68,251 nucleotide sequences and 689,574 annotated protein sequences across 414 species from 2319 submissions. Out of these, 63,614 (93%) nucleotide sequences and 620,640 (90%) annotated protein sequences have been released and are publicly accessible through GenBase's web search system, File Transfer Protocol (FTP), and Application Programming Interface (API). Additionally, in collaboration with INSDC, GenBase has constructed an effective data exchange mechanism with GenBank and started sharing released nucleotide sequences. Furthermore, GenBase integrates all sequences from GenBank with daily updates, demonstrating its commitment to actively contributing to global sequence data management and sharing.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11434157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447873","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