{"title":"ScLineageAtlas:一个全面的单细胞基因组数据库,用于表征癌症中的细胞克隆。","authors":"Jinyang Liu, Rui Hou, Junlin Xu, Tingting Hui, Haotian Tian, Yankun Liu, Meijun Zhang, Geng Tian, Jialiang Yang","doi":"10.1093/database/baaf046","DOIUrl":null,"url":null,"abstract":"<p><p>Accurate identification of clonal relationships between cell populations is crucial for investigating cellular differentiation trajectories and gaining insights into the underlying mechanisms of cancer initiation and development. The Single Cell Lineage Atlas (ScLineageAtlas; https://www.scladb.geneis.org.cn) is a comprehensive single-cell genomics database that characterizes cellular clones across various cancer types. The database currently includes 24 processed single-cell RNA sequencing datasets spanning 13 different cancer types. ScLineageAtlas leverages advanced computational methods to identify cellular clones, providing researchers with a detailed understanding of clone relationships and evolutionary dynamics. Additionally, the database offers comprehensive metadata for each sample, enabling researchers to explore contextual information and sample characteristics. The spatial visualization of cell clones presented in the ScLineageAtlas provides a valuable tool for enhancing our understanding of the genetic heterogeneity within the tumour microenvironment. Through the analysis of biological differences between these diverse cell populations, researchers can explore key genes and signalling pathways associated with cancer initiation, development, and therapeutic efficacy. In summary, the ScLineageAtlas serves as a user-friendly platform for data operations on cellular clones, facilitating the understanding of tumour heterogeneity, differentiation trajectories, and evolution. It thus contributes significantly to cancer research and clinical practice.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462628/pdf/","citationCount":"0","resultStr":"{\"title\":\"ScLineageAtlas: a comprehensive single-cell genomics database for characterizing cellular clones in cancer.\",\"authors\":\"Jinyang Liu, Rui Hou, Junlin Xu, Tingting Hui, Haotian Tian, Yankun Liu, Meijun Zhang, Geng Tian, Jialiang Yang\",\"doi\":\"10.1093/database/baaf046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Accurate identification of clonal relationships between cell populations is crucial for investigating cellular differentiation trajectories and gaining insights into the underlying mechanisms of cancer initiation and development. The Single Cell Lineage Atlas (ScLineageAtlas; https://www.scladb.geneis.org.cn) is a comprehensive single-cell genomics database that characterizes cellular clones across various cancer types. The database currently includes 24 processed single-cell RNA sequencing datasets spanning 13 different cancer types. ScLineageAtlas leverages advanced computational methods to identify cellular clones, providing researchers with a detailed understanding of clone relationships and evolutionary dynamics. Additionally, the database offers comprehensive metadata for each sample, enabling researchers to explore contextual information and sample characteristics. The spatial visualization of cell clones presented in the ScLineageAtlas provides a valuable tool for enhancing our understanding of the genetic heterogeneity within the tumour microenvironment. Through the analysis of biological differences between these diverse cell populations, researchers can explore key genes and signalling pathways associated with cancer initiation, development, and therapeutic efficacy. In summary, the ScLineageAtlas serves as a user-friendly platform for data operations on cellular clones, facilitating the understanding of tumour heterogeneity, differentiation trajectories, and evolution. It thus contributes significantly to cancer research and clinical practice.</p>\",\"PeriodicalId\":10923,\"journal\":{\"name\":\"Database: The Journal of Biological Databases and Curation\",\"volume\":\"2025 \",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-01-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462628/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Database: The Journal of Biological Databases and Curation\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/database/baaf046\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Database: The Journal of Biological Databases and Curation","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/database/baaf046","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
ScLineageAtlas: a comprehensive single-cell genomics database for characterizing cellular clones in cancer.
Accurate identification of clonal relationships between cell populations is crucial for investigating cellular differentiation trajectories and gaining insights into the underlying mechanisms of cancer initiation and development. The Single Cell Lineage Atlas (ScLineageAtlas; https://www.scladb.geneis.org.cn) is a comprehensive single-cell genomics database that characterizes cellular clones across various cancer types. The database currently includes 24 processed single-cell RNA sequencing datasets spanning 13 different cancer types. ScLineageAtlas leverages advanced computational methods to identify cellular clones, providing researchers with a detailed understanding of clone relationships and evolutionary dynamics. Additionally, the database offers comprehensive metadata for each sample, enabling researchers to explore contextual information and sample characteristics. The spatial visualization of cell clones presented in the ScLineageAtlas provides a valuable tool for enhancing our understanding of the genetic heterogeneity within the tumour microenvironment. Through the analysis of biological differences between these diverse cell populations, researchers can explore key genes and signalling pathways associated with cancer initiation, development, and therapeutic efficacy. In summary, the ScLineageAtlas serves as a user-friendly platform for data operations on cellular clones, facilitating the understanding of tumour heterogeneity, differentiation trajectories, and evolution. It thus contributes significantly to cancer research and clinical practice.
期刊介绍:
Huge volumes of primary data are archived in numerous open-access databases, and with new generation technologies becoming more common in laboratories, large datasets will become even more prevalent. The archiving, curation, analysis and interpretation of all of these data are a challenge. Database development and biocuration are at the forefront of the endeavor to make sense of this mounting deluge of data.
Database: The Journal of Biological Databases and Curation provides an open access platform for the presentation of novel ideas in database research and biocuration, and aims to help strengthen the bridge between database developers, curators, and users.