Your browser doesn't support javascript.
loading
scATAC-Ref: a reference of scATAC-seq with known cell labels in multiple species.
Qian, Feng-Cui; Zhou, Li-Wei; Zhu, Yan-Bing; Li, Yan-Yu; Yu, Zheng-Min; Feng, Chen-Chen; Fang, Qiao-Li; Zhao, Yu; Cai, Fu-Hong; Wang, Qiu-Yu; Tang, Hui-Fang; Li, Chun-Quan.
Afiliación
  • Qian FC; The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
  • Zhou LW; Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
  • Zhu YB; The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
  • Li YY; State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
  • Yu ZM; Beijing Clinical Research Institute, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Feng CC; School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
  • Fang QL; School of Computer, University of South China, Hengyang, Hunan, 421001, China.
  • Zhao Y; School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
  • Cai FH; School of Computer, University of South China, Hengyang, Hunan, 421001, China.
  • Wang QY; School of Computer, University of South China, Hengyang, Hunan, 421001, China.
  • Tang HF; School of Computer, University of South China, Hengyang, Hunan, 421001, China.
  • Li CQ; The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Nucleic Acids Res ; 52(D1): D285-D292, 2024 Jan 05.
Article en En | MEDLINE | ID: mdl-37897340
ABSTRACT
Chromatin accessibility profiles at single cell resolution can reveal cell type-specific regulatory programs, help dissect highly specialized cell functions and trace cell origin and evolution. Accurate cell type assignment is critical for effectively gaining biological and pathological insights, but is difficult in scATAC-seq. Hence, by extensively reviewing the literature, we designed scATAC-Ref (https//bio.liclab.net/scATAC-Ref/), a manually curated scATAC-seq database aimed at providing a comprehensive, high-quality source of chromatin accessibility profiles with known cell labels across broad cell types. Currently, scATAC-Ref comprises 1 694 372 cells with known cell labels, across various biological conditions, >400 cell/tissue types and five species. We used uniform system environment and software parameters to perform comprehensive downstream analysis on these chromatin accessibility profiles with known labels, including gene activity score, TF enrichment score, differential chromatin accessibility regions, pathway/GO term enrichment analysis and co-accessibility interactions. The scATAC-Ref also provided a user-friendly interface to query, browse and visualize cell types of interest, thereby providing a valuable resource for exploring epigenetic regulation in different tissues and cell types.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Cromatina / Bases de Datos Genéticas / Análisis de la Célula Individual / Secuenciación de Inmunoprecipitación de Cromatina Límite: Animals / Humans Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Cromatina / Bases de Datos Genéticas / Análisis de la Célula Individual / Secuenciación de Inmunoprecipitación de Cromatina Límite: Animals / Humans Idioma: En Año: 2024 Tipo del documento: Article