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singleCellBase: a high-quality manually curated database of cell markers for single cell annotation across multiple species

Annotating cells in the analysis of single-cell RNA-seq (scRNA-seq) data is one of the most challenging tasks that researchers are actively addressing. Manual cell annotation is generally considered the gold standard method, although it is labor intensive and independent of prior knowledge. At prese...

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Autores principales: Meng, Fan-Lin, Huang, Xiao-Ling, Qin, Wen-Yan, Liu, Kun-Bang, Wang, Yan, Li, Ming, Ren, Yong-Hong, Li, Yan-Ze, Sun, Yi-Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10510128/
https://www.ncbi.nlm.nih.gov/pubmed/37730627
http://dx.doi.org/10.1186/s40364-023-00523-3
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author Meng, Fan-Lin
Huang, Xiao-Ling
Qin, Wen-Yan
Liu, Kun-Bang
Wang, Yan
Li, Ming
Ren, Yong-Hong
Li, Yan-Ze
Sun, Yi-Min
author_facet Meng, Fan-Lin
Huang, Xiao-Ling
Qin, Wen-Yan
Liu, Kun-Bang
Wang, Yan
Li, Ming
Ren, Yong-Hong
Li, Yan-Ze
Sun, Yi-Min
author_sort Meng, Fan-Lin
collection PubMed
description Annotating cells in the analysis of single-cell RNA-seq (scRNA-seq) data is one of the most challenging tasks that researchers are actively addressing. Manual cell annotation is generally considered the gold standard method, although it is labor intensive and independent of prior knowledge. At present, the relationship between high-quality, known marker genes and cell types is very limited, especially for a variety of species other than humans and mice. The singleCellBase is a manually curated resource of high-quality cell types and gene markers associations across multiple species. In details, it offers 9,158 entries spanning a total of 1,221 cell types and linking with 8,740 genes (cell markers), covering 464 diseases/status, and 165 types of tissues across 31 species. The singleCellBase provides a user-friendly interface to the scientific community to browse, search, download and submit records of marker genes and cell types. The resource providing ineluctable prior knowledge required by manual cell annotation, which is valuable to interpret scRNA-seq data and elucidate what cell type or cell state that a cell population represents. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40364-023-00523-3.
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spelling pubmed-105101282023-09-21 singleCellBase: a high-quality manually curated database of cell markers for single cell annotation across multiple species Meng, Fan-Lin Huang, Xiao-Ling Qin, Wen-Yan Liu, Kun-Bang Wang, Yan Li, Ming Ren, Yong-Hong Li, Yan-Ze Sun, Yi-Min Biomark Res Correspondence Annotating cells in the analysis of single-cell RNA-seq (scRNA-seq) data is one of the most challenging tasks that researchers are actively addressing. Manual cell annotation is generally considered the gold standard method, although it is labor intensive and independent of prior knowledge. At present, the relationship between high-quality, known marker genes and cell types is very limited, especially for a variety of species other than humans and mice. The singleCellBase is a manually curated resource of high-quality cell types and gene markers associations across multiple species. In details, it offers 9,158 entries spanning a total of 1,221 cell types and linking with 8,740 genes (cell markers), covering 464 diseases/status, and 165 types of tissues across 31 species. The singleCellBase provides a user-friendly interface to the scientific community to browse, search, download and submit records of marker genes and cell types. The resource providing ineluctable prior knowledge required by manual cell annotation, which is valuable to interpret scRNA-seq data and elucidate what cell type or cell state that a cell population represents. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40364-023-00523-3. BioMed Central 2023-09-20 /pmc/articles/PMC10510128/ /pubmed/37730627 http://dx.doi.org/10.1186/s40364-023-00523-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Correspondence
Meng, Fan-Lin
Huang, Xiao-Ling
Qin, Wen-Yan
Liu, Kun-Bang
Wang, Yan
Li, Ming
Ren, Yong-Hong
Li, Yan-Ze
Sun, Yi-Min
singleCellBase: a high-quality manually curated database of cell markers for single cell annotation across multiple species
title singleCellBase: a high-quality manually curated database of cell markers for single cell annotation across multiple species
title_full singleCellBase: a high-quality manually curated database of cell markers for single cell annotation across multiple species
title_fullStr singleCellBase: a high-quality manually curated database of cell markers for single cell annotation across multiple species
title_full_unstemmed singleCellBase: a high-quality manually curated database of cell markers for single cell annotation across multiple species
title_short singleCellBase: a high-quality manually curated database of cell markers for single cell annotation across multiple species
title_sort singlecellbase: a high-quality manually curated database of cell markers for single cell annotation across multiple species
topic Correspondence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10510128/
https://www.ncbi.nlm.nih.gov/pubmed/37730627
http://dx.doi.org/10.1186/s40364-023-00523-3
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