ColorCells: a database of expression, classification and functions of lncRNAs in single cells

Author:

Zheng Ling-Ling1ORCID,Xiong Jing-Hua1,Zheng Wu-Jian1,Wang Jun-Hao1,Huang Zi-Liang1,Chen Zhi-Rong1,Sun Xin-Yao1,Zheng Yi-Min1,Zhou Ke-Ren2,Li Bin1,Liu Shun3,Qu Liang-Hu1,Yang Jian-Hua1ORCID

Affiliation:

1. Key Laboratory of Gene Engineering of the Ministry of Education, State Key Laboratory for Biocontrol, Sun Yat-sen University, Guangzhou 510275, P. R. China

2. Department of Systems Biology, Beckman Research Institute of City of Hope, Monrovia, California 91016, USA

3. Department of Chemistry and Institute for Biophysical Dynamics, the University of Chicago, Chicago, IL 60637, USA

Abstract

Abstract Although long noncoding RNAs (lncRNAs) have significant tissue specificity, their expression and variability in single cells remain unclear. Here, we developed ColorCells (http://rna.sysu.edu.cn/colorcells/), a resource for comparative analysis of lncRNAs expression, classification and functions in single-cell RNA-Seq data. ColorCells was applied to 167 913 publicly available scRNA-Seq datasets from six species, and identified a batch of cell-specific lncRNAs. These lncRNAs show surprising levels of expression variability between different cell clusters, and has the comparable cell classification ability as known marker genes. Cell-specific lncRNAs have been identified and further validated by in vitro experiments. We found that lncRNAs are typically co-expressed with the mRNAs in the same cell cluster, which can be used to uncover lncRNAs’ functions. Our study emphasizes the need to uncover lncRNAs in all cell types and shows the power of lncRNAs as novel marker genes at single cell resolution.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Guangdong Province

Guangzhou city

Pearl River S and T Nova Program of Guangzhou

Youth science and technology

Central Universities in China

Guangdong Province Key Laboratory of Computational Science

Guangdong Province Computational Science Innovative Research Team

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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