SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based RNA-seq of single cells

Genome Biology

Guo-Wei Li¹  Fang Nan¹  Guo-Hua Yuan¹  Li Yang²  Xindong Liu³  Bin Tian⁴  Chu-Xiao Liu⁵  Ling-Ling Chen⁶ 

[1] CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 200031, Shanghai, China;CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 200031, Shanghai, China;School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China;Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), 400038, Chongqing, China;Program in Gene Expression and Regulation, and Center for Systems and Computational Biology, The Wistar Institute, 19104, Philadelphia, PA, USA;State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, 200031, Shanghai, China;State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, 200031, Shanghai, China;School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China;School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 310024, Hangzhou, China;

关键词: scRNA-seq;    PAS;    APA;    Deep learning;    Peak calling;    Transcript quantification;   

DOI  :  10.1186/s13059-021-02437-5

来源: Springer

【 摘 要 】

Single-cell RNA-seq (scRNA-seq) profiles gene expression with high resolution. Here, we develop a stepwise computational method-called SCAPTURE to identify, evaluate, and quantify cleavage and polyadenylation sites (PASs) from 3′ tag-based scRNA-seq. SCAPTURE detects PASs de novo in single cells with high sensitivity and accuracy, enabling detection of previously unannotated PASs. Quantified alternative PAS transcripts refine cell identity analysis beyond gene expression, enriching information extracted from scRNA-seq data. Using SCAPTURE, we show changes of PAS usage in PBMCs from infected versus healthy individuals at single-cell resolution.

【 授权许可】

CC BY