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

Abstract 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...

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Main Authors:	Guo-Wei Li, Fang Nan, Guo-Hua Yuan, Chu-Xiao Liu, Xindong Liu, Ling-Ling Chen, Bin Tian, Li Yang
Format:	Article
Language:	English
Published:	BMC 2021-08-01
Series:	Genome Biology
Subjects:	scRNA-seq PAS APA Deep learning Peak calling Transcript quantification
Online Access:	https://doi.org/10.1186/s13059-021-02437-5

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spelling	doaj-f3a3c7780e4e44299ca918ddc95afacf2021-08-15T11:45:49ZengBMCGenome Biology1474-760X2021-08-0122112410.1186/s13059-021-02437-5SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based RNA-seq of single cellsGuo-Wei Li0Fang Nan1Guo-Hua Yuan2Chu-Xiao Liu3Xindong Liu4Ling-Ling Chen5Bin Tian6Li Yang7CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of SciencesCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of SciencesCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of SciencesState 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 SciencesInstitute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University)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 SciencesProgram in Gene Expression and Regulation, and Center for Systems and Computational Biology, The Wistar InstituteCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of SciencesAbstract 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.https://doi.org/10.1186/s13059-021-02437-5scRNA-seqPASAPADeep learningPeak callingTranscript quantification
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Guo-Wei Li Fang Nan Guo-Hua Yuan Chu-Xiao Liu Xindong Liu Ling-Ling Chen Bin Tian Li Yang
spellingShingle	Guo-Wei Li Fang Nan Guo-Hua Yuan Chu-Xiao Liu Xindong Liu Ling-Ling Chen Bin Tian Li Yang SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based RNA-seq of single cells Genome Biology scRNA-seq PAS APA Deep learning Peak calling Transcript quantification
author_facet	Guo-Wei Li Fang Nan Guo-Hua Yuan Chu-Xiao Liu Xindong Liu Ling-Ling Chen Bin Tian Li Yang
author_sort	Guo-Wei Li
title	SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based RNA-seq of single cells
title_short	SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based RNA-seq of single cells
title_full	SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based RNA-seq of single cells
title_fullStr	SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based RNA-seq of single cells
title_full_unstemmed	SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based RNA-seq of single cells
title_sort	scapture: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based rna-seq of single cells
publisher	BMC
series	Genome Biology
issn	1474-760X
publishDate	2021-08-01
description	Abstract 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.
topic	scRNA-seq PAS APA Deep learning Peak calling Transcript quantification
url	https://doi.org/10.1186/s13059-021-02437-5
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SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based RNA-seq of single cells

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