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

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 wit...

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Autores principales: Li, Guo-Wei, Nan, Fang, Yuan, Guo-Hua, Liu, Chu-Xiao, Liu, Xindong, Chen, Ling-Ling, Tian, Bin, Yang, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8353616/
https://www.ncbi.nlm.nih.gov/pubmed/34376223
http://dx.doi.org/10.1186/s13059-021-02437-5
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author Li, Guo-Wei
Nan, Fang
Yuan, Guo-Hua
Liu, Chu-Xiao
Liu, Xindong
Chen, Ling-Ling
Tian, Bin
Yang, Li
author_facet Li, Guo-Wei
Nan, Fang
Yuan, Guo-Hua
Liu, Chu-Xiao
Liu, Xindong
Chen, Ling-Ling
Tian, Bin
Yang, Li
author_sort Li, Guo-Wei
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02437-5.
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spelling pubmed-83536162021-08-10 SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based RNA-seq of single cells Li, Guo-Wei Nan, Fang Yuan, Guo-Hua Liu, Chu-Xiao Liu, Xindong Chen, Ling-Ling Tian, Bin Yang, Li Genome Biol Method 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02437-5. BioMed Central 2021-08-10 /pmc/articles/PMC8353616/ /pubmed/34376223 http://dx.doi.org/10.1186/s13059-021-02437-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Method
Li, Guo-Wei
Nan, Fang
Yuan, Guo-Hua
Liu, Chu-Xiao
Liu, Xindong
Chen, Ling-Ling
Tian, Bin
Yang, Li
SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based RNA-seq of single cells
title 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_short 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
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8353616/
https://www.ncbi.nlm.nih.gov/pubmed/34376223
http://dx.doi.org/10.1186/s13059-021-02437-5
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