MAAPER: model-based analysis of alternative polyadenylation using 3′ end-linked reads

Most eukaryotic genes express alternative polyadenylation (APA) isoforms. A growing number of RNA sequencing methods, especially those used for single-cell transcriptome analysis, generate reads close to the polyadenylation site (PAS), termed nearSite reads, hence inherently containing information a...

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Detalles Bibliográficos
Autores principales: Li, Wei Vivian, Zheng, Dinghai, Wang, Ruijia, Tian, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8356463/
https://www.ncbi.nlm.nih.gov/pubmed/34376236
http://dx.doi.org/10.1186/s13059-021-02429-5
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author Li, Wei Vivian
Zheng, Dinghai
Wang, Ruijia
Tian, Bin
author_facet Li, Wei Vivian
Zheng, Dinghai
Wang, Ruijia
Tian, Bin
author_sort Li, Wei Vivian
collection PubMed
description Most eukaryotic genes express alternative polyadenylation (APA) isoforms. A growing number of RNA sequencing methods, especially those used for single-cell transcriptome analysis, generate reads close to the polyadenylation site (PAS), termed nearSite reads, hence inherently containing information about APA isoform abundance. Here, we present a probabilistic model-based method named MAAPER to utilize nearSite reads for APA analysis. MAAPER predicts PASs with high accuracy and sensitivity and examines different types of APA events with robust statistics. We show MAAPER’s performance with both bulk and single-cell data and its applicability in unpaired or paired experimental designs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02429-5.
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spelling pubmed-83564632021-08-16 MAAPER: model-based analysis of alternative polyadenylation using 3′ end-linked reads Li, Wei Vivian Zheng, Dinghai Wang, Ruijia Tian, Bin Genome Biol Method Most eukaryotic genes express alternative polyadenylation (APA) isoforms. A growing number of RNA sequencing methods, especially those used for single-cell transcriptome analysis, generate reads close to the polyadenylation site (PAS), termed nearSite reads, hence inherently containing information about APA isoform abundance. Here, we present a probabilistic model-based method named MAAPER to utilize nearSite reads for APA analysis. MAAPER predicts PASs with high accuracy and sensitivity and examines different types of APA events with robust statistics. We show MAAPER’s performance with both bulk and single-cell data and its applicability in unpaired or paired experimental designs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02429-5. BioMed Central 2021-08-10 /pmc/articles/PMC8356463/ /pubmed/34376236 http://dx.doi.org/10.1186/s13059-021-02429-5 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, Wei Vivian
Zheng, Dinghai
Wang, Ruijia
Tian, Bin
MAAPER: model-based analysis of alternative polyadenylation using 3′ end-linked reads
title MAAPER: model-based analysis of alternative polyadenylation using 3′ end-linked reads
title_full MAAPER: model-based analysis of alternative polyadenylation using 3′ end-linked reads
title_fullStr MAAPER: model-based analysis of alternative polyadenylation using 3′ end-linked reads
title_full_unstemmed MAAPER: model-based analysis of alternative polyadenylation using 3′ end-linked reads
title_short MAAPER: model-based analysis of alternative polyadenylation using 3′ end-linked reads
title_sort maaper: model-based analysis of alternative polyadenylation using 3′ end-linked reads
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8356463/
https://www.ncbi.nlm.nih.gov/pubmed/34376236
http://dx.doi.org/10.1186/s13059-021-02429-5
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