SCAPE-APA: a package for estimating alternative polyadenylation events from scRNA-seq data

<ns4:p>SCAPE was a package that was previously developed to estimate alternative polyadenylation events from single-cell RNA-seq (scRNA-seq) data. It was composed of ad-hoc python scripts and has speed issues when handling large scRNA-seq data. To suit the needs of analyzing large scRNA-seq datasets, we present SCAPE-APA, which is a reimplementation of SCAPE with substantial changes. We made the following updates to the package: (1) we binned similar reads together to accelerate the estimation; (2) we re-derived the mixture model to tailor it for binned reads; (3) we implemented the inference algorithm using Taichi language for acceleration; (4) we re-implemented the untranslated region (UTR) annotation extraction script using the professional package gffutils for better maintenance; (5) we wrote a script to detect spurious alternative polyadenylation sites generated due to junction reads, and (6) we created a formal Python package and uploaded it to the Python Package Index website (Pypi).</ns4:p>

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Publication Details

Journal
Open Research Europe
Published
2025-03-07
DOI
https://doi.org/10.12688/openreseurope.18673.3
Primary Topic
RNA Research and Splicing
Type
article
Field-Weighted Citation Impact
0.00

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article

SCAPE-APA: a package for estimating alternative polyadenylation events from scRNA-seq data

Ran Zhou, Tien Le, Guangzhao Cheng, Lu Cheng
Open Research Europe
RNA Research and Splicing
article

SCAPE-APA: a package for estimating alternative polyadenylation events from scRNA-seq data

Ran Zhou, Tien Le, Guangzhao Cheng, Lu Cheng
article en

Abstract

<ns4:p>SCAPE was a package that was previously developed to estimate alternative polyadenylation events from single-cell RNA-seq (scRNA-seq) data. It was composed of ad-hoc python scripts and has speed issues when handling large scRNA-seq data. To suit the needs of analyzing large scRNA-seq datasets, we present SCAPE-APA, which is a reimplementation of SCAPE with substantial changes. We made the following updates to the package: (1) we binned similar reads together to accelerate the estimation; (2) we re-derived the mixture model to tailor it for binned reads; (3) we implemented the inference algorithm using Taichi language for acceleration; (4) we re-implemented the untranslated region (UTR) annotation extraction script using the professional package gffutils for better maintenance; (5) we wrote a script to detect spurious alternative polyadenylation sites generated due to junction reads, and (6) we created a formal Python package and uploaded it to the Python Package Index website (Pypi).</ns4:p>

Open Research EuropeVol. 4
Horizon 2020 Framework Programme
Openalex Percentile: Top 96%
RNA Research and Splicing
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