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Table 1 Computational tools for detecting APA

From: Alternative polyadenylation: methods, mechanism, function, and role in cancer

Name Description Environment Year Website Ref.
InPAS A package that can detect the dynamics of APA events from RNA-seq data by removing false sites due to internal-priming. R 2013 [137]
ChangePoint A change-point model based on a likelihood ratio test for detecting 3’UTR switching. Java 2014 [13]
DaPars A bioinformatics algorithm for the de novo identification of dynamic APAs from standard RNA-seq. Python 2014 [8]
Roar A strategy for detecting alternative PAS usage and comparing these between two biological conditions. R 2016 [138]
QAPA An approach to infer and quantify APA from RNA-seq data. Python & R 2018 [139]
PAQR_KAPAC A combined method that can quantify PAS usage from RNA-seq data and infer regulatory sequence motifs on PAS usage. Python & R 2018 [120]
APAtrap An approach to identify and quantify APA sites from RNA-seq data based on the mean squared error model. R 2018 [140]
IntMap An integrated method for detecting novel APA events from RNA-seq and PAS-seq data. Matlab 2018 [141]
TAPAS A tool that can detect more than two APA sites in a gene and APA sites before the last exon from RNA-seq data. R 2018 [142]
APARENT A deep learning approach to predict APA from DNA sequences. Python 2019 [143]
DeepPASTA A deep learning method to predict APA from DNA sequences and RNA secondary structure data. Python 2019 [144]
scDAPA A tool to detect and visualize APA events from single-cell RNA-seq data. R 2019 [145]
APAlyzer A bioinformatics package which can examine 3’UTR-APA, intronic APA, and gene expression changes using RNA-seq data. R 2020
APA-Scan A robust program that infers 3’UTR-APA events and visualizes the RNA-seq short-read coverage with gene annotations. Python 2020 [147]