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 | http://www.bioconductor.org/packages/release/bioc/html/InPAS.html | [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 | https://bioconductor.org/packages/ release/bioc/html/APAlyzer.html | [146] |
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] |