TY  - JOUR
A1  - Long, Yongkang
A1  - Zhang, Bin
A1  - Tian, Shuye
A1  - Chan, Jia Jia
A1  - Zhou, Juexiao
A1  - Li, Zhongxiao
A1  - Li, Yisheng
A1  - An, Zheng
A1  - Liao, Xingyu
A1  - Wang, Yu
A1  - Sun, Shiwei
A1  - Xu, Ying
A1  - Tay, Yvonne
A1  - Chen, Wei
A1  - Gao, Xin
T1  - Accurate transcriptome-wide identification and quantification of alternative polyadenylation from RNA-seq data with APAIQ
Y1  - 2023/04/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 644 
EP  - 657 
DO  - 10.1101/gr.277177.122 
VL  - 33 
IS  - 4 
UR  - http://genome.cshlp.org/content/33/4/644.abstract 
N2  - Alternative polyadenylation (APA) enables a gene to generate multiple transcripts with different 3′ ends, which is dynamic across different cell types or conditions. Many computational methods have been developed to characterize sample-specific APA using the corresponding RNA-seq data, but suffered from high error rate on both polyadenylation site (PAS) identification and quantification of PAS usage (PAU), and bias toward 3′ untranslated regions. Here we developed a tool for APA identification and quantification (APAIQ) from RNA-seq data, which can accurately identify PAS and quantify PAU in a transcriptome-wide manner. Using 3′ end-seq data as the benchmark, we showed that APAIQ outperforms current methods on PAS identification and PAU quantification, including DaPars2, Aptardi, mountainClimber, SANPolyA, and QAPA. Finally, applying APAIQ on 421 RNA-seq samples from liver cancer patients, we identified >540 tumor-associated APA events and experimentally validated two intronic polyadenylation candidates, demonstrating its capacity to unveil cancer-related APA with a large-scale RNA-seq data set. 
ER  -