RT Journal
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
JF Genome Research 
JO Genome Research 
YR 2023 
FD April 01 
VO 33 
IS 4 
SP 644 
OP 657 
DO 10.1101/gr.277177.122 
UL http://genome.cshlp.org/content/33/4/644.abstract 
AB 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.