Accurate transcriptome-wide identification and quantification of alternative polyadenylation from RNA-seq data with APAIQ

Yongkang Long, Bin Zhang, Shuye Tian, Jia Jia Chan, Juexiao Zhou, Zhongxiao Li, Yisheng Li, Zheng An, Xingyu Liao, Yu Wang, Shiwei Sun, Ying Xu, Yvonne Tay, Wei Chen, Xin Gao

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.
Original languageEnglish (US)
JournalGenome Research
StatePublished - Apr 28 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-05-01
Acknowledged KAUST grant number(s): FCC/1/1976-44-01, FCC/1/1976-45-01, REI/1/4940-01-01, REI/1/5202-01-01, URF/1/4098-01-01, URF/1/4352-01-01, URF/1/4379-01-01, URF/1/ 4663-01-01
Acknowledgements: We thank all past and present members in the Structure and Functional Bioinformatics Group for their assistance and constructive feedback on this project. We also thank KAUST-HPC for providing generous support on computational resources. This work was supported by King Abdullah University of Science and Technology (KAUST) Office Administration (ORA) under Award Nos. FCC/1/1976-44-1, FCC/1/1976-44-01, FCC/1/1976-45-01, URF/1/4098-01-01, URF/1/4352-01-01, URF/1/4379-01-01, URF/1/ 4663-01-01, REI/1/5202-01-01, and REI/1/4940-01-01; National Key Research and Development Program of China (Grant No. 2021YFF1201000); National Nature Science Foundation of China (Grant Nos. 62002388, 32100431, 31970601); Shenzhen Science and Technology Program (Grant No. KQTD20180411143432337); and Shenzhen–Hong Kong Institute of Brain Science–Shenzhen Fundamental Research Institutions (Grant No. 2021SHIBS0002).

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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