DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning

Zhongxiao Li, Yisheng Li, Bin Zhang, Yu Li, Yongkang Long, Juexiao Zhou, Xudong Zou, Min Zhang, Yuhui Hu, Wei Chen, Xin Gao

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Alternative polyadenylation (APA) is a crucial step in post-transcriptional regulation. Previous bioinformatic works have mainly focused on the recognition of polyadenylation sites (PASs) in a given genomic sequence, which is a binary classification problem. Recently, computational methods for predicting the usage level of alternative PASs in a same gene have been proposed. However, all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into account. To address this, here we propose a deep learning architecture, DeeReCT-APA, to quantitatively predict the usage of all alternative PASs of a given gene. To accommodate different genes with potentially different numbers of PASs, DeeReCT-APA treats the problem as a regression task with a variable-length target. Based on a CNN-LSTM architecture, DeeReCT-APA extracts sequence features with CNN layers, uses bidirectional LSTM to explicitly model the interactions among competing PASs, and outputs percentage scores representing the usage levels of all PASs of a gene. In addition to the fact that only our method can predict quantitatively the usage of all the PASs within a gene, we show that our method consistently outperforms other existing methods on three different tasks for which they are trained: pairwise comparison task, highest usage prediction task, and ranking task. Finally, we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and shed light on future mechanistic understanding in APA regulation. Our code and data are available at
Original languageEnglish (US)
JournalGenomics, Proteomics & Bioinformatics
StatePublished - Mar 2 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-03-05
Acknowledged KAUST grant number(s): BAS/1/1624-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, URF/1/4098-01-01
Acknowledgements: This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) (Grant Nos. URF/1/4098-01-01, BAS/1/1624-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, and FCS/1/4102-02-01), the International Cooperation Research Grant from Science and Technology Innovation Commission of Shenzhen Municipal Government, China (Grant No. GJHZ20170310161947503 to YH), and the Shenzhen Science and Technology Program, China (Grant No. KQTD20180411143432337 to YH and WC).

ASJC Scopus subject areas

  • Biochemistry
  • Genetics
  • Computational Mathematics
  • Molecular Biology


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