DeeReCT-PolyA: A robust and generic deep learning method for PAS identification

Xia，Zhihao1; Li，Yu2; Zhang，Bin3; Li，Zhongxiao2; Hu，Yuhui3; Chen，Wei3; Gao，Xin2

2019-07-15

10.1093/bioinformatics/bty991

BIOINFORMATICS   影响因子和分区

1367-4803

1460-2059

Motivation: Polyadenylation is a critical step for gene expression regulation during the maturation of mRNA. An accurate and robust method for poly(A) signals (PASs) identification is not only desired for the purpose of better transcripts' end annotation, but can also help us gain a deeper insight of the underlying regulatory mechanism. Although many methods have been proposed for PAS recognition, most of them are PAS motif- and human-specific, which leads to high risks of overfitting, low generalization power, and inability to reveal the connections between the underlying mechanisms of different mammals. Results: In this work, we propose a robust, PAS motif agnostic, and highly interpretable and transferrable deep learning model for accurate PAS recognition, which requires no prior knowledge or human-designed features. We show that our single model trained over all human PAS motifs not only outperforms the state-of-the-art methods trained on specific motifs, but can also be generalized well to two mouse datasets. Moreover, we further increase the prediction accuracy by transferring the deep learning model trained on the data of one species to the data of a different species. Several novel underlying poly(A) patterns are revealed through the visualization of important oligomers and positions in our trained models. Finally, we interpret the deep learning models by converting the convolutional filters into sequence logos and quantitatively compare the sequence logos between human and mouse datasets.

SCI

英语

通讯

WOS:000477703600074

BIOLOGY & BIOCHEMISTRY

2-s2.0-85068940235

Scopus

1.Department of Computer Science and Engineering (CSE)Washington University in St Louis,St Louis,63130,United States
2.ComputerElectrical and Mathematical Sciences and Engineering (CEMSE) DivisionKing Abdullah University of Science and Technology (KAUST)Computational Bioscience Research Center (CBRC),Thuwal,23955-6900,Saudi Arabia
3.Department of BiologySouthern University of Science and Technology (SUSTC),Shenzhen,518055,China

Xia，Zhihao,Li，Yu,Zhang，Bin,et al. DeeReCT-PolyA: A robust and generic deep learning method for PAS identification[J]. BIOINFORMATICS,2019,35(14):2371-2379.

Xia，Zhihao.,Li，Yu.,Zhang，Bin.,Li，Zhongxiao.,Hu，Yuhui.,...&Gao，Xin.(2019).DeeReCT-PolyA: A robust and generic deep learning method for PAS identification.BIOINFORMATICS,35(14),2371-2379.

Xia，Zhihao,et al."DeeReCT-PolyA: A robust and generic deep learning method for PAS identification".BIOINFORMATICS 35.14(2019):2371-2379.