DeeReCT-PolyA: a robust and generic deep learning method for PAS identification
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Computational Bioscience Research Center (CBRC)
Online Publication Date2018-11-30
Print Publication Date2019-07-15
Permanent link to this recordhttp://hdl.handle.net/10754/630192
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AbstractMotivation \nPolyadenylation is a critical step for gene expression regulation during the maturation of mRNA. An accurate and robust method for poly(A) signals (PAS) 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-specific 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. \nResults \nIn 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-theart methods trained on specific motifs, but can also be generalized well to two mouse data sets. 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.
CitationXia Z, Li Y, Zhang B, Li Z, Hu Y, et al. (2018) DeeReCT-PolyA: a robust and generic deep learning method for PAS identification. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/bty991.
SponsorsWe would like to thank Jeffery Jung and Min Zhang for insightful discussion. This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards No. FCC/1/1976-04, URF/1/2602-01, URF/1/3007-01, URF/1/3412-01, URF/1/3450-01 and URF/1/3454-01. Y.H. was supported by the International Cooperation Research Grant (No. GJHZ20170310161947503) from Science and Technology Innovation Commission of Shenzhen Municipal Government. W.C. was supported by Basic Research Grant (JCYJ20170307105752508) from Science and Technology Innovation Commission of Shenzhen Municipal Government.
PublisherOxford University Press (OUP)
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