DeepPASTA: deep neural network based polyadenylation site analysis

Author:

Arefeen Ashraful1ORCID,Xiao Xinshu2,Jiang Tao134

Affiliation:

1. Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA

2. Department of Integrative Biology and Physiology, University of California, Los Angeles, CA 90095, USA

3. Institute of Integrative Genome Biology, University of California, Riverside, CA 92521, USA

4. Bioinformatics Division, BNRIST, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Abstract

Abstract Motivation Alternative polyadenylation (polyA) sites near the 3′ end of a pre-mRNA create multiple mRNA transcripts with different 3′ untranslated regions (3′ UTRs). The sequence elements of a 3′ UTR are essential for many biological activities such as mRNA stability, sub-cellular localization, protein translation, protein binding and translation efficiency. Moreover, numerous studies in the literature have reported the correlation between diseases and the shortening (or lengthening) of 3′ UTRs. As alternative polyA sites are common in mammalian genes, several machine learning tools have been published for predicting polyA sites from sequence data. These tools either consider limited sequence features or use relatively old algorithms for polyA site prediction. Moreover, none of the previous tools consider RNA secondary structures as a feature to predict polyA sites. Results In this paper, we propose a new deep learning model, called DeepPASTA, for predicting polyA sites from both sequence and RNA secondary structure data. The model is then extended to predict tissue-specific polyA sites. Moreover, the tool can predict the most dominant (i.e. frequently used) polyA site of a gene in a specific tissue and relative dominance when two polyA sites of the same gene are given. Our extensive experiments demonstrate that DeepPASTA signisficantly outperforms the existing tools for polyA site prediction and tissue-specific relative and absolute dominant polyA site prediction. Availability and implementation https://github.com/arefeen/DeepPASTA Supplementary information Supplementary data are available at Bioinformatics online.

Funder

NSF

NIH

NSFC

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference54 articles.

1. Polyar, a new computer program for prediction of poly(A) sites in human sequences;Akhtar;BMC Genomics,2010

2. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning;Alipanahi;Nat. Biotechnol,2015

3. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning;Angermueller;Genome Biol,2017

4. Dragon PolyA Spotter: prediction of poly(A) motifs within human genomic sequences;Bajic;Bioinformatics,2012

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