Affiliation:
1. School of Computer Science and Engineering, Nanyang Techonological University, 50 Nanyang Avenue, Singapore
2. School of Mathematical Sciences, Dalian University of Technology, No.2 Linggong Road, Dalian, China
Abstract
Abstract
The development of deep sequencing technologies has led to the discovery of novel transcripts. Many in silico methods have been developed to assess the coding potential of these transcripts to further investigate their functions. Existing methods perform well on distinguishing majority long noncoding RNAs (lncRNAs) and coding RNAs (mRNAs) but poorly on RNAs with small open reading frames (sORFs). Here, we present DeepCPP (deep neural network for coding potential prediction), a deep learning method for RNA coding potential prediction. Extensive evaluations on four previous datasets and six new datasets constructed in different species show that DeepCPP outperforms other state-of-the-art methods, especially on sORF type data, which overcomes the bottleneck of sORF mRNA identification by improving more than 4.31, 37.24 and 5.89% on its accuracy for newly discovered human, vertebrate and insect data, respectively. Additionally, we also revealed that discontinuous k-mer, and our newly proposed nucleotide bias and minimal distribution similarity feature selection method play crucial roles in this classification problem. Taken together, DeepCPP is an effective method for RNA coding potential prediction.
Funder
Ministry of Education Academic Research
National Research Foundation
National Natural Science Foundation of Liaoning Province
Fundamental Research Funds
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems
Cited by
41 articles.
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