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DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature

Yu Zhang1, Cangzhi Jia2, Melissa Jane Fullwood1

  • 1School of Computer Science and Engineering, Nanyang Techonological University, 50 Nanyang Avenue, Singapore.

Briefings in Bioinformatics
|April 1, 2020
PubMed
Summary

DeepCPP, a deep learning tool, accurately predicts RNA coding potential, especially for small open reading frame (sORF) RNAs. This advances the identification of novel transcripts and their functions.

Keywords:
RNA coding potentialdeep learninglong noncoding RNAssORF RNA

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Deep sequencing technologies have revealed novel RNA transcripts.
  • In silico methods are crucial for assessing RNA coding potential and function.
  • Current methods struggle to differentiate small open reading frame (sORF) RNAs from non-coding RNAs.

Purpose of the Study:

  • To introduce DeepCPP, a deep learning model for enhanced RNA coding potential prediction.
  • To address the limitations of existing methods in identifying sORF RNAs.
  • To improve the accuracy of distinguishing coding and non-coding RNAs, particularly sORFs.

Main Methods:

  • Development of DeepCPP, a deep neural network for coding potential prediction.
  • Utilized discontinuous k-mer, nucleotide bias, and minimal distribution similarity features.
  • Evaluated performance on multiple datasets across different species.

Main Results:

  • DeepCPP significantly outperforms existing state-of-the-art methods.
  • Demonstrated substantial accuracy improvements for sORF RNA identification in humans (4.31%), vertebrates (37.24%), and insects (5.89%).
  • Identified key features contributing to accurate classification.

Conclusions:

  • DeepCPP is an effective and robust method for RNA coding potential prediction.
  • The model overcomes previous bottlenecks in sORF mRNA identification.
  • Deep learning approaches show promise for advancing transcriptomic analysis.