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. 2021 Dec 30;22(5):384-390.
doi: 10.2174/1389202922666211011143008.

Splice Junction Identification using Long Short-Term Memory Neural Networks

Kevin Regan  1 , Abolfazl Saghafi  2 , Zhijun Li  1
Affiliations

Splice Junction Identification using Long Short-Term Memory Neural Networks

Kevin Regan et al. Curr Genomics. .

Abstract

Background: Splice junctions are the key to move from pre-messenger RNA to mature messenger RNA in many multi-exon genes due to alternative splicing. Since the percentage of multi-exon genes that undergo alternative splicing is very high, identifying splice junctions is an attractive research topic with important implications.

Objective: The aim of this paper is to develop a deep learning model capable of identifying splice junctions in RNA sequences using 13,666 unique sequences of primate RNA.

Methods: A Long Short-Term Memory (LSTM) Neural Network model is developed that classifies a given sequence as EI (Exon-Intron splice), IE (Intron-Exon splice), or N (No splice). The model is trained with groups of trinucleotides and its performance is tested using validation and test data to prevent bias.

Results: Model performance was measured using accuracy and f-score in test data. The finalized model achieved an average accuracy of 91.34% with an average f-score of 91.36% over 50 runs.

Conclusion: Comparisons show a highly competitive model to recent Convolutional Neural Network structures. The proposed LSTM model achieves the highest accuracy and f-score among published alternative LSTM structures.

Keywords: LSTM; RNA-seq; Splice junction; classification; deep learning; neural networks.

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Figures

Fig. (1)
Fig. (1)
Pre-mRNA spliced into mature mRNA (figure obtained from https://www.khanacademy.org). (A higher resolution / colour version of this figure is available in the electronic copy of the article).
Fig. (2)
Fig. (2)
Outline of the model development process. (A higher resolution / colour version of this figure is available in the electronic copy of the article).
Fig. (3)
Fig. (3)
Outline of the developed deep learning model. (A higher resolution / colour version of this figure is available in the electronic copy of the article).
Fig. (4)
Fig. (4)
Distribution of accuracy and f-score in test set for 50 runs of the developed LSTM model (in blue) and a CNN model for comparison (in gray). (A higher resolution / colour version of this figure is available in the electronic copy of the article).
See this image and copyright information in PMC

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