DeepASmRNA: Reference-free prediction of alternative splicing events with a scalable and interpretable deep learning model

• Published in: iScience Vol. 25; no. 11; p. 105345
• Main Authors: Cao, Lei, Zhang, Quanbao, Song, Hongtao, Lin, Kui, Pang, Erli
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 18.11.2022; Elsevier
• Subjects: Artificial intelligence; Artificial intelligence applications; Biological sciences; Molecular biology; Molecular biology experimental approach; Artificial intelligence applications; Biological sciences; Molecular biology; Artificial intelligence; Molecular biology experimental approach
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2022.105345

Alternative splicing is crucial for a wide range of biological processes. However, limited by the availability of reference genomes, genome-wide patterns of alternative splicing remain unknown in most nonmodel organisms. We present an attention-based convolutional neural network model, DeepASmRNA, for predicting alternative splicing events using only transcriptomic data. DeepASmRNA consists of two parts: identification of alternatively spliced transcripts and classification of alternative splicing events, which outperformed the state-of-the-art method, AStrap, and other deep learning models. Then, we utilize transfer learning to increase the performance in species with limited training data and use an interpretation method to decipher splicing codes. Finally, applying Amborella, DeepASmRNA can identify more AS events than AStrap while maintaining the same level of precision, suggesting that DeepASmRNA has superior sensitivity to identify alternative splicing events. In summary, DeepASmRNA is scalable and interpretable for detecting genome-wide patterns of alternative splicing in species without a reference genome. [Display omitted] •DeepASmRNA uses only the transcriptome to predict alternative splicing events•DeepASmRNA identifies adjacent HSPs to greatly improve the recall•DeepASmRNA uses attention-based convolutional neural network to classify AS events•Transfer learning is used to increase the predictive power of a target species Biological sciences; Molecular biology; Molecular biology experimental approach; Artificial intelligence; Artificial intelligence applications