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

ABSTRACT: 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, Dee

SUBMITTER: Cao L 

PROVIDER: S-EPMC9619290 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature