Advances in alternative splicing identification: deep learning and pantranscriptome

Fei Shen; Chenyang Hu; Xin Huang; Hao He; Deng Yang; Jirong Zhao; Xiaozeng Yang

doi:10.3389/fpls.2023.1232466

Advances in alternative splicing identification: deep learning and pantranscriptome

Front Plant Sci. 2023 Sep 18:14:1232466. doi: 10.3389/fpls.2023.1232466. eCollection 2023.

Authors

Fei Shen¹, Chenyang Hu^{1

2}, Xin Huang¹, Hao He¹, Deng Yang¹, Jirong Zhao², Xiaozeng Yang¹

Affiliations

¹ Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
² Shanxi Key Lab of Chinese Jujube, College of Life Science, Yan'an University, Yan'an, Shanxi, China.

Abstract

In plants, alternative splicing is a crucial mechanism for regulating gene expression at the post-transcriptional level, which leads to diverse proteins by generating multiple mature mRNA isoforms and diversify the gene regulation. Due to the complexity and variability of this process, accurate identification of splicing events is a vital step in studying alternative splicing. This article presents the application of alternative splicing algorithms with or without reference genomes in plants, as well as the integration of advanced deep learning techniques for improved detection accuracy. In addition, we also discuss alternative splicing studies in the pan-genomic background and the usefulness of integrated strategies for fully profiling alternative splicing.

Keywords: Iso-seq; RNA-seq; alternative splicing; deep learning; detection algorithm; pantranscriptome.

Grants and funding

The authors declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (32102339), Beijing Academy of Agriculture and Forestry Sciences (YXQN202203, QNJJ202106).