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本文引用的文献

1
Aberrant splicing prediction across human tissues.跨人类组织的异常剪接预测
Nat Genet. 2023 May;55(5):861-870. doi: 10.1038/s41588-023-01373-3. Epub 2023 May 4.
2
Two Splicing Factors, U2AF65a and U2AF65b, Differentially Control Flowering Time by Modulating the Expression or Alternative Splicing of a Subset of Upstream Regulators.两种剪接因子U2AF65a和U2AF65b通过调节上游调控因子子集的表达或可变剪接来差异控制开花时间。
Plants (Basel). 2023 Apr 14;12(8):1655. doi: 10.3390/plants12081655.
3
Haplotype-aware pantranscriptome analyses using spliced pangenome graphs.基于拼接泛基因组图的单体型感知泛转录组分析。
Nat Methods. 2023 Feb;20(2):239-247. doi: 10.1038/s41592-022-01731-9. Epub 2023 Jan 16.
4
DeepASmRNA: Reference-free prediction of alternative splicing events with a scalable and interpretable deep learning model.DeepASmRNA：使用可扩展且可解释的深度学习模型对可变剪接事件进行无参考预测。
iScience. 2022 Oct 14;25(11):105345. doi: 10.1016/j.isci.2022.105345. eCollection 2022 Nov 18.
5
SWAP1-SFPS-RRC1 splicing factor complex modulates pre-mRNA splicing to promote photomorphogenesis in .SWAP1-SFPS-RRC1 剪接因子复合物调节前体 mRNA 剪接以促进. 的光形态建成。
Proc Natl Acad Sci U S A. 2022 Nov;119(44):e2214565119. doi: 10.1073/pnas.2214565119. Epub 2022 Oct 25.
6
Alternative Splicing and Its Roles in Plant Metabolism.可变剪接及其在植物代谢中的作用。
Int J Mol Sci. 2022 Jul 1;23(13):7355. doi: 10.3390/ijms23137355.
7
CI-SpliceAI-Improving machine learning predictions of disease causing splicing variants using curated alternative splice sites.CI-SpliceAI-利用已注释的可变剪接位点来改进疾病相关剪接变异体的机器学习预测。
PLoS One. 2022 Jun 3;17(6):e0269159. doi: 10.1371/journal.pone.0269159. eCollection 2022.
8
Deep Splicer: A CNN Model for Splice Site Prediction in Genetic Sequences.深度剪接体：用于预测遗传序列中剪接位点的 CNN 模型。
Genes (Basel). 2022 May 19;13(5):907. doi: 10.3390/genes13050907.
9
The Importance of a Genome-Wide Association Analysis in the Study of Alternative Splicing Mutations in Plants with a Special Focus on Maize.全基因组关联分析在研究植物选择性剪接突变中的重要性，特别关注玉米。
Int J Mol Sci. 2022 Apr 11;23(8):4201. doi: 10.3390/ijms23084201.
10
Splice Junction Identification using Long Short-Term Memory Neural Networks.使用长短期记忆神经网络进行剪接位点识别。
Curr Genomics. 2021 Dec 30;22(5):384-390. doi: 10.2174/1389202922666211011143008.

可变剪接识别的进展：深度学习与泛转录组

Advances in alternative splicing identification: deep learning and pantranscriptome.

作者信息

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

机构信息

Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.

Shanxi Key Lab of Chinese Jujube, College of Life Science, Yan'an University, Yan'an, Shanxi, China.

出版信息

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

DOI:10.3389/fpls.2023.1232466
PMID:37790793
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10544900/
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.

摘要

在植物中，可变剪接是转录后水平调控基因表达的关键机制，通过产生多种成熟的mRNA异构体导致产生多种蛋白质，并使基因调控多样化。由于这一过程的复杂性和可变性，准确识别剪接事件是研究可变剪接的关键步骤。本文介绍了有无参考基因组的可变剪接算法在植物中的应用，以及整合先进的深度学习技术以提高检测准确性。此外，我们还讨论了泛基因组背景下的可变剪接研究以及用于全面分析可变剪接的整合策略的实用性。