当前位置: X-MOL 学术J. Bioinform. Comput. Biol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
SpliceViNCI: Visualizing the splicing of non-canonical introns through recurrent neural networks
Journal of Bioinformatics and Computational Biology ( IF 1 ) Pub Date : 2021-06-04 , DOI: 10.1142/s0219720021500141
Aparajita Dutta 1 , Kusum Kumari Singh 2 , Ashish Anand 1
Affiliation  

Most of the current computational models for splice junction prediction are based on the identification of canonical splice junctions. However, it is observed that the junctions lacking the consensus dimers GT and AG also undergo splicing. Identification of such splice junctions, called the non-canonical splice junctions, is also essential for a comprehensive understanding of the splicing phenomenon. This work focuses on the identification of non-canonical splice junctions through the application of a bidirectional long short-term memory (BLSTM) network. Furthermore, we apply a back-propagation-based (integrated gradient) and a perturbation-based (occlusion) visualization techniques to extract the non-canonical splicing features learned by the model. The features obtained are validated with the existing knowledge from the literature. Integrated gradient extracts features that comprise contiguous nucleotides, whereas occlusion extracts features that are individual nucleotides distributed across the sequence.

中文翻译:

SpliceViNCI:通过循环神经网络可视化非规范内含子的剪接

目前大多数用于拼接点预测的计算模型都是基于规范拼接点的识别。然而,观察到缺乏共有二聚体 GT 和 AG 的连接也经历剪接。识别这种称为非规范剪接点的剪接点对于全面了解剪接现象也是必不可少的。这项工作的重点是通过应用双向长短期记忆 (BLSTM) 网络来识别非规范剪接点。此外,我们应用基于反向传播(集成梯度)和基于扰动(遮挡)的可视化技术来提取模型学习的非规范拼接特征。所获得的特征通过文献中的现有知识进行验证。
更新日期:2021-06-04
down
wechat
bug