• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Human mutation >Predicting the impact of single nucleotide variants on splicing via sequence‐based deep neural networks and genomic features
【24h】

Predicting the impact of single nucleotide variants on splicing via sequence‐based deep neural networks and genomic features

机译：通过基于序列的深神经网络和基因组特征预测单核苷酸变体对剪接的影响

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Abstract Single nucleotide mutations in exonic regions can significantly affect gene function through a disruption of splicing, and various computational methods have been developed to predict the splicing‐related effects of a single nucleotide mutation. We implemented a new method using ensemble learning that combines two types of predictive models: (a) base sequence‐based deep neural networks (DNNs) and (b) machine learning models based on genomic attributes. This method was applied to the Massively Parallel Splicing Assay challenge of the Fifth Critical Assessment of Genome Interpretation, in which challenge participants predicted various experimentally‐defined exonic splicing mutations, and achieved a promising result. We successfully revealed that combining different predictive models based upon the stacked generalization method led to significant improvement in prediction performance. In addition, whereas most of the genomic features adopted in constructing machine learning models were previously reported, feature values generated with DSSP, a DNN‐based splice site prediction tool, were novel and helpful for the prediction. Learning the sequence patterns associated with normal splicing and the change in splicing site probabilities caused by a mutation was presumed to be helpful in predicting splicing disruption.
机译：摘要封锁区中的单核苷酸突变可以通过破坏剪接显着影响基因函数，并且已经开发了各种计算方法来预测单个核苷酸突变的剪接相关效果。我们使用集合学习实现了一种新方法，该方法结合了两种类型的预测模型：（a）基于基于序列的深神经网络（DNN）和（b）机器学习模型基于基因组属性。将该方法应用于基因组解释的第五次临界评估的大规模平行剪接测定挑战，其中挑战参与者预测各种实验鉴定的封面剪接突变，并实现了有希望的结果。我们成功地显示，基于堆叠的概括方法结合不同的预测模型，导致预测性能的显着改善。此外，此前，在构建机器学习模型中采用的大多数基因组特征，先前报道，用DSSP，基于DNN的剪接站点预测工具产生的特征值是新颖的并有助于预测。学习与正常剪接相关的序列模式以及由突变引起的拼接站点概率的变化被认为有助于预测剪接破坏。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号