在植物LTR-逆转移子中检测和分类长端重复序列,并使用可解释的机器学习进行分析
Jakub Horvath1, Pavel Jedlicka2, Marie Kratka2,3
1Faculty of Informatics, Masaryk University, Botanicka 68a, Brno, 60200, Czech Republic. jakubhorvath119@gmail.com.
BioData mining
|December 19, 2024
概括
机器学习模型有效地识别了植物基因组中的长终端重复 (LTR),揭示了对逆转移子功能至关重要的关键序列动机. 这种方法增强了LTR分类和预测,为基因组调节提供了洞察力.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 长终端重复 (LTR) 是LTR逆转录和逆转录病毒的重要组成部分,在真核生物基因组中大量存在.
- LTR含有对逆转移子生命周期至关重要的调节序列,但它们的结构和组成仍然不完全理解.
- 以前的研究提供了有限的见解,主要来自模型系统,需要先进的分析方法.
研究的目的:
- 通过分析不同逆转移子家族和基因组区域之间的对比,增强对LTR结构和组成的理解.
- 开发和应用机器学习方法来准确分类和预测LTR序列.
- 通过可解释性分析,在LTR中识别生物学相关的序列动机.
主要方法:
- 应用机器学习技术,包括渐变增强,混合卷积/长短内存网络和基于变压器的模型,对大量植物LTR逆转换子序列的数据集.
- 用于DNA预训练的变压器模型的k-mer序列表示.
- 在经过训练的模型上进行可解释性分析,以识别序列特征及其位置相关性.
主要成果:
- 这三种机器学习方法都成功地对LTR进行了分类和隔离,并提供了对其序列组成的见解.
- 混合网络模型实现了最高的LTR检测F1得分,为0.85.
- 可解释性分析确定了生物相关的动机,包括一个中央的TATA-box和LTR边缘的TG..CA模式,并突出了LTR终点的意义.
结论:
- 机器学习模型准确地识别了生物相关的动机,例如核心促进元和转录因子结合点.
- 可解释性分析强调了LTR5'和3'边缘对认同的重要性,表明需要超越二核酸的分析.
- 该研究证明了机器学习在调节序列分析和分类中的实用性,证实了已识别的动机在LTR检测中的作用.
关键词:
美国有线电视新闻 (CNN-LSTM)DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABER深度学习是一种深度学习.简介:真核生物 (Eukaryote) 是一种真核生物.监管机制 监管机制重复重复 重复 重复 重复在SHAP中,分数是SHAP分数.序列分析是指进行序列分析.根据TFBS的规定,TFBS可以转录因子的结合位点.可转移的要素是可以转移的.
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