通过融合物理化学性质的序列衍生特征和核酸分布模式来识别长额外染色体圆形DNA
Ahtisham Fazeel Abbasi1,2, Muhammad Nabeel Asim3, Sheraz Ahmed4
1Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, 67663, Kaiserslautern, Germany. ahtisham.abbasi@dfki.de.
Scientific reports
|April 24, 2024
概括
第一个计算预测器,iLEC-DNA,准确地识别了跨物种的长额外染色体圆形DNA (leccDNA). 这个工具有助于理解leccDNA.
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:
- 长额外染色体圆形DNA (leccDNA) 在基因组不稳定性,基因放大和瘤发生中发挥作用.
- 识别leccDNA对于了解其与癌症和心血管疾病等疾病的联系至关重要.
- 目前用于leccDNA识别的湿实验室方法资源密集,需要先前的知识.
研究的目的:
- 开发第一个用于识别多个物种的leccDNA的计算预测器.
- 为leccDNA检测提供更容易获得和更有效的工具.
- 探索leccDNA研究中的计算方法的潜力.
主要方法:
- 使用支持矢量机 (SVM) 分类器开发iLEC-DNA预测器.
- 基于核酸分布模式和DNA序列的物理化学性质的特征提取.
- 使用12个基准leccDNA数据集进行大规模实验验证,这些数据集来自Homo sapiens,Arabidopsis Thaliana和Saccharomyces cerevisiae.
主要成果:
- 与140多个基线预测器和858个编码器组合相比,iLEC-DNA预测器表现出卓越的性能.
- 在所有测试数据集中实现了81.09%的ACC,62.2%的MCC和81.08%的AUC-ROC的平均性能指标.
- 该预测器在不同的leccDNA数据集中显示出强大的性能.
结论:
- 该iLEC-DNA预测器代表了计算leccDNA识别的重大进步.
- 开发的工具及其相关的网络应用程序可以促进对leccDNA及其疾病关联的更广泛研究.
- 这种计算方法为传统的湿实验室方法提供了有价值的替代方案.
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