评估基于下一代测序的计算方法,用查询基因组来预测转录调节器.
Zeyu Lu1, Xue Xiao2, Qiang Zheng3
1Department of Statistics and Data Science, Moody School of Graduate and Advanced Studies, Southern Methodist University, 3225 Daniel Ave., P.O. Box 750332, Dallas, TX, United States.
Briefings in bioinformatics
|July 31, 2024
概括
本综述评估了使用下一代测序 (NGS) 数据预测转录调节器 (TRs) 的计算方法. 巴特,Chip-Atlas和Lisa显示出有希望的性能,指导未来的TR识别策略.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 转录调节器 (TRs) 对于理解生物过程和疾病至关重要.
- 准确的TR识别对于药物发现和治疗目标预测至关重要.
- 许多使用下一代测序 (NGS) 数据的计算方法存在,但缺乏系统的评估.
研究的目的:
- 系统地审查和评估使用NGS数据进行TR预测的计算方法.
- 将现有的基于NGS的TR预测方法分为基于图书馆和基于地区的类别.
- 基于准确性,灵敏性,覆盖范围和可用性来对方法性能进行基准测试.
主要方法:
- 基于NGS的TR预测方法的分类.
- 使用分子实验数据集进行基准研究.
- 对准确性,灵敏性,覆盖范围和可用性的评估.
主要成果:
- 确定了基于图书馆和基于地区的TR预测方法类别.
- 在基准研究中,BART,ChIP-Atlas和Lisa在基准研究中表现相对优越.
- 目前基于NGS的TR预测方法的突出局限性.
结论:
- 对于TR预测任务,建议使用BART,Chip-Atlas和Lisa.
- 需要进一步的研究来克服局限性并改进基于NGS的TR识别.
- 这一审查为选择和开发先进的TR预测工具提供了基础.
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