DDK-Linker:一个基于网络的策略通过将高通量omics数据集与疾病知识联系起来来识别疾病信号
Xiangren Kong1, Lihong Diao1,2, Peng Jiang1
1State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
Briefings in bioinformatics
|March 22, 2024
概括
DDK-Linker是一种基于网络的新策略,通过将感兴趣的基因列表与已知的疾病基因联系起来,在omics数据中识别疾病信号. 这种工具加速了疾病基因和潜在药物点的发现.
科学领域:
- 基因组学和生物信息学
- 系统生物学 系统生物学
- 计算生物学 计算生物学
背景情况:
- 高通量奥米克研究产生了广泛的基因列表,使疾病机制的发现复杂化.
- 手动验证感兴趣的基因列表 (GOI) 是耗时且具有挑战性的.
- 从omics数据中识别生物学相关的疾病信号需要高效的分析工具.
研究的目的:
- 引入DDK-Linker,这是一个基于网络的策略,用于探索omics数据中的疾病信号.
- 为了促进兴趣的基因列表和已知的疾病基因之间的联系.
- 为候选疾病信号提供生物信息学注释.
主要方法:
- 在蛋白质-蛋白质相互作用 (PPI) 网络中使用六种网络算法 (随机步行与重启,Deepwalk,Node2Vec,LINE,HOPE,Laplacian) 重建基因距离.
- 通过接近已知的疾病基因来识别疾病信号.
- 整合一个知识库,以提供全面的生物信息学注释.
主要成果:
- 通过分析PPI网络中的基因距离,DDK-Linker有效地识别了潜在的疾病信号.
- 该战略为候选基因提供了有价值的生物信息学注释.
- 为了用户方便,开发了一个可访问的Web应用程序和R包.
结论:
- DDK-Linker加速了从基因组学,转录组学和蛋白质组学数据中探索疾病基因和药物标的速度.
- 该工具提供了对复杂疾病机制的洞察,并有助于药理学研究.
- DDK-Linker是免费提供,促进在生物医学研究更广泛的采用.
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