闪亮的应用程序:闪亮的瘤高通量基于omics的路线图
Eduardo Navarrete-Bencomo1,2, Anthony Vladimir Campos Segura3,4,5, Orlando R Sevillano6
1Unidad Profesional Interdisciplinaria de Biotecnología del Instituto Politécnico Nacional, Av. Luis Enrique Erro S/N, Zacatenco, Alcaldía Gustavo A. Madero, Ciudad de México, CP 07738, México.
Bioinformatics advances
|May 19, 2025
概括
ShinyTHOR提供了一个用户友好的网络应用程序,用于探索来自癌症细胞系的多组和药物反应数据. 这种工具提高了研究人员的可访问性,促进了对癌症生物学和药物敏感性的更深入的了解.
科学领域:
- 癌症研究 癌症研究
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 癌症细胞系百科全书 (CCLE) 为超过1000个癌症细胞系提供了广泛的多组和药物反应数据.
- 像DepMap这样的当前工具缺乏针对不同分子数据类型和药物反应的综合分析能力.
- 访问和比较这些复杂的数据往往需要专门的生物信息学专业知识,限制了许多研究人员的使用.
研究的目的:
- 开发ShinyTHOR,一个直观的Web应用程序,用于访问和分析来自癌症细胞系的多组和药物相关数据.
- 将CCLE,miRTarBase,circInteractome和癌症药物敏感性基因组学 (GDSC) 的数据集成到一个平台上.
- 为研究人员提供基因表达,药物敏感性和分子相互作用的比较分析工具.
主要方法:
- 使用Shiny框架开发一个Web应用程序.
- 整合各种数据集,包括转录组,代谢组,甲基组,蛋白组和miRNomic数据.
- 实施六个不同的分析模块来探索细胞系数据,基因组,分子相互作用和药物反应.
主要成果:
- 闪亮THOR提供了直观的访问多组和药物敏感性数据.
- 该应用程序可以对分子标记物,药物IC50值和基因组丰富进行比较分析.
- 模块方便探索miRNA-目标相互作用,miRNA对翻译的影响以及circRNA/miRNA调制.
- 使用胃癌预后研究 (GES7) 的验证证明了该工具的实用性.
结论:
- ShinyTHOR使更广泛的研究社区能够获得复杂的癌症细胞系数据.
- 该网络应用程序促进了癌症生物学和精准医学中的假设生成和发现.
- 对于研究癌症细胞系依赖性和药物反应的研究人员来说,ShinyTHOR 是一个宝贵的资源.
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