通过signR 2.0将突变特征暴露与癌症临床数据联系起来
Rodrigo D Drummond1, Alexandre Defelicibus1, Mathilde Meyenberg2
1Laboratory of Computational Biology and Bioinformatics, CIPE/A.C.Camargo Cancer Center, São Paulo, São Paulo, 01508-010, Brazil.
BMC bioinformatics
|November 22, 2023
概括
该软件提供了一个用户友好的贝叶斯方法来分析癌症突变特征及其临床相关性. 这个开源的R包通过提高性能和数据集成能力来增强以前的分析.
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:
- 癌症源于由体位突变驱动的不受调节的细胞过程.
- 突变特征,体突变中的模式,在瘤学研究中至关重要.
- 现有的算法处理签名/暴露估计或预定义的签名暴露分析.
研究的目的:
- 介绍signeR 2.0,一个增强的贝叶斯软件用于突变签名分析.
- 扩大能力,探索标志性暴露的临床相关性.
- 为基因组数据分析提供一个用户友好的界面和更好的性能.
主要方法:
- 使用贝叶斯的方法进行 de novo 签名估计和暴露分析.
- 整合了R-Shiny框架,提供了一个用户友好的界面.
- 支持对用户提交的数据和嵌入式TCGA数据的分析.
主要成果:
- signeR 2.0 简化了与临床数据相关的签名暴露的探索.
- 该软件提供了更好的性能和直观的用户界面.
- 能够对突变特征进行 de novo 和 fitting 分析.
结论:
- signeR 2.0是一个有价值的开源R包,用于生成和探索突变签名暴露数据.
- 该工具支持de novo和fitting分析.
- 通过癌症研究应用的生物导体项目获得.
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