Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA.
Bioinformatics. 2013 Feb 15;29(4):509-10. doi: 10.1093/bioinformatics/btt003. Epub 2013 Jan 9.
Determining the functional relevance of identified sequence variants in cancer is a prerequisite to ultimately matching specific therapies with individual patients. This level of mechanistic understanding requires integration of genomic information with complementary functional analyses to identify oncogenic targets and relies on the development of computational frameworks to aid in the prioritization and visualization of these diverse data types. In response to this, we have developed HitWalker, which prioritizes patient variants relative to their weighted proximity to functional assay results in a protein-protein interaction network. It is highly extensible, allowing incorporation of diverse data types to refine prioritization. In addition to a ranked list of variants, we have also devised a simple shortest path-based approach of visualizing the results in an intuitive manner to provide biological interpretation.
The program, documentation and example data are available as an R package from www.biodevlab.org/HitWalker.html.
确定癌症中已识别序列变异的功能相关性是最终将特定疗法与个体患者相匹配的前提。这种机制理解水平需要将基因组信息与互补的功能分析相结合,以确定致癌靶点,并依赖于计算框架的发展,以帮助对这些不同类型的数据进行优先级排序和可视化。针对这一需求,我们开发了 HitWalker,它根据患者变异与蛋白质-蛋白质相互作用网络中功能测定结果的加权接近程度对其进行优先级排序。它具有高度的可扩展性,允许纳入各种数据类型来优化优先级排序。除了排名列表中的变体外,我们还设计了一种简单的基于最短路径的方法,以直观的方式可视化结果,提供生物学解释。
该程序、文档和示例数据可作为 R 包从 www.biodevlab.org/HitWalker.html 获取。