Masica David L, Douville Christopher, Tokheim Collin, Bhattacharya Rohit, Kim RyangGuk, Moad Kyle, Ryan Michael C, Karchin Rachel
Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland.
The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland.
Cancer Res. 2017 Nov 1;77(21):e35-e38. doi: 10.1158/0008-5472.CAN-17-0338.
Cancer sequencing studies are increasingly comprehensive and well powered, returning long lists of somatic mutations that can be difficult to sort and interpret. Diligent analysis and quality control can require multiple computational tools of distinct utility and producing disparate output, creating additional challenges for the investigator. The Cancer-Related Analysis of Variants Toolkit (CRAVAT) is an evolving suite of informatics tools for mutation interpretation that includes mutation mapping and quality control, impact prediction and extensive annotation, gene- and mutation-level interpretation, including joint prioritization of all nonsilent mutation consequence types, and structural and mechanistic visualization. Results from CRAVAT submissions are explored in an interactive, user-friendly web environment with dynamic filtering and sorting designed to highlight the most informative mutations, even in the context of very large studies. CRAVAT can be run on a public web portal, in the cloud, or downloaded for local use, and is easily integrated with other methods for cancer omics analysis. .
癌症测序研究越来越全面且有充足的样本量，会得出一长串难以分类和解读的体细胞突变。细致的分析和质量控制可能需要多种具有不同用途且会产生不同输出结果的计算工具，这给研究人员带来了额外的挑战。癌症相关变异分析工具包（CRAVAT）是一套不断发展的信息学工具，用于突变解读，包括突变定位和质量控制、影响预测和广泛注释、基因和突变水平的解读（包括对所有非同义突变后果类型进行联合优先级排序）以及结构和机制可视化。通过一个交互式、用户友好的网络环境来探索CRAVAT提交的结果，该环境具有动态过滤和排序功能，旨在突出最具信息价值的突变，即使是在非常大型的研究中也是如此。CRAVAT可以在公共网络门户上运行，也可以在云端运行，或者下载供本地使用，并且很容易与其他癌症组学分析方法集成。
