Ge Aaron, Zhang Tongwu, Martins Yasmmin Côrtes, Landi Maria Teresa, Park Brian, Chen Kailing, Balasubramanian Jeya, Almeida Jonas S
-Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA.
-University of Maryland, School of Medicine, Maryland, USA.
Res Sq. 2025 Sep 2:rs.3.rs-6536730. doi: 10.21203/rs.3.rs-6536730/v1.
In our previous work, we demonstrated that it is feasible to perform analysis on mutation signature data without the need for downloads or installations and analyze individual patient data without compromising privacy. Building on this foundation, we developed an in-browser Software Development Kit (a JavaScript SDK), mSigSDK, to facilitate the orchestration of distributed data processing workflows and graphic visualization of mutational signature analysis results. We strictly adhered to modern web computing standards, particularly the modularization standards set by the ECMAScript ES6 framework (JavaScript modules). Our approach allows for the computation to be entirely performed by secure delegation to the computational resources of the user's own machine (in-browser), without any downloads or installations. The mSigSDK was developed primarily as a companion library to the mSig Portal resource of the National Cancer Institute Division of Cancer Epidemiology and Genetics (NIH/NCI/DCEG), with a focus on FAIR extensibility as components of other researchers' own data science constructs. Anticipated extensions include the programmatic operation of other mutation signature API ecosystems such as SIGNAL and COSMIC, advancing towards a data commons for mutational signature research (Grossman et al., 2016).
在我们之前的工作中，我们证明了对突变特征数据进行分析而无需下载或安装，并且在不损害隐私的情况下分析个体患者数据是可行的。在此基础上，我们开发了一个浏览器内软件开发工具包（JavaScript软件开发工具包，即mSigSDK），以促进分布式数据处理工作流程的编排以及突变特征分析结果的图形可视化。我们严格遵循现代网络计算标准，特别是由ECMAScript ES6框架（JavaScript模块）设定的模块化标准。我们的方法允许通过安全委托给用户自己机器（浏览器内）的计算资源来完全执行计算，而无需任何下载或安装。mSigSDK主要作为美国国立卫生研究院/国立癌症研究所癌症流行病学与遗传学司（NIH/NCI/DCEG）的mSig门户资源的配套库开发，重点是作为其他研究人员自己的数据科学构建组件的公平可扩展性。预期的扩展包括对其他突变特征API生态系统（如SIGNAL和COSMIC）的编程操作，朝着突变特征研究的数据共享库发展（格罗斯曼等人，2016年）。
