标题： 一种用于在肿瘤测序数据上重现突变特征检测的公平平台 显示英文标题

标题： A FAIR platform for reproducing mutational signature detection on tumor sequencing data

摘要： 本文介绍了一个便携的、隐私保护的、浏览器内平台，用于从靶向基因面板生成的稀疏测序数据中可重复评估突变特征检测方法。 该平台旨在通过遵循FAIR原则来解决突变特征研究中的可重复性挑战，使其具有可查找性、可访问性、互操作性和可重用性。 我们的方法专注于检测特定的突变特征，例如与特定诱变过程相关的SBS3。 该平台依赖于公开可用的数据、模拟、下采样技术以及机器学习算法来生成训练数据和标签，并训练和评估模型。 我们平台的主要成就是其透明性、可重用性和隐私保护，使研究人员和临床医生能够分析突变特征，并确保数据不会在客户端计算机之外传播。 显示英文摘要

摘要： This paper presents a portable, privacy-preserving, in-browser platform for the reproducible assessment of mutational signature detection methods from sparse sequencing data generated by targeted gene panels. The platform aims to address the reproducibility challenges in mutational signature research by adhering to the FAIR principles, making it findable, accessible, interoperable, and reusable. Our approach focuses on the detection of specific mutational signatures, such as SBS3, which have been linked to specific mutagenic processes. The platform relies on publicly available data, simulation, downsampling techniques, and machine learning algorithms to generate training data and labels and to train and evaluate models. The key achievement of our platform is its transparency, reusability, and privacy preservation, enabling researchers and clinicians to analyze mutational signatures with the guarantee that no data circulates outside the client machine.