Data Analysis Center for Somatic Mosaicism Across Human Tissues Network

项目总结 人体组织体细胞嵌合体(SMAHT)网络的目标是更好地描述这种情况的发生 并了解它们在调节生物过程中的作用 影响健康和疾病。SMAHT数据分析中心(DAC)的目标是收集和管理所有生成的数据 在网络中；评估、开发和应用最先进的分析管道；以及编制不同的目录和 一个Sciencefic社区的数据门户。我们已经组建了一支由调查人员、生物信息学科学家、 数据管理员和软件开发人员，在马赛克变体分析、长期阅读数据方面有良好的记录 分析、数据门户开发、可视化、大规模数据管理和计算以及开发 安全且fl可扩展的云技术。在目标1中，我们将与网络成员合作，以消除fine数据和 元数据标准，并确保统一生成、处理和批注高质量的数据 EFfi明显升高。在目标2中，我们将对Identifi阳离子的当前工具和技术进行基准研究 并领导在云平台上执行fine和实施分析管道的工作。我们会 还应根据需要开发新的方法。我们将确保一套全面的所有类型的马赛克变体 单核苷酸变异、Indels、拷贝数变异、易位、复杂重排、可转座 基因插入、微卫星突变、重复扩增等。)是identified，使用短读和长读 平台，全基因组和靶向分析，以及散装和单细胞技术。在目标3中，我们将建立一个 用户友好的交互式数据门户，包含变体目录，并以阅读级变体浏览器为特色 使SCRIENTIfic社区充分利用网络数据。我们将确保所有方法和过程 所有工具和数据都被记录下来，以便完全重现，社区可以免费获得。 1

PROJECT SUMMARY The goal of the Somatic Mosaicism across Human Tissues (SMaHT) Network is to better characterize the occurrence of mosaic variants in human tissues and to understand their role in the regulation of biological processes that impact health and disease. The SMaHT Data Analysis Center (DAC) aims to collect and curate all data generated in the Network; assess, develop, and apply state-of-the-art analytical pipelines; and produce a variant catalog and a data portal for the scientific community. We have assembled a team of investigators, bioinformatics scientists, data curators, and software developers with a strong track record in mosaic variant analysis, long-read data analysis, data portal development, visualization, large-scale data management and computing, and development of secure and flexible cloud technologies. In Aim 1, we will work with the Network members to define data and metadata standards and ensure that high-quality data are generated, processed, and annotated uniformly and efficiently. In Aim 2, we will perform benchmark studies for the current tools and technologies for identification of mosaic variants and lead an effort to define and implement analytical pipelines on a cloud platform. We will also develop new approaches as needed. We will ensure that a comprehensive set of mosaic variants of all types (single nucleotide variants, indels, copy number variants, translocations, complex rearrangements, transposable element insertions, microsatellite mutations, repeat expansions, etc.) are identified, using short- and long-read platforms, genome-wide and targeted assays, and bulk and single cell technologies. In Aim 3, we will build a user-friendly and interactive data portal containing a variant catalog and featuring a read-level variant browser to enable the scientific community to fully utilize the Network data. We will ensure that all methods and processes are documented for full reproducibility and that all tools and data are freely available to the community. 1