Comprehensive Somatic Variant Characterization at the HGSC

迄今为止，体细胞突变的研究主要集中在致病性变异，导致癌症或严重的 疾病跨人类组织的体细胞镶嵌（SMaHT）计划现在将扩展以下知识： 这类关键的基因组变异在正常组织中，并建立一个全面的了解， 体细胞变异的生物学。贝勒大学基因组表征中心 Medicine人类基因组测序中心（HGSC）将对750份组织样本中的变异进行表征- 1/3 从150个人中抽取15个样本。我们的研究中采用了新的步骤 设计，使全面发现体细胞突变。两种常见的核心检测类型（WGS 短读段、长读段和批量RNAseq）和另外两种测定（nanoSeq和snRNAseq），我们的小组 专业化将用于数据生成。基准标准、统一数据结构和标准操作程序 将与其他网络成员合作，使用现有的最先进的方法， 技术验证的发现和正交方法。满足性能要求的新技术 标准将被引入生产。统计模型将指导组织亚取样和测序 因此，我们需要制定一些战略，将目前的阈值设定为有进一步改进的余地。NanoSeq和单核RNA 将对每项程序进行修改，以提高绩效并与调查人员密切合作 开发更多的工具将确保最佳的发现。本地分析将生成假定的 变体，特别关注长读段序列数据的表征。后者将使 转座事件的建模，揭示了结构变异的证据，以及 表观遗传标记的模式所有数据都将与DAC合作进行严格的QA/QC， 镜像在集中式数据存储库中。

Studies of somatic mutations have so far focused on pathogenic variation, leading to cancer or severe disease. The Somatic Mosaicism Across Human Tissues (SMaHT) program will now expand knowledge of this critical class of genomic variation in normal tissues and build a comprehensive understanding of the biology of somatic variation in all contexts. The Genome Characterization Center at the Baylor College of Medicine Human Genome Sequencing Center (HGSC) will characterize variation in 750 tissue samples - 1/3 of the Program’s 15 samples from each of 150 individuals. Novel steps have been incorporated into our study design to enable comprehensive discovery of somatic mutations. Both common core assay types (WGS short-read, long read and bulk RNAseq) and two additional assays (nanoSeq and snRNAseq) that our group specializes will be used for data generation. Benchmark standards, harmonized data structures and SOPs will be created in collaboration with other Network members using established state-of-the-art methods for discovery and orthogonal approaches for technical validation. New technologies that satisfy performance criteria will be introduced into production. Statistical models will guide tissue-subsampling and sequencing strategies, set the current thresholds with further room for improvement. NanoSeq and single nuclear RNA procedures will each be modified for enhanced performance and close collaboration with investigators developing additional tools will ensure optimal discovery. Local analyses will generate lists of putative variants, with a particular focus upon characterization of long read sequence data. The latter will enable modelling of transposition events, revealed within evidence for structural variation, as well as changes in patterns of epigenetic marks. All data will be subjected to rigorous QA/QC, in collaboration with the DAC, and mirrored in the centralized data repository.