A reference-free computational algorithm for comprehensive somatic mosaic mutation detection

一种用于综合体细胞嵌合突变检测的无参考计算算法

• 批准号: 10662755
• 负责人: Gabor T Marth
• 金额: $ 38.46万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10662755
• 关键词: Adoption; Algorithms; Atlases; Automobile Driving; Behavior; Benign; Biological; Calibration; Cataloging; Cells; Clinical; Computational algorithm; Computer software; DNA; DNA Sequence; Data; Data Analyses; Data Discovery; Data Set; Detection; Development; Event; Experimental Designs; Frequencies; Funding; Future; Gene Frequency; Genetic Polymorphism; Genome; Goals; Human; Individual; Inherited; Knowledge; Length; Link; Malignant Neoplasms; Maps; Measures; Mosaicism; Mutation; Mutation Analysis; Mutation Detection; Noise; Organ; Pathogenicity; Pattern; Performance; Phase; Rare Diseases; Repetitive Sequence; Resources; Sampling; Sensitivity and Specificity; Somatic Mutation; Specificity; Tissues; Training; United States National Institutes of Health; Validation; Variant; algorithm development; analysis pipeline; base; de novo mutation; design; detection sensitivity; developmental disease; disease diagnostic; human tissue; improved; mosaic variant; novel; predictive modeling; tool; tool development; tumor; variant detection

ABSTRACT Somatic mosaicism (SM), i.e. the presence of cells with somatically acquired mutations, is a driving feature of cancer and several developmental diseases. However, whereas today we have detailed understanding and predictive models of benign and pathogenic inherited polymorphisms, germline de novo mutations, and tumor mutations, we have only limited knowledge of the burden, allele frequency spectrum, clonal patterns, and mutational signatures of healthy somatic mosaicism. Realizing that such currently missing knowledge is critical for informing experimental design in future studies of mosaicism’s biological and clinical consequences, NIH is launching an ambitious initiative, the Somatic Mosaicism across Human Tissues (SMaHT) project to construct a comprehensive human somatic mosaicism atlas. As part of this initiative, funding announcement RFA-RM-22- 011 calls for Tool Development Projects to develop “approaches that significantly improve the sensitivity, accuracy, and threshold of detection of all types of somatic variants across the complete genome”. Such comprehensive detection is currently challenging because somatic mosaicism mutations occur across a wide range of mutation types and lengths, but the majority of today’s variant detection tools have low sensitivity for larger, structural events. Furthermore, somatic mutations are typically at very low allele frequency (<1%), but accurate detection of low-frequency variation today is beyond the capabilities of most tools. We have pioneered a unique-kmer guided detection approach in our RUFUS tool, designed for germline de novo mutation detection. This approach focuses on identifying the novel DNA sequence created by a mutation, which allows the same underlying algorithm, with uniform algorithmic behavior and sensitivity, to be applied across the full range of mutation types. RUFUS has been validated for accurately detecting germline de novo mutations in large discovery datasets and rare-disease diagnostic studies. Our preliminary analyses also indicate that RUFUS has high sensitivity across a full range of somatic mutations. This application proposes to adapt the RUFUS algorithm for somatic mosaic mutation detection with high sensitivity and specificity across the entire mutation type, mutation length, and allele frequency spectrum; and thus, substantially contribute to the construction of a comprehensive mosaicism atlas. To achieve this overall goal, in the first (UG3) phase of the project we will focus on algorithmic development to improve low-frequency allele detection, empirically characterize RUFUS’s sensitivity and specificity, and ready the tool for adoption into the SMaHT Network’s central analysis pipelines. In the second (UH3) phase of the project, we will integrate RUFUS into the central analysis workflow of the SMaHT consortium; optimize and extend its performance for analyzing the vast SMaHT somatic mosaicism dataset. We anticipate that RUFUS will contribute substantially to the SMaHT Initiative's goal to comprehensively map out human somatic mosaicism across individuals, organs, and tissues.

摘要 体细胞嵌合现象（SM），即存在具有体细胞获得性突变的细胞，是一个驱动特征， 癌症和几种发育性疾病。然而，尽管我们今天有了详细的了解， 良性和致病性遗传多态性、生殖系新生突变和肿瘤的预测模型 突变，我们只有有限的知识的负担，等位基因频谱，克隆模式， 健康体细胞嵌合体的突变特征认识到这些目前缺失的知识是至关重要的 为了在未来研究镶嵌现象的生物学和临床后果时为实验设计提供信息，NIH正在 启动了一项雄心勃勃的计划，即跨人类组织的体细胞镶嵌（SMaHT）项目，以构建一个 人体嵌合体综合图谱。作为该倡议的一部分，资金公告RFA-RM-22- 011呼吁工具开发项目开发“显著提高灵敏度的方法， 准确性和整个基因组中所有类型体细胞变异的检测阈值”。等 全面的检测目前是具有挑战性的，因为体细胞嵌合突变发生在广泛的 突变类型和长度的范围，但大多数今天的变异检测工具具有低灵敏度， 更大的结构性事件此外，体细胞突变通常处于非常低的等位基因频率（<1%），但 如今，低频变化的精确检测超出了大多数工具的能力。我们开创 RUFUS工具中的独特kmer引导检测方法，专为生殖系从头突变检测而设计。 这种方法的重点是识别由突变产生的新DNA序列，这使得相同的 基础算法，具有统一的算法行为和灵敏度，适用于整个范围 突变类型RUFUS已被验证用于准确检测大样本中的生殖系新生突变。 发现数据集和罕见疾病诊断研究。我们的初步分析还表明，RUFUS 对各种体细胞突变的高灵敏度。本申请建议调整RUFUS 用于体细胞嵌合突变检测的算法，其在整个系统中具有高灵敏度和特异性 突变类型、突变长度和等位基因频率谱;并且因此，基本上有助于基因突变。 综合马赛克地图集的构建。为了实现这一总体目标，在第一阶段（UG 3）， 该项目我们将专注于算法开发，以改善低频等位基因检测，经验 描述RUFUS的敏感性和特异性，并准备将该工具纳入SMaHT网络的 中央分析管道。在项目的第二阶段（UH 3），我们将把RUFUS整合到中央 SMaHT联盟的分析工作流程;优化和扩展其性能，以分析庞大的SMaHT 体细胞镶嵌数据集。我们预计RUFUS将为SMaHT倡议的目标做出重大贡献 全面绘制出人体在个体、器官和组织上的嵌合体。