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.

抽象的 体细胞镶嵌（SM），即具有体形获得突变的细胞的存在，是 癌症和几种发育疾病。但是，尽管今天我们已经详细了解了 良性和致病性遗传性多态性，生殖新生突变和肿瘤的预测模型 突变，我们对烧伤，等位基因频谱，克隆模式和 健康体细胞镶嵌的突变特征。意识到当前缺少的知识至关重要 为了在镶嵌物的生物学和临床后果的未来研究中为实验设计提供信息，NIH是 发起了雄心勃勃的计划，跨人类组织的躯体镶嵌主义（SMAHT）项目建造了一个 全面的人类体细节镶嵌图集。作为该倡议的一部分，资助公告RFA-RM-22-- 011要求开发工具开发项目以开发“可以显着提高灵敏度的方法， 整个基因组中所有类型的体细胞变体的检测的准确性和阈值”。 全面检测目前具有挑战性，因为体细胞镶嵌突变发生在广泛的 突变类型和长度的范围，但是当今的大多数变体检测工具对 更大的结构事件。此外，体细胞突变通常处于非常低的等位基因频率（<1％），但 当今低频变化的准确检测超出了大多数工具的功能。我们已经开创了开创性 我们的Rufus工具中的独特引导检测方法，该方法是为新生突变检测设计的。 这种方法着重于识别突变产生的新型DNA序列，该序列允许相同 具有均匀算法和灵敏度的基础算法，可在整个范围内应用 突变类型。 Rufus已经过验证，以准确检测大型种系突变 发现数据集和稀有诊断研究。我们的初步分析也表明Rufus有 在整个体细胞突变中高灵敏度。此申请提案以适应rufus 整个算法用于体细胞镶嵌突变检测，并具有高灵敏度和特异性 突变类型，突变长度和等位基因频谱；因此，很大程度上有助于 建造全面的镶嵌图集。为了实现这一总体目标，在第一个（ug3）阶段 该项目我们将重点介绍算法开发以改善低频等位基因检测，从经验上 表征鲁弗斯的敏感性和特异性，并准备好采用SMAHT网络的工具 中央分析管道。在项目的第二（UH3）阶段，我们将将Rufus整合到中央 分析SMAHT联盟的工作流程；优化并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 全面绘制在个体，器官和组织之间的人类体细胞镶嵌。