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 体细胞嵌合体（SM），即具有体细胞获得性突变的细胞的存在，是 然而，今天我们已经有了详细的了解和了解。 良性和致病性遗传多态性、种系从头突变和肿瘤的预测模型 突变，我们对负担、等位基因频谱、克隆模式和 认识到目前缺失的知识至关重要。 为了为嵌合现象的生物学和临床后果的未来研究中的实验设计提供信息，NIH 发起一项雄心勃勃的倡议，跨人体组织的体细胞镶嵌（SMaHT）项目，以构建一个 全面的人类体细胞镶嵌图谱作为该计划的一部分，资助公告 RFA-RM-22- 011 呼吁工具开发项目开发“显着提高灵敏度、 整个基因组中所有类型体细胞变异的准确性和检测阈值”。 全面的检测目前具有挑战性，因为体细胞嵌合突变发生在广泛的领域 突变类型和长度的范围，但当今大多数变异检测工具的灵敏度较低 此外，体细胞突变的等位基因频率通常非常低（<1%），但 如今，准确检测低频变化超出了我们开创的大多数工具的能力。 我们的 RUFUS 工具中采用独特的 kmer 引导检测方法，专为种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，这允许相同的 底层算法，具有统一的算法行为和灵敏度，可应用于整个范围 RUFUS 已被验证可准确检测大型种系新生突变。 我们的初步分析还表明，RUFUS 已经发现了数据集和罕见疾病诊断研究。 该应用建议对 RUFUS 进行调整。 体细胞嵌合突变检测算法，在整个过程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谱；因此，对 为了实现这一总体目标，在第一阶段（UG3）构建了一个全面的镶嵌图谱。 该项目我们将重点关注算法开发，以凭经验改进低频等位基因检测 描述 RUFUS 的敏感性和特异性，并准备好将该工具采用到 SMaHT 网络中 在项目的第二阶段（UH3），我们将把 RUFUS 集成到中央分析管道中。 SMaHT 联盟的分析工作流程；优化和扩展其分析庞大 SMaHT 的性能 我们预计 RUFUS 将为 SMaHT Initiative 的目标做出重大贡献。 全面绘制跨个体、器官和组织的人类体细胞镶嵌现象。