Novel bioinformatics methods for integrative detection of structural variants from long-read sequencing

用于从长读长测序中综合检测结构变异的新型生物信息学方法

• 批准号: 10752265
• 负责人: Jonathan Perdomo
• 金额: $ 4.77万
• 依托单位: DREXEL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2026-09-14
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10752265
• 关键词: Address; Area; Awareness; Base Pairing; Bioinformatics; Biomedical Engineering; Biomedical Research; Collection; Communication; Communities; Complex; Data; Data Science; Detection; Development; Disease; Education; Ethnic Population; Future; Genetic Variation; Genome; Genomic DNA; Genomics; Goals; Graph; Haplotypes; Human; Human Genome; Lead; Length; Maps; Methods; Modeling; Optics; Oral; Performance; Population; Repetitive Sequence; Research Training; Resolution; Resources; Sequence Alignment; Site; Source; Structure; Technology; Time; Variant; Work; Writing; base; candidate identification; career; computerized tools; contig; data integration; disease phenotype; doctoral student; file format; genome sequencing; genome-wide; genomic platform; genomic variation; human pangenome; human reference genome; insertion/deletion mutation; machine learning model; novel; pan-genome; reference genome; restriction enzyme; scaffold; sequencing platform; skills; statistics; technology development; tool; variant detection; whole genome

Project Summary/Abstract Structural variants (SVs) are the largest source of variations in the human genome and are frequently associated with disease phenotypes. Thus, the identification and characterization of SVs are essential for understanding human genome structure and function. The goal of this proposal is to develop a generalized SV calling pipeline that can leverage information from the latest developments in sequencing technology and human reference genome representations to discover and resolve SVs at high accuracy. I will first integrate information across sequencing platforms to increase SV calling accuracy. Multiple sequencing and mapping platforms are now used to detect SVs from human genome data. My pipeline will increase the accuracy of SV calling with a data integration model that handles a diverse set of genomic platforms. I will next develop a novel SV scoring model based on genomic context and coverage. Several factors, such as the generally low sequence coverage in typical long-read studies, as well as alignment errors due to highly repetitive sequences, can result in a potentially high rates of false positives for SVs when using parameters for high-sensitivity calling. I use two sets of important features of SVs, genomic context and coverage, into a machine-learning model to compute confidence in SV calls for downstream analysis. Finally, I will add support for graph genome alignments by implementing support for sequence data aligned to graph genome assemblies in GFA file format. Unlike single reference genomes, pangenomes are particularly useful for characterizing large-scale structural differences in genomes between different ethnicity groups. Pangenomes would bring us closer to capturing the full extent of human genomic variation, and thus represent an important resource to leverage for SV calling. In summary, in this project I will develop a generalized SV calling pipeline capable of integrating multiple technical platforms for discovering SVs and providing support for future developments in pangenome graph assemblies. With the research training plan, I will 1) gain expertise in genomics and bioinformatics, 2) promote diversity in biomedical research though involvement in educational efforts in the community, 3) develop oral and written communication skills, and 4) prepare a scientific career focused on the study and education of human genome variation.

项目摘要/摘要 结构变异体(SVS)是人类基因组中最大的变异来源，经常 与疾病表型相关。因此，SVS的识别和特征对于 了解人类基因组的结构和功能。该方案的目标是开发一种通用的SV 调用管道，该管道可以利用测序技术的最新发展和 人类参考基因组表示，以高精度发现和解析SVS。我会先整合 跨排序平台的信息，以提高SV呼叫的准确性。多重测序和作图 平台现在被用来从人类基因组数据中检测SVS。我的流水线将提高SV的精度 使用处理不同基因组平台的数据集成模型进行呼叫。接下来我要写一部小说 基于基因组背景和覆盖率的SV评分模型。几个因素，如普遍较低的 典型长读研究中的序列覆盖，以及由于高度重复的序列而引起的比对错误， 在使用高敏感度参数时，可能会导致SVS的假阳性率潜在较高 我在打电话。我将SVS的两组重要特征--基因组上下文和覆盖率--用于机器学习 用于计算用于下游分析的SV呼叫的置信度的模型。最后，我将添加对图形基因组的支持 通过实现对与GFA文件中的图形基因组组合进行比对的序列数据的支持进行比对 格式化。与单一参考基因组不同，泛基因组特别适用于描述大规模的 不同种族群体基因组的结构差异。潘格诺梅斯会让我们更接近 捕捉人类基因组变异的全部范围，因此是一种重要的资源，可用于 我是SV。综上所述，在本项目中，我将开发一个能够集成的通用SV调用流水线 用于发现SVS并为Pangenome的未来开发提供支持的多个技术平台 绘制部件图。有了研究培训计划，我将1)获得基因组学和生物信息学方面的专业知识，2) 通过参与社区的教育努力促进生物医学研究的多样性，3) 培养口头和书面沟通技能，并4)为专注于学习和学习的科学生涯做准备 人类基因组变异的教育。