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.

项目总结/摘要 结构变异（SV）是人类基因组中变异的最大来源，并且经常被用于人类基因组的遗传学研究。 与疾病表型相关。因此，SV的识别和表征对于以下方面至关重要： 了解人类基因组的结构和功能。本建议的目标是开发一个通用的SV 调用管道，可以利用来自测序技术最新发展的信息， 人类参考基因组表示，以高精度发现和解决SV。我先整合一下 在整个测序平台上的信息，以增加SV调用准确性。多重测序和作图 平台现在用于从人类基因组数据中检测SV。我的管道将增加SV的准确性 调用处理不同基因组平台的数据集成模型。我接下来要写一本小说 基于基因组背景和覆盖度的SV评分模型。几个因素，如普遍低 在典型的长读段研究中的序列覆盖率，以及由于高度重复序列引起的比对误差， 当使用高灵敏度参数时，可能导致SV的假阳性率很高 调用.我使用SV的两组重要特征，基因组背景和覆盖率，进入机器学习 计算SV置信度的模型要求进行下游分析。最后，我将添加对graph genome的支持 通过实现对序列数据的支持来进行比对，以在GFA文件中绘制基因组组装图 格式.与单一参考基因组不同，泛基因组对于表征大规模的基因组特别有用。 不同种族群体之间基因组的结构差异。泛星系群会让我们更接近 捕获人类基因组变异的全部范围，因此代表了利用的重要资源， SV呼叫总之，在这个项目中，我将开发一个通用的SV调用管道， 多个技术平台，用于发现SV并为泛基因组的未来发展提供支持 图形程序集。通过研究培训计划，我将1）获得基因组学和生物信息学方面的专业知识，2） 通过参与社区教育工作促进生物医学研究的多样性，3） 发展口头和书面沟通能力，4）准备一个科学的职业生涯集中在研究和 人类基因组变异的教育。