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; 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（SV）是人类基因组中最大的变异来源，并且经常是 与疾病表型相关。因此，SV的识别和表征对于 了解人类的基因组结构和功能。该提议的目的是开发广义的SV 致电管道，可以利用测序技术最新进展的信息和 人类参考基因组表示以高精度发现和解决SV。我将首先整合 跨排序平台的信息以提高SV通话准确性。多次测序和映射 现在，平台用于从人类基因组数据中检测SV。我的管道将提高SV的准确性 使用数据集成模型来呼叫，该模型可以处理各种基因组平台。接下来我将发展一本小说 基于基因组上下文和覆盖范围的SV评分模型。几个因素，例如通常很低 典型的长阅读研究中的序列覆盖范围以及由于高度重复序列而导致的对齐误差 在使用参数以实现高敏性时，可能导致SVS的假阳性率可能高高 打电话。我将SVS的两组重要特征，基因组上下文和覆盖范围用于机器学习 模型以计算对SV呼吁下游分析的信心。最后，我将增加对图基因组的支持 通过实现对与GFA文件中图形基因组组件的序列数据的支持来对齐 格式。与单个参考基因组不同，pangenomes对于表征大规模特别有用 不同种族群体之间基因组的结构差异。 Pangenomes会使我们更接近 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打电话。总而言之，在此项目中，我将开发一条能够集成的通用SV呼叫管道 多个技术平台，用于发现SVS并为Pangenome的未来发展提供支持 图组件。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） 尽管参与社区的教育工作，但在生物医学研究中促进多样性，3） 发展口头和书面沟通技巧，以及4）准备专注于研究的科学职业， 人类基因组变异的教育。