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Bioinformatics solution for clinical utilization of next generation DNA sequencing.

机译:用于下一代DNA测序临床应用的生物信息学解决方案。

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摘要

DNA sequencing as an application of Next Generation Sequencing (NGS) is beginning to reshape how physicians diagnose and make treatment decisions for their patients. These NGS technologies provide a great depth of information by bringing along unprecedented throughput of data, huge scalability and speed. The terabytes of data generated has precipitated a need for efficient bioinformatics analysis and interpretation processes. My dissertation provides an end-to-end solution to analyze DNA sequencing data, interpret and deliver results efficiently and effectively. I developed a modular, robust workflow Targeted RE-sequencing Annotation Tool (TREAT) to provide a backbone for NGS DNA analysis, in collaboration with Mayo Clinic's bioinformatics core [1]. TREAT is one of the first bioinformatics solutions to incorporate alignment, variant calling, annotation and visualization of DNA sequencing data. To better evaluate the increasing foray of NGS into the clinical domain, I designed a module for comprehensive depth of coverage evaluation for genes and variants of interest. This module extending upon the TREAT pipeline helps quantify the applicability of NGS for clinical gene panels [2]. With dwindling costs and increasing availability of whole genome sequencing, turnaround time remains a major factor for clinical adaptation of NGS. I developed a novel iterative bioinformatics approach to expedite whole genome analysis by focusing on clinically relevant genomic regions, reporting results in less than 10% of the original processing time [3]. Further research employing additional clinical annotation has given us insight into a comprehensive genotype phenotype correlation evaluation of clinically reportable variants. Here I report on the characteristics of clinically relevant variants typically expected per individual from whole exome DNA sequencing data. These data highlight challenges that need to be addressed including both phenotype issues of disease penetrance and uncertainty about what is clinically reportable, and sequencing issues like incomplete sequencing coverage, thresholds for data filtering and lack of high quality databases to determine functional annotation.
机译:DNA测序作为下一代测序(NGS)的应用正在开始重塑医生为患者诊断和做出治疗决策的方式。这些NGS技术通过带来前所未有的数据吞吐率,巨大的可扩展性和速度,提供了深度的信息。生成的TB级数据催生了对有效生物信息学分析和解释过程的需求。本文提供了一种端到端的解决方案,用于分析DNA测序数据,有效地解释和提供结果。我与Mayo Clinic的生物信息学核心合作[1],开发了一种模块化的,功能强大的工作流程,靶向RE测序注释工具(TREAT),为NGS DNA分析提供了基础。 TREAT是首批结合比对,变异调用,注释和DNA测序数据可视化的生物信息学解决方案之一。为了更好地评估NGS在临床领域中的应用,我设计了一个模块,用于全面评估目标基因和变体的覆盖范围。该模块延伸到TREAT管线上,有助于量化NGS在临床基因组中的适用性[2]。随着成本的降低和全基因组测序可用性的提高,周转时间仍然是NGS临床适应的主要因素。我开发了一种新颖的迭代生物信息学方法,通过专注于临床相关的基因组区域来加快全基因组分析,报告的结果不到原始处理时间的10%[3]。采用其他临床注释的进一步研究使我们对临床可报告变异的全面基因型表型相关性评估有了深入的了解。在这里,我报告了根据整体外显子组DNA测序数据通常每个人都期望的临床相关变异体的特征。这些数据突出显示了需要解决的挑战,包括疾病渗透性的表型问题和临床上可报告的不确定性,以及测序问题,例如测序覆盖范围不完整,数据过滤的阈值以及缺乏确定功能注释的高质量数据库。

著录项

  • 作者

    Middha, Sumit.;

  • 作者单位

    University of Minnesota.;

  • 授予单位 University of Minnesota.;
  • 学科 Bioinformatics.;Genetics.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 144 p.
  • 总页数 144
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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