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Developing computational methods for studying nonmodel organism genetics and human disease with next-generation sequencing data.

机译：利用下一代测序数据开发用于研究非模型生物遗传学和人类疾病的计算方法。

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摘要

The rapidly decreasing of costs of sequencing is revolutionizing genetics. Two applications of next-generation sequencing data are of particular importance in this regard. First, high-throughput sequencing now offers a fast and inexpensive means to investigate the genomes and genetics of nonmodel organisms. Second, human personal-genomics data offer a unique opportunity for discovering the genetic basis of human traits and diseases.;My PhD research has focused on developing computational methods to study genetics using next-generation sequencing data. In the first chapter of my thesis, I present a series of genome-based studies of the venomous cone snail Conus bullatus, a source of pharmaceutically important small cysteine-rich peptides called conopeptides or conotoxins. Using high-coverage transcriptome sequence from its venom duct together with low-coverage genomic reads, I have developed new methods to characterize key genomic traits in the absence of a complete reference genome, including genome size, sequence diversity, repeat content and mobile element densities. I have also developed an in silico transcriptomics pipeline for conotoxin discovery, and have used it to identify novel conotoxins as well as candidate enzymes that are likely to be involved in the post-translational processing of conotoxins.;In the second and the third chapters of my thesis, I describe a probabilistic disease-gene search algorithm VAAST (the Variant Annotation, Analysis and Search Tool) for finding damaged genes and their disease-causing variants; I also describe a powerful new extension to the original code-base called VAAST 2.0. In these chapters, I demonstrate that VAAST is both an accurate rare Mendelian disease-gene finder and a powerful means for identifying genes and alleles underlying common diseases. I have also carried systematic population-genetic simulations in order to benchmark the performance of VAAST and VAAST 2.0 under different genetic scenarios, and these demonstrate that VAAST 2.0 is the most robust and broadly applicable method available today for identification of genes involved in common genetic diseases such as breast cancer, hypertriglyceridemia and Crohn disease.
机译：测序成本的快速下降正在彻底改变遗传学。在这方面，下一代测序数据的两种应用尤为重要。首先，高通量测序现在提供了一种快速而廉价的方法来研究非模型生物的基因组和遗传学。其次，人类个人基因组学数据为发现人类特征和疾病的遗传基础提供了独特的机会。我的博士研究重点是开发利用下一代测序数据研究遗传学的计算方法。在论文的第一章中，我介绍了有毒锥蜗牛Conus bullatus的一系列基于基因组的研究，Conus Bullatus是一种药学上重要的富含半胱氨酸的小肽，称为conopeptides或conotoxins。我利用毒液管中的高覆盖转录组序列和低覆盖率的基因组读数，开发了新方法来表征缺乏完整参考基因组的关键基因组特征，包括基因组大小，序列多样性，重复含量和移动元件密度。我还开发了一种用于芋螺毒素发现的计算机转录组学流水线，并用它来识别新型的芋螺毒素以及可能与芋螺毒素的翻译后加工有关的候选酶。；在第二章和第三章中我的论文中，我描述了一种概率疾病基因搜索算法VAAST（变异注释，分析和搜索工具），用于查找受损基因及其致病变异。我还描述了对原始代码库VAAST 2.0的强大扩展。在这些章节中，我证明了VAAST既是准确的孟德尔疾病罕见基因发现者，又是识别常见疾病的基因和等位基因的有力手段。我还进行了系统的种群遗传模拟，以便对VAAST和VAAST 2.0在不同遗传情况下的性能进行基准测试，这些证明了VAAST 2.0是当今用于鉴定常见遗传疾病基因的最强大和最广泛应用的方法。例如乳腺癌，高甘油三酯血症和克罗恩病。

著录项

  • 作者

    Hu, Hao.;

  • 作者单位

    The University of Utah.;

  • 授予单位 The University of Utah.;
  • 学科 Biology Genetics.;Biology Bioinformatics.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 90 p.
  • 总页数 90
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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