Computational methods for variant calling and haplotyping using long-read sequencing technologies

项目摘要/摘要 在这个项目中，我们建议开发全基因组单倍型的计算方法和工具 使用太平洋生物科学公司和牛津大学等长读测序技术的小型变异叫声 纳米孔和链接读取技术。单倍型信息是解释遗传变异的关键 在个体基因组、疾病图谱、临床基因组学和其他几种人类遗传分析中 变种。在使用短阅读测序的人类基因组中缺乏阶段或单倍型信息是一种 识别与复合杂合突变有关的疾病的主要障碍。600多个基因 序列同源性高的重叠片段复制和100多个此类基因的变异 与罕见的孟德尔疾病和包括癌症在内的复杂疾病有关。无法检测到 利用短读测序技术在基因组重复区域中获得高精度的变异 降低了在医学遗传学研究中识别引起疾病的突变的能力。在目标1中，我们将开发 一种基于长阅读的二倍体基因分型通用计算方法，可实现准确的单倍体分型 对于使用长阅读和连锁阅读以及准确的小片段的单核苷酸变体和短indels 使用短信技术的变体呼叫。在目标2中，我们将开发敏感映射的计算方法 在人类基因组的重复区域中的短信读取和准确的变体调用，目前 排除在参考人类基因组的基准小变体呼叫集之外。最后，在目标3中，我们将 利用AIMS 1和AIMS 2中的方法对使用短信测序的多个基因组执行变体调用 对变体PSV进行分类并利用此目录改进读取映射和变体调用的技术 基因组重复区域短读测序的准确性。我们将在 强大且计算高效的软件工具，并使用公开可用的Long- 读取不同祖先的多个人类基因组的序列数据集。

Project Summary/Abstract In this project, we propose to develop computational methods and tools for whole-genome haplotyping and small variant calling using long-read sequencing technologies such as Pacific Biosciences and Oxford Nanopore and linked-read technologies. Haplotype information is crucial for interpretation of genetic variation in individual genomes, disease mapping, clinical genomics and several other analysis of human genetic variation. The lack of phase or haplotype information in human genomes sequenced using short reads is a major barrier in identifying disease associations with compound heterozygous mutations. More than 600 genes overlap segmental duplications with high sequence identity and variants in more than 100 such genes have been associated with rare Mendelian disorders and complex diseases including cancer. The inability to detect variants with high accuracy in duplicated regions of the genome using short-read sequencing technologies reduces the ability to identify disease causing mutations in medical genetics studies. In Aim 1, we will develop a general computational method for long-read based diploid genotyping that will enable accurate haplotyping for single nucleotide variants and short indels using long-read and linked-reads as well as accurate small variant calling using SMS technologies. In Aim 2, we will develop computational methods for sensitive mapping of SMS reads and accurate variant calling in repetitive regions of the human genome that are currently excluded from benchmark small variant call sets for reference human genomes. Finally, in Aim 3, we will leverage the methods from Aims 1 and 2 to perform variant calling on multiple genomes sequenced using SMS technologies to catalog variant PSVs and leverage this catalog to improve read mapping and variant calling accuracy of short-read sequencing in repetitive regions of the genome. We will implement the methods in robust and computationally efficient software tools and benchmark their accuracy using publicly available long- read sequence datasets for multiple human genomes of diverse ancestries.