权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Methods for detecting short indels from high-throughput sequence data

从高通量序列数据中检测短插入缺失的方法

基本信息

批准号：
8706938
负责人：
Vikas Bansal
金额：
$ 19.38万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-07-25 至 2016-04-30
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): In recent years, high-throughput sequencing technologies have transformed our understanding of human genetic variation by enabling the sequencing of individual human genomes as well as sequencing on a population-scale. Short insertions/deletions (indels) represent the second most frequent form of variation in the human genome, which is also functionally important. Indels have not received as much attention as single nucleotide variants (SNVs) and structural variants in part because the detection of indels from high-throughput sequence datasets is challenging and available computational methods exhibit significantly lower sensitivity and specificity compared to methods that are designed to identify single nucleotide variants. Novel computational methods that address the challenge presented by the detection and genotyping of indels are thus urgently needed. We propose to develop novel methods for the detection of short indels from both individual and population-scale sequence datasets that will utilize information about indel error rates that are specific to sequence context as well as sequencing platform from large-scale sequence datasets in order to generate accurate indel calls and genotypes. The development of these methods will significantly enhance the ability of researchers to extract accurate information about genetic variation from sequencing datasets, improve their ability to identify variants that are associated with disease susceptibility and improve our understanding of the extent and distribution of short indels in the human genome.

描述（由申请人提供）：近年来，高通量测序技术通过实现个体人类基因组的测序以及群体规模的测序，改变了我们对人类遗传变异的理解。短插入/缺失（indels）是人类基因组中第二常见的变异形式，在功能上也很重要。插入缺失没有受到与单核苷酸变体（SNV）和结构变体一样多的关注，部分原因是从高通量序列数据集检测插入缺失具有挑战性，并且与设计用于鉴定单核苷酸变体的方法相比，可用的计算方法表现出显著较低的灵敏度和特异性。因此，迫切需要解决由indel的检测和基因分型所提出的挑战的新的计算方法。我们建议开发用于从个体和群体规模的序列数据集检测短indel的新方法，该方法将利用关于indel错误率的信息，这些信息特定于序列上下文以及来自大规模序列数据集的测序平台，以便生成准确的indel调用和基因型。这些方法的发展将显著增强研究人员从测序数据集中提取有关遗传变异的准确信息的能力，提高他们识别与疾病易感性相关的变异的能力，并提高我们对人类基因组中短indels的程度和分布的理解。