Discovering Novel Structural Genomic Rearrangements Using Deep Neural Networks

使用深度神经网络发现新的结构基因组重排

• 批准号: 9755117
• 负责人: Alexandra Marie Weber
• 金额: $ 3.72万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9755117
• 关键词: Affect; Algorithms; Benchmarking; Biological Sciences; Categories; Complex; Computing Methodologies; DNA; DNA Resequencing; DNA Sequence Rearrangement; Data; Data Set; Detection; Diagnostic; Disease; Future; Genome; Genotype; Haplotypes; Health; Human; Human Genome; Image; Image Analysis; Label; Methods; Molecular; Molecular Computations; Pattern; Process; Recurrence; Repetitive Sequence; Research; Research Personnel; Sampling; Structure; Techniques; Technology; Training; Validation; Variant; base; clinically relevant; comparative; computerized tools; cost; deep learning; deep neural network; design; genome sequencing; human disease; insertion/deletion mutation; new technology; novel; reference genome; structural genomics; tool

Abstract Accurately detecting structural variation in the genome is a challenging task. Many approaches have been developed over the last few decades, yet it is estimated that tens of thousands of variants are still being missed in a given sample. Many of these variants are missed due to the limitations of using short-read sequencing to identify large variants. Although many of these missed variants are located within complex regions of the genome, it has been shown that some still have clinical relevance making their discovery important. New platforms have been developed for sequencing the genome using long-reads and show promise for overcoming many of these limitations creating the ability to identify the full spectrum of simple and complex structural variants. Because this technology is relatively young, new computational approaches to support the analysis of long-read sequencing data can aid in the discovery of these variants which are still being missed. In addition to detecting novel variation in samples with long-read sequencing data, computational approaches can be developed to leverage these novel variant calls to reanalyze the hundreds of thousands of short-read datasets currently available. In this proposal, we plan to develop new computational approaches to identify novel structural variation in the genome. In Aim 1, we will apply a recurrence approach to analyze long read sequencing datasets utilizing deep neural networks. In Aim 2, we will develop a tool to derive profiles of structural variants predicted in long- reads which can be used to identify and genotype structural variants calls in short read data-sets. Together, these approaches will allow researchers to accurately characterize structural variation in both long and short- read datasets.

摘要 准确检测基因组中的结构变异是一项具有挑战性的任务。许多方法已经 在过去的几十年里发展起来的，但据估计，成千上万的变种仍然被遗漏 在给定的样本中。由于使用短读段测序的局限性，这些变体中的许多都被遗漏， 识别大的变量。尽管这些缺失的变异体中有许多位于细胞的复杂区域内， 尽管基因组中的一些基因已经被证明具有临床相关性，但它们的发现仍然很重要。新 已经开发了使用长读段进行基因组测序的平台，并显示出克服 这些限制中的许多产生了识别简单和复杂结构变体的全谱的能力。 由于这项技术相对年轻，新的计算方法来支持长读 测序数据可以帮助发现这些仍然被遗漏的变体。除了检测 对于具有长读序测序数据的样品中的新变异，可以开发计算方法， 利用这些新的变体调用来重新分析目前数十万个短读数据集， available.在这个建议中，我们计划开发新的计算方法来识别新的结构变异 在基因组中。在目标1中，我们将应用递归方法来分析长读段测序数据集， 深层神经网络。在目标2中，我们将开发一种工具，用于推导在长期预测中预测的结构变体的概况。 可以用于在短读段数据集中识别和基因分型结构变体调用的读段。在一起， 这些方法将使研究人员能够准确地描述长期和短期的结构变化， 读取数据集。