III: Small: Reconstructing viral population without using a reference genome

III：小：不使用参考基因​​组重建病毒群体

• 批准号: 1421908
• 负责人: Raj Acharya
• 金额: $ 50万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421908&HistoricalAwards=false
• 关键词: III; Small; Reconstructing; viral; population

Next-generation sequencing (NGS), which allows sampling millions of short DNA sequences from a genome, has revolutionized the field of genomics. One area of particular importance is the reconstruction of genomes (haplotypes) from a viral population, which is a fundamental problem in virology, evolutionary biology, and human health. Though there have been several methods developed to take advantage of NGS data, those are limited to populations for which a reference genome is available. This excludes many important cases, such as RNA viruses or certain HIV/HCV viral populations. In such situations, the haplotypes are sufficiently divergent as to render the reference meaningless. Moreover, most algorithms are not robust in the presence of recombination, which is a common occurrence in many viral populations. The achievement of this project's aims will allow for the full potential of NGS data to be realized in the field of virology. In particular, it will help to propel the understanding of viral population dynamics and give biologists powerful tools to understand disease progression and enable novel treatment and prevention strategies. The algorithms and software developed will be made freely available for use through software sharing platforms like GitHub or Galaxy. The PIs will offer a strong educational component including (a) graduate and undergraduate classes that use the output of the proposed research, and (b) development of a seminar series. The PIs will (a) train future generations of scientists and engineers to enhance and use bioinformatic/genomic cyber resources; (b) facilitate creative, cyber-enabled boundary-crossing collaborations, including those with industry and international dimensions, to advance the frontiers of science and engineering and broaden participation in STEM fields.This project?s aim is to develop probabilistic De Bruijn graphs and network flow on such graphs for the reconstruction of viral population when a reference is not available. Given NGS data, the algorithms should determine the number, sequences, and relative frequencies of the haplotypes. This project's proposed algorithms are based on a unique combination of established techniques (e.g. maximum likelihood, expectation-maximization, clustering, Lander Waterman statistics) with novel propositions for probabilistic De Bruijn graphs, machine learning, and network flows that are of interest in other applications. The PI and Co-PIs have complementary backgrounds in virology, machine learning, network flow, and genome reconstruction problems.

下一代测序（NGS）允许从基因组中抽样数百万个短的DNA序列，彻底改变了基因组学领域。一个特别重要的领域是病毒种群的基因组（单倍型）的重建，这是病毒学，进化生物学和人类健康中的基本问题。尽管已经开发了几种利用NGS数据的方法，但这些方法仅限于参考基因组可用的种群。这不包括许多重要病例，例如RNA病毒或某些HIV/HCV病毒群体。在这种情况下，单倍型具有足够的分歧，以使参考文献毫无意义。此外，在存在重组的情况下，大多数算法并不强大，这在许多病毒群体中都是常见的。该项目目标的实现将使NGS数据的全部潜力在病毒学领域实现。特别是，它将有助于推动对病毒种群动态的理解，并为生物学家提供强大的工具，以了解疾病进展并实现新颖的治疗和预防策略。 开发的算法和软件将通过GitHub或Galaxy等软件共享平台免费使用。 PI将提供强大的教育组成部分，包括（a）使用拟议研究的产出的研究生和本科课程，以及（b）开发研讨会系列。 PI将（a）培训子孙后代的科学家和工程师，以增强和使用生物信息学/基因组网络资源； （b）促进包括行业和国际维度在内的创造性，支持网络的边界越过的合作，以推进科学和工程的前沿，并扩大参与STEM领域的参与。该项目的目的是开发概率de bruijn图形和网络流量，以在此类图上进行此类图形，以在参考资源不可用的情况下进行此类参考人群的重建。给定NGS数据，算法应确定单倍型的数量，序列和相对频率。 该项目的拟议算法基于建立技术的独特组合（例如，最大似然，期望最大化，聚类，集群，Lander Waterman Statistics）以及有关概率de Bruijn图，机器学习的新颖命题，机器学习和网络流量以及其他应用中感兴趣的网络流。 PI和CO-PI具有病毒学，机器学习，网络流和基因组重建问题的互补背景。