ABI Innovation: Integrated Approach to Improve Detection of Genome Rearrangements

这笔赠款将支持开发一种算法，用于从下一代测序(NGS)技术产生的数据中寻找基因组重排。NGS已成为生命科学研究的重要工具。这些技术被用来解决生物学各个领域的许多基本问题，使人们能够通过与参考基因组进行比较来找到样本(例如，相关物种)之间的确切差异。虽然在这类研究中可以很好地识别单核苷酸错配，但精确和可靠地检测更复杂的变化，即基因组重排，仍然是一个重大问题，解决这个问题将使大量生物学研究人员受益。预计在不久的将来，可用的基因组序列将泛滥，这将需要新的方法来自动进行功能预测，以处理当今无法想象的规模的“大数据”。可靠地识别重排将是这种自动化基因组注释的重要一步。预计罗格斯-卡姆登校园的本科生、研究生和博士后参与者(包括代表人数不足的少数群体的成员)将积极参与这些研究活动并进行强有力的互动。所有这些工作都将与支持校园高性能计算设施的NSF奖产生强大的协同作用。有了这个奖，将开发一种新的方法来寻找基因组重排，基于整合多条证据线和对映射配对阅读的逻辑约束。它将为参考序列中的每个核苷酸评估参与重排事件的可能性。考虑到NGS领域的快速变化，该方法还将允许纳入新类型的证据，例如，长时间阅读，以改进对重新排列的预测。作为这项研究的结果，功能算法将通过用户界面实现，显示复杂的重排，并以方便的图形形式突出显示来自序列数据的可用证据，以及基因组上下文信息。该算法将在出版物和科学会议上进行描述，并将公开提供。这将有助于解释检测到的基因组变化在进化、功能和其他方面的作用，并导致生物学许多领域的NGS数据分析的实质性改进。

This grant will support the development of an algorithm for finding genome rearrangements from data produced by the next-generation sequencing (NGS) technologies. NGS has become an essential tool for the life science research. These technologies are used to address many fundamental questions in various fields of biology allowing one to find exact differences between samples (for instance, related species) via comparison to a reference genome. While single-nucleotide mismatches are robustly identified in such studies, precise and reliable detection of more complex changes, genome rearrangements, still presents a significant problem and solving this problem will benefit a large number of biological researchers. The expected deluge of available genome sequences in the near future will require novel approaches for automated functional predictions dealing with "big data" on a scale unimaginable today. Reliable identification of rearrangements will be an important step in such automated genome annotation. Active involvement and a strong interaction of undergraduate, graduate and postdoctoral-level participants on Rutgers-Camden campus is foreseen (including members of underrepresented minority groups) in these research activities. All of this work will have a strong synergy with the NSF award supporting the high-performance computational facilities on campus.With this award, a novel approach for finding genome rearrangements will be developed, based on integrating multiple lines of evidence and logical constraints on mapped paired reads. It will evaluate for each nucleotide in a reference sequence a likelihood of involvement in a rearrangement event. Given the rapidly changing filed of NGS the methodology will also allow for including of novel types of evidence, for example, long reads, to improve predictions of rearrangements. As a result of this research a functional algorithm will be implemented with a user interface, displaying complex rearrangements and highlighting available evidence from the sequence data in a convenient graphical form, together with genomic context information. The algorithm will be described in publications and at scientific meetings and will be made publicly available. This will aid in interpretation of evolutionary, functional and other roles of the detected genomic changes and result in substantial improvements in NGS data analysis in many areas of biology.