ABI Innovation: Integrated Approach to Improve Detection of Genome Rearrangements

这笔拨款将支持开发一种算法，用于从下一代测序（NGS）技术产生的数据中发现基因组重排。NGS已成为生命科学研究的重要工具。这些技术用于解决生物学各个领域的许多基本问题，允许人们通过与参考基因组的比较来发现样本（例如，相关物种）之间的确切差异。虽然单核苷酸错配在这些研究中得到了强有力的识别，但精确可靠地检测更复杂的变化，基因组重排，仍然是一个重要的问题，解决这个问题将使大量的生物学研究人员受益。在不久的将来，预计会有大量可用的基因组序列，这将需要新的自动化功能预测方法，以处理今天难以想象的规模的“大数据”。可靠的重排鉴定将是这种自动化基因组注释的重要一步。预计罗格斯-卡姆登校区的本科生、研究生和博士后水平的参与者（包括代表性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。有了这个奖项，一种发现基因组重排的新方法将被开发出来，该方法将基于对配对序列的多重证据和逻辑约束的整合。它将评估参考序列中每个核苷酸参与重排事件的可能性。考虑到NGS领域的快速变化，该方法还将允许包括新类型的证据，例如长读数，以改进对重排的预测。作为这项研究的结果，一个功能算法将实现与用户界面，显示复杂的重排和突出显示可用的证据，从序列数据在一个方便的图形形式，连同基因组背景信息。该算法将在出版物和科学会议上进行描述，并将向公众提供。这将有助于解释所检测到的基因组变化的进化、功能和其他作用，并导致许多生物学领域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.