III: Small: Collaborative Research: A Scalable and Efficient Optical Map Assembler

III：小型：协作研究：可扩展且高效的光学地图组装器

• 批准号: 1618814
• 负责人: Christina Boucher
• 金额: $ 38.4万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618814&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Scalable

Optical mapping is a laboratory technique for constructing ordered high-resolution optical maps from stained molecules of DNA. The popularity of this type of data has amplified because the commercial production of the data has improved in terms of quality, expense, and throughput. For example, BioNano Genomics released a new generation of optical mapping technology called the Irys System in 2015, which has been used to uncover diploid variation in the human genome. However, the raw optical mapping data, called Rmaps, is not inherently useful by itself and must be first assembled into a genome-wide optical map; a computational process that has very few nonproprietary solutions. The optical map assembly problem that aims to stich together the Rmap data into a genome-wide optical map has some similarities to genome assembly that aims to build contiguous sequences corresponding to the genome of interest from short sequence reads. Although this similarity exists, there are some significant differences that have prevented the direct application of genome assemblers to this latter problem. The main objective of this proposed work is to build a scalable and efficient optical map assembler through the exploration and adaptation of genome assembly algorithms and methods. This research objective will be enhanced by an education plan that aims to broaden the participation in computer science by creating research opportunities for female graduate students, as well as, high school senior students. The plan is to redevelop the existing bioinformatics graduate course so that it: (1) can be cross-listed with the Department of Biology and thus, increase the female enrolment, and (2) will be project based and thus, produce supportive relationships between female students. The team will conduct comprehensive surveys of graduate students in the redeveloped course, as well as the other graduate courses in computer science, to determine whether the changes are impactful. We plan to disseminate our findings to other institutions.Even with significantly high coverage and various insert sizes, genome assembly and structural variation detection are tenuous computational processes using short read data alone due to repetitive regions in the genome. Optical maps, which are ordered genome-wide high-resolution restriction maps that specify the positions of occurrence of one or more short nucleotide sequences, are one such type of data. The raw optical mapping data identified by the image processing is an ordered sequence of fragment lengths. The (unassembled) optical mapping data produced by the system are referred to as Rmaps and are synonymous with sequence reads in standard genome sequencing. Optical mapping has gained popularity due to (1) the ability to automate the generation of the data; and (2) its use in several large sequencing projects---including the sequencing projects of goat, budgerigar, and Ambler trichopoda. With this popularity comes the growing need for means to analyze optical mapping data. The goal of this proposed work is to build an optical map assembler that is efficient for genomes of various sizes that will accept as input a set of Rmaps and return an assembled genome-wide optical map. Thus, we have divided this larger goal into the following intermediate research objectives that we plan to tackle in succession: (1) Develop a Rmap error correction method; (2) create a robust alignment algorithm; and (3) create a succinct graph representation of a genome wide optical map. Our algorithmic contributions are not limited to optical mapping data but can be extended to other type of data, e.g., PacBio and genetic linkage maps. We will actively engage in knowledge transfer through these research objectives and our outreach and education plan. These efforts will redevelop a bioinformatics course in order to attract a greater number of female students and foster collaborative relationships, and continue an ongoing high school outreach program. In addition to these activities, we will survey the students in the redeveloped course and other graduate classes to see whether the changes were impactful, and mentor a senior high school student researcher during the summers.

光学映射是一种实验室技术，用于从染色的DNA分子构建有序的高分辨率光学映射。 这种类型的数据的受欢迎程度已经扩大，因为数据的商业生产在质量，费用和吞吐量方面有所改善。例如，BioNano Genomics在2015年发布了新一代光学映射技术Irys System，该技术已用于揭示人类基因组中的二倍体变异。 然而，原始的光学映射数据，称为Rmaps，本身并不是天生有用的，必须首先组装成一个全基因组的光学地图;一个计算过程，有很少的非专有解决方案。旨在将Rmap数据斯蒂奇到全基因组光学图谱中的光学图谱组装问题与旨在从短序列读段构建对应于感兴趣基因组的连续序列的基因组组装具有一些相似性。 虽然存在这种相似性，但存在一些显著的差异，这些差异阻止了基因组组装器直接应用于后一个问题。 本文的主要目标是通过对基因组组装算法和方法的探索和改进，构建一个可扩展的高效的光学图谱组装器。这项研究目标将通过一项教育计划得到加强，该计划旨在通过为女研究生和高中高年级学生创造研究机会，扩大对计算机科学的参与。 计划重新开发现有的生物信息学研究生课程，以便：（1）与生物系交叉列出，从而增加女生入学率;（2）以项目为基础，从而在女生之间建立支持关系。 该团队将对重新开发的课程的研究生以及计算机科学的其他研究生课程进行全面调查，以确定这些变化是否具有影响力。 我们计划将我们的发现传播给其他机构。即使具有显著高的覆盖率和各种插入大小，由于基因组中的重复区域，仅使用短读段数据的基因组组装和结构变异检测是脆弱的计算过程。 光学图谱是有序的全基因组高分辨率限制性图谱，其指定一个或多个短核苷酸序列的出现位置，是这样一种类型的数据。通过图像处理识别的原始光学映射数据是片段长度的有序序列。系统产生的（未组装的）光学映射数据被称为Rmaps，与标准基因组测序中的序列读数同义。 光学图谱已经得到普及，因为（1）能够自动生成数据;（2）它在几个大型测序项目中的使用-包括山羊、虎皮鹦鹉和Ambler Appropoda的测序项目。 随着这种普及，对分析光学测绘数据的手段的需求日益增长。 这项工作的目标是建立一个光学地图汇编程序，是有效的各种大小的基因组，将接受作为输入的一组Rmaps和返回组装的全基因组光学地图。 因此，我们将这个更大的目标分为以下中期研究目标，我们计划连续解决：（1）开发Rmap错误校正方法;（2）创建一个强大的比对算法;（3）创建一个简洁的全基因组光学图谱的图形表示。我们的算法贡献不限于光学映射数据，而是可以扩展到其他类型的数据，例如，PacBio和遗传连锁图谱。我们将通过这些研究目标和我们的推广和教育计划积极参与知识转移。这些努力将重新开发生物信息学课程，以吸引更多的女学生和促进合作关系，并继续进行一项正在进行的高中外联方案。除了这些活动，我们将调查学生在重新开发的课程和其他研究生班，看看这些变化是否有影响力，并指导高中学生研究员在夏季。