CAREER: Algorithms for Next-Generation Genomics

职业：下一代基因组学算法

• 批准号: 1724784
• 负责人: Benjamin Raphael
• 金额: $ 29.21万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724784&HistoricalAwards=false
• 关键词: CAREER; Algorithms; Next; Generation; Genomics

CAREER: Algorithms for Next-Generation GenomicsThe objective of this project is to develop algorithms for new and emerging high-throughput DNA sequencing technologies. These technologies are lowering the cost of DNA sequencing by orders of magnitude and thereby enabling a variety of new applications. These new applications, combined with the varying characteristics of the DNA sequences produced by these technologies, are increasing demand for efficient algorithms to interpret the resulting large volumes of DNA sequence data. The PI will develop a new class of robust algorithms for genome assembly and discovery of DNA sequence variants. Some of these algorithms will rely on the availability of a closely related reference genome sequence, while others will operate de novo directly from the individual DNA sequences (i.e. reads) produced by a DNA sequencing machine. In the latter case, the PI will design algorithms that exploit longer range DNA sequence information available in newer single-molecule and nanopore sequencing technologies. These algorithms will retain high sensitivity and specificity while scaling to billions-trillions of nucleotides and thousands of genomes. Finally, the PI will introduce combinatorial algorithms for the study of genome rearrangements in heterogeneous mixtures of DNA sequences. Such mixtures arise in metagenomics or cancer genomics, where the DNA that is sequenced is a mixture of genomes from different species, or from cells harboring different mutations, respectively. The PI collaborates closely with biologists and technology developers to ensure relevance and applicability of the algorithms. At the same time, some of algorithms and techniques from graph theory, combinatorial optimization, and probability that are developed in the proposal are applicable to problems outside of biology. Broader ImpactThe proposed research will be integrated with an educational component that includes the development of undergraduate seminar in personal genomics, a summer research experience in computational biology for high-school students, and the incorporation of a computational biology module into a summer computing camp for 9th grade girls. The PI will continue to actively mentor and recruit undergraduate and graduate students, including women and underrepresented minorities. Finally, software implementing the algorithms will be freely distributed to the scientific community through a public webserver.

职业：下一代基因组学算法该项目的目的是为新的和新兴的高通量DNA测序技术开发算法。 这些技术通过数量级降低了DNA测序的成本，从而降低了各种新应用。 这些新应用，结合了这些技术产生的DNA序列的不同特征，它增加了对有效算法的需求，以解释所得的大量DNA序列数据。 PI将开发一种新的鲁棒算法，用于基因组组装和DNA序列变体的发现。 这些算法中的一些将依赖于密切相关的参考基因组序列的可用性，而其他算法将直接从单个DNA序列（即读取）直接从头运行，而DNA测序机产生了。 在后一种情况下，PI将设计算法，以利用新的单分子和纳米分子测序技术中可用的更长范围的DNA序列信息。 这些算法将保留高灵敏度和特异性，同时将核苷酸和数千个基因组扩展到数十亿亿美元。最后，PI将引入组合算法，以研究DNA序列异质混合物中的基因组重排。 这种混合物出现在宏基因组学或癌症基因组学中，其中测序的DNA分别是来自不同物种或具有不同突变的细胞的混合物。 PI与生物学家和技术开发人员紧密合作，以确保算法的相关性和适用性。 同时，该提案中开发的图理论，组合优化和概率的某些算法和技术适用于生物学以外的问题。更广泛的影响将与拟议的研究与一个教育组成部分集成，其中包括开发个人基因组学的本科研讨会，高中生的夏季研究经验以及将计算生物学模块纳入9年级女孩的夏季计算营。 PI将继续积极指导和招募本科生和研究生，包括妇女和代表性不足的少数民族。最后，实施该算法的软件将通过公共网络服务器自由分发给科学界。