CAREER: Algorithms for Next-Generation Genomics

职业：下一代基因组学算法该项目的目标是为新兴的高通量DNA测序技术开发算法。这些技术将DNA测序的成本降低了几个数量级，从而使各种新的应用成为可能。这些新的应用，加上这些技术产生的DNA序列的不同特征，增加了对有效算法的需求，以解释由此产生的大量DNA序列数据。PI将为基因组组装和DNA序列变异的发现开发一类新的强大算法。其中一些算法将依赖于密切相关的参考基因组序列的可用性，而另一些算法将直接从DNA测序机产生的单个DNA序列（即读取）从头开始操作。在后一种情况下，PI将设计算法，利用较新的单分子和纳米孔测序技术中可用的更长距离DNA序列信息。这些算法将保持高灵敏度和特异性，同时扩展到数十亿-数万亿核苷酸和数千个基因组。最后，PI将引入组合算法，用于研究DNA序列异质混合物中的基因组重排。这种混合出现在宏基因组学或癌症基因组学中，其中测序的DNA是来自不同物种或来自不同突变细胞的基因组的混合物。PI与生物学家和技术开发人员密切合作，以确保算法的相关性和适用性。同时，一些算法和技术，从图论，组合优化，和概率论中发展的建议是适用于生物学以外的问题。更广泛的影响拟议的研究将与教育组成部分相结合，包括发展个人基因组学的本科生研讨会，高中生的计算生物学暑期研究经验，以及将计算生物学模块纳入九年级女生的暑期计算夏令营。PI将继续积极指导和招收本科生和研究生，包括妇女和代表性不足的少数民族。最后，实现算法的软件将通过公共网络服务器免费分发给科学界。

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.