III: Small: Algorithms for decoding complex patterns of genomic variation

基因组通过不同的机制进化和多样化，包括小的点突变和大的结构变异（SV）。当整个种群的个体被测序时，我们观察到一个复杂的模式马赛克。其中一些是选择性限制的特征，如对缺氧的耐受性（高地人）或乳糖耐受性。在该提案的一个目标中，研究人员开发了用于识别特征遗传模式的计算技术，以识别适应这些选择性约束的基因。另一个目标是重建具有复杂变异模式的区域，如杀伤细胞免疫球蛋白样受体（KIR）区域。KIR多样性在介导免疫应答中起着重要作用，有助于理解包括类风湿性关节炎在内的疾病，控制HIV疾病进展以及某些白血病（血癌）的细胞替代疗法的成功率。研究人员将使用组合算法、机器学习和群体遗传学的混合技术来解码遗传模式。该提案在该领域具有更广泛的影响，作为开发用于遗传分析的高效计算工具的更大努力的一部分;这是现代廉价测序时代的一个关键问题。这里描述的工具和技术将对了解人群的遗传多样性以及个性化的医疗保健方法产生直接影响。该提案旨在通过两个方面来破译观察到的人群遗传变异。一方面，它着眼于单倍型基因组结构变异，并发现复杂免疫区域如KIR和HLA的基因组结构。在第二个推力中，研究人员分析了指示选择性约束的变化模式。对于选择签名，研究人员将使用缩放的站点频谱更好地理解当前可用的测试，并使用算法方法来识别更好的签名。对于重排的基因组区域，研究人员将使用优化算法来调整高度重复区域中的读段覆盖率。该提案在该领域具有更广泛的影响，作为开发用于遗传分析的有效计算工具的更大努力的一部分;这是现代廉价测序时代的一个关键问题。这里描述的工具和技术也将对理解代表性不足的人群的遗传多样性产生直接影响，并实现个性化的医疗保健方法。拟议的研究与本科和研究生教育紧密相连，因为这里的所有研究都将直接纳入跨学科课程。PI有一个强大的跟踪记录指导妇女和其他代表性不足的学生在计算机科学。

Genomes evolve and diversify through different mechanisms, including small point mutations, and large structural variations (SV). As entire populations of individuals get sequenced, we observe a complex mosaic of patterns. Some of these are characteristic of a selective constraint such as tolerance to lack of oxygen (for highlander populations), or lactose tolerance. In one aim of the proposal, the investigators develop computational techniques for identifying characteristic genetic patterns to identify genes that are adapting to these selective constraints. The other aims to reconstruct regions with complex variation patterns such as the Killer cell Immunoglobulin-like Receptor (KIR) region. KIR diversity plays a significant role in mediating immune response, helping with an understanding of diseases including rheumatoid arthritis, control of HIV disease progression as well as the success rate of cell replacement therapy for certain leukemias (blood cancer). The investigators will use a mix of techniques from combinatorial algorithms, machine learning, and population genetics to decode the genetic patterns. The proposal has broader impact in the field as part of a larger effort to develop efficient computational tools for genetic analysis; a critical problem in the modern era of inexpensive sequencing. The tools and technologies described here will have a direct impact on understanding the genetic diversity of populations, and towards a personalized approach to healthcare.The proposal seeks to decipher the observed genetic variation across populations using two thrusts. In one thrust, it looks to haplotype genomic structural variation, and discover the genomic architecture of complex immunological regions like KIR and HLA. In a second thrust, the investigators analyze patterns of variation that are indicative of selective constraints. For selection signatures, the investigators will provide a better understanding of currently available tests using the scaled site frequency spectrum, and use an algorithmic approach to identify a better discriminator. For the rearranged genomic regions, the investigators will use optimization algorithms to adjust read coverage in highly repetitive regions. The proposal has broader impact in the field as part of a larger effort to develop effcient computational tools for genetic analysis; a critical problem in the modern era of inexpensive sequencing. The tools and technologies described here as well will have a direct impact on understanding the genetic diversity of under-represented populations, and towards a personalized approach to healthcare. The proposed research is tightly connected to undergraduate and graduate education, as all research here will be directly incorporated in interdisciplinary classes. The PI has a strong track record mentoring womena and other under-represented students in Computer Science.