BIGDATA: IA: Collaborative Research: Parsimonious Anomaly Detection in Sequencing Data

基因组包含了构建生物体的一整套指令。结构变异是基因组的重排，如插入和缺失，其发现促进了对物种进化和适应性的理解。高通量测序技术的最新进展导致了大量基因组数据的收集。正因为如此，需要快速和鲁棒的算法来识别结构变异，这些变异是罕见的，容易受到噪声的影响。本研究将对计算基因组学中大规模问题的优化方法做出根本性的贡献。这些算法将在生物、数学和计算机科学社区内外公开传播。通过这项跨学科研究，研究生将获得科学研究和编程方面的培训，并高度鼓励来自代表性不足背景的学生参与。该奖项的研究目标是为计算基因组学中出现的大规模数据驱动问题开发计算工具。这些问题尤其难以解决，因为它们是高维的，而且数据有噪声和不精确。本研究将利用已测序基因组中已知的关系，在数据覆盖率低且对多个相关个体进行测序的情况下，提高群体研究中识别基因组变异的准确性。具体而言，该研究将(i)探索用于描述基因组中结构变异存在的统计模型，（ii）开发和实施用于基因组结构变异检测的新颖稀疏优化方法，以及（iii）验证现有基因组数据集并预测新数据。

Genomes contain the complete set of instructions for building an organism. Structural variants are rearrangements in the genome such as insertions and deletions, whose discovery advances the understanding of the evolution and the adaptability of species. Recent advances in high-throughput sequencing technologies have led to the collection of vast quantities of genomic data. Because of this, fast and robust algorithms are needed to identify structural variants, which are rare and are prone to noise. This research will contribute fundamentally to optimization methods for large-scale problems in computational genomics. The algorithms will be disseminated publicly for use within and outside the biology, mathematics, and computer science community. Graduate students will be trained in scientific research and programming through this interdisciplinary research, and the participation of students from under-represented backgrounds will be highly encouraged. The research objective of this award is to develop computational tools for large-scale data-driven problems arising in computational genomics. These problems are especially difficult to solve since they are high-dimensional and the data are noisy and inexact. This study will take advantage of known relationships in sequenced genomes to improve the accuracy of identifying genomic variants in population studies when there is both low coverage in the data and multiple related individuals are sequenced. Specifically, the proposed research will (i) explore statistical models for describing the presence of structural variants in genomes, (ii) develop and implement novel sparse optimization methods for genomic structural variant detection, and (iii) validate on existing genomic data sets and predict on new data.