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

基因组包含构建有机体的一整套指令。结构变异是基因组中的插入和缺失等重排，它的发现促进了对物种进化和适应能力的理解。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，需要快速和健壮的算法来识别结构变体，这些变体很少见，而且容易受到噪声的影响。这项研究将从根本上为计算基因组学中大规模问题的优化方法做出贡献。这些算法将公开传播，供生物、数学和计算机科学界内外使用。通过这种跨学科研究，研究生将接受科学研究和编程方面的培训，并将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，而且数据噪声和不准确。这项研究将利用已测序基因组中的已知关系，在数据覆盖率低且多个相关个体被测序的情况下，提高在种群研究中识别基因组变异的准确性。具体地说，拟议的研究将(I)探索描述基因组中结构变异存在的统计模型，(Ii)开发和实施新的稀疏优化方法来检测基因组结构变异，以及(Iii)验证现有的基因组数据集并预测新的数据。

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.