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

BIGDATA：IA：协作研究：测序数据中的简约异常检测

基本信息

批准号：
1741264
负责人：
Jennifer Erway
金额：
$ 18.11万
依托单位：
Wake Forest University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1741264&HistoricalAwards=false
关键词：
BIGDATA IA Collaborative Research Parsimonious

项目摘要

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.

基因组包含了构建生物体的一整套指令。结构变异是指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性的理解。高通量测序技术的最新进展已经导致大量基因组数据的收集。正因为如此，需要快速和强大的算法来识别结构变体，这是罕见的，容易受到噪音的影响。这项研究将从根本上有助于优化方法的大规模问题的计算基因组学。这些算法将在生物学、数学和计算机科学界内外公开传播。研究生将通过这一跨学科研究进行科学研究和编程培训，并将高度鼓励来自代表性不足背景的学生的参与。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。这些问题特别难以解决，因为它们是高维的，并且数据是有噪声的和不精确的。这项研究将利用测序基因组中的已知关系，以提高在数据覆盖率低且多个相关个体被测序的人群研究中识别基因组变异的准确性。具体而言，拟议的研究将（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型稀疏优化方法，以及（iii）验证现有基因组数据集并预测新数据。