ATD: Improving Analysis of Microbial Mixtures through Sparse Reconstruction Algorithms and Statistical Inference

ATD：通过稀疏重建算法和统计推断改进微生物混合物的分析

• 批准号: 1120622
• 负责人: Simon Foucart
• 金额: $ 66.63万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1120622&HistoricalAwards=false
• 关键词: ATD; Improving; Analysis; Microbial; Mixtures

The project aims to improve microbial classification and comparison from next-generation sequencing technology. The first objective is to improve microbial identification from short reads, using a Bayesian classifier. While the classifier is fast, it can rely on a large set of features. This is due to its reliance on fixed DNA word sizes that may have many zero-frequencies when the word size is long. The investigators propose to compensate the classifier with a zero-inflated negative binomial and Poisson models, instead of traditionally using linguistic "smoothing" techniques that are ad-hoc at best. The second objective is to reduce the feature size for whole-genome analysis. For long DNA word sizes, there is an enormous feature space, and by using random manifolds, compressive sensing, and other techniques, the investigators propose to reduce the feature space while retaining accuracy of microbial classification. Finally, the third objective is to be able to model and fit functions to microbial population changes in a gradient (a changing environmental factor), especially when many of the data points are missing. This final objective will allow biologists and ecologists to now correlate the microbial composition (from the first two objectives) to environmental factors and to model microbial changes and thus improve future threat detection.The investigators are developing mathematical methods to model how an environment is uniquely identified by its microbial community. Because a chemical will not have to be measured directly, the projects' results will enable advances in biotechnology for trace chemical detection and forensics. An example is modeling soil microbial community changes in response to buried explosives in order to enhance detection of these devices and secure our troops.

该项目旨在改进下一代测序技术中的微生物分类和比较。第一个目标是使用贝叶斯分类器从短读中改进微生物识别。虽然分类器速度很快，但它可以依赖于一大组特征。这是因为它依赖于固定的DNA单词大小，当单词大小较长时，可能会有许多零频率。研究人员建议用零膨胀的负二项和泊松模型来补偿分类器，而不是传统上使用充其量是特别的语言“平滑”技术。第二个目标是减少全基因组分析的特征尺寸。对于长DNA字长，存在着巨大的特征空间，研究人员建议通过使用随机流形、压缩传感等技术来减少特征空间，同时保持微生物分类的准确性。最后，第三个目标是能够对梯度(不断变化的环境因素)中的微生物种群变化建立模型并使其适应函数，特别是在许多数据点缺失的情况下。这一最终目标将使生物学家和生态学家现在能够将微生物组成(来自前两个目标)与环境因素相关联，并对微生物变化进行建模，从而改进未来的威胁检测。研究人员正在开发数学方法，以建模如何通过微生物群落唯一地识别环境。由于不需要直接测量化学物质，这些项目的结果将使微量化学物质检测和取证方面的生物技术取得进展。一个例子是模拟土壤微生物群落的变化，以响应埋在地下的爆炸物，以加强对这些装置的探测，并确保我们部队的安全。