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

该项目旨在通过下一代测序技术改进微生物分类和比较。第一个目标是使用贝叶斯分类器从短读段中改进微生物鉴定。虽然分类器速度很快，但它可以依赖于大量的特征。这是由于它依赖于固定的DNA字长，当字长时可能有许多零频率。研究人员建议用零膨胀负二项和泊松模型来补偿分类器，而不是传统上使用的语言“平滑”技术，这种技术充其量是临时的。第二个目标是减少全基因组分析的特征尺寸。对于较长的DNA字长，存在巨大的特征空间，通过使用随机流形、压缩感知和其他技术，研究人员提出在保持微生物分类准确性的同时减少特征空间。最后，第三个目标是能够模拟和拟合微生物种群在梯度（变化的环境因素）中的变化，特别是在许多数据点缺失的情况下。这个最终目标将允许生物学家和生态学家现在将微生物组成（从前两个目标）与环境因素联系起来，并建立微生物变化模型，从而改进未来的威胁检测。研究人员正在开发数学方法来模拟微生物群落如何独特地识别环境。由于不需要直接测量化学物质，这些项目的结果将推动生物技术在痕量化学检测和取证方面的进步。一个例子是模拟土壤微生物群落对埋藏爆炸物的反应，以加强对这些装置的探测并保护我们的部队。

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.