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

该项目旨在利用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改进短读数的微生物识别。虽然分类器速度很快，但它可能依赖于大量特征。这是因为它依赖于固定的 DNA 字长，当字长很长时，可能会有许多零频率。研究人员建议用零膨胀负二项式和泊松模型来补偿分类器，而不是传统上使用充其量是临时的语言“平滑”技术。第二个目标是减小全基因组分析的特征尺寸。对于长 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，研究人员建议在保持微生物分类准确性的同时减少特征空间。最后，第三个目标是能够对梯度（不断变化的环境因素）中微生物种群变化的函数进行建模和拟合，特别是当许多数据点丢失时。这个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，从而改善未来的威胁检测。研究人员正在开发数学方法来模拟环境如何被其微生物群落独特地识别。 由于不需要直接测量化学物质，因此该项目的结果将推动微量化学物质检测和法医学生物技术的进步。 一个例子是模拟土壤微生物群落因埋藏爆炸物而发生的变化，以加强对这些设备的检测并确保我们部队的安全。

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.