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改进来自短读段的微生物鉴定。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字大小，当字大小很长时，可能会有许多零频率。研究人员建议用零膨胀负二项和泊松模型来补偿分类器，而不是传统上使用语言“平滑”技术，这些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。对于长的DNA字长，有一个巨大的特征空间，通过使用随机流形，压缩传感和其他技术，研究人员建议减少特征空间，同时保持微生物分类的准确性。最后，第三个目标是能够对微生物种群的梯度变化（变化的环境因素）进行建模和拟合，特别是当许多数据点缺失时。最终目标将使生物学家和生态学家能够将微生物组成（前两个目标）与环境因素相关联，并模拟微生物变化，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通过其微生物群落进行独特识别。 由于不必直接测量化学品，这些项目的结果将使生物技术在痕量化学品检测和法医学方面取得进展。 一个例子是模拟土壤微生物群落的变化，以应对埋在地下的爆炸物，以加强对这些装置的检测和保护我们的部队。

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.