III: AF: Medium: Collaborative Research: Scalable and Highly Accurate Methods for Metagenomics

微生物群落的宏基因组学研究可以产生数百万到数十亿的测序读数。为这些序列分配准确的分类标签是许多分析中的关键组成部分，但由于在环境或宿主相关群落中发现的大多数生物体不能在实验室中容易地培养这一事实而变得复杂。即使在可以培养的生物体中，也只有相对较少的生物体被测序，甚至是部分测序。因此，许多常见的生物体在已知基因组和基因的现有数据库中基本上不存在。因此，为宏基因组序列提供分类标签需要将序列数据库中包含的知识外推到以前看不见的DNA串。简单的基于相似性的方法（例如，挑选最佳数据库命中作为分类标签的最佳猜测）已被证明不够准确，导致开发更复杂的方法。需要进一步的发展来处理新兴测序技术的特征，例如大量插入和缺失的高错误率。到目前为止，宏基因组分类单元鉴定方法已经评估了它们估计细菌分类单元（种、属、科等）分布的能力。在宏基因组样本中。然而，不同的科学和临床环境可能需要特定类型的分析，这一类型的评估可能不适合所有环境。例如，在临床环境中，最重要的问题可能是检测是否存在特定病原体，而在科学环境中，最有趣的问题可能是能够确定观察到的读数是否来自从未见过的物种。必须开发新的评估策略，专门针对应用程序领域的特定需求。 该项目中开发的所有方法都将被制作成开源软件，供科学界免费使用。研究人员将提供培训活动，每年与资金提供给学生和博士后从全国各地，并推广计划，以少数民族服务机构和妇女？s学院。马里兰州大学帕克分校还将提供一个夏季REU项目，该团队将开发一个新的框架，用于将生物用例的正式定义与评估数据集和指标相结合，以确保正在开发的软件充分满足最终用户的需求。其次，他们将开发新的方法，用于基于标记的分类单元识别和丰度分析，这些方法可以利用多种信息来源（例如，多个标记）以及处理第三代测序技术的高错误率。这些方法将建立在开发TIPP的经验基础上-TIPP是该团队最近发布的一种分类学分析软件包，其性能优于领先的宏基因组分类学分析软件，特别是对于新序列或更长的高错误序列。最后，他们计划开发这些方法的高性能计算实现，以便能够快速分析样品。分析速度在临床环境中特别重要，其中医学治疗可能取决于方法可以返回分析的速率。在非医学应用中，速度也很重要，更快的分析使研究人员能够对微生物群落进行更深入或更广泛的分析。

Metagenomic studies of microbial communities can generate millions to billions of sequencing reads. The assignment of accurate taxonomic labels to these sequences is a critical component in many analyses, but is complicated by the fact that the majority of the organisms found in environmental or host-associated communities cannot be easily cultured in a laboratory. Even among the organisms that can be cultured, relatively few have been sequenced, even partially. Thus, many commonly encountered organisms are largely absent from existing databases of known genomes and genes. Providing taxonomic labels to metagenomic sequences, thus, requires extrapolating the knowledge contained in sequence databases to previously unseen DNA strings. Simple similarity-based approaches (e.g., picking the best database hit as the best guess at the taxonomic label) have been shown to be insufficiently accurate, leading to the development of more sophisticated methods. Further developments are necessary to handle the characteristics of emerging sequencing technologies, such as high error rates with large numbers of insertions and deletions. To date, metagenomic taxon identification methods have been evaluated with respect to their ability to estimate the distribution of bacterial taxa (species, genera, families, etc.) within a metagenomic sample. Yet, different scientific and clinical settings may require specific types of analyses, and this one type of evaluation may not be the most appropriate for all settings. For example, in a clinical setting the most important question may be to detect whether a specific pathogen is present, while in a scientific setting the most interesting question may be to be able to determine if an observed read comes from a never-been-seen-before species. New evaluation strategies must be developed that specifically target the specific needs of the application domain. All the methods developed in the project will be made into open-source software that is freely available to the scientific public. Researchers will provide training activities each year with funds available to students and postdocs from around the country, and an outreach program to minority serving institutions and women?s colleges. A summer REU program will also be provided at the University of Maryland, College Park.The team will develop a new framework for integrating the formal definition of biological use-cases with evaluation datasets and metrics in order to ensure the software being developed adequately addresses the needs of the end-users. Second, they will develop new approaches for marker-based taxon identification and abundance profiling that can leverage multiple sources of information (e.g., multiple markers) as well as handle the high error rates of third-generation sequencing technologies. These approaches will build upon experience developing TIPP - a taxonomic profiling package recently published by the team that outperforms the leading metagenomic taxonomic profiling software, in particular for novel sequences, or for longer, high-error sequences. Finally they plan to develop high-performance computing implementations of these methods in order to enable rapid analysis of sample. Speed of analysis is particularly important in clinical settings where medical treatments may depend on the rate at which the method can return an analysis. Speed is also important in non-medical applications where faster analyses enable researchers to perform deeper or broader analyses of microbial communities.