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

对微生物群落的元基因组研究可以产生数百万到数十亿的测序读数。为这些序列赋予准确的分类标签是许多分析中的一个关键组成部分，但由于在环境或寄主相关群落中发现的大多数生物不容易在实验室中培养，这一事实变得复杂起来。即使在可以培养的生物体中，被测序的相对较少，甚至是部分测序。因此，许多常见的生物在现有的已知基因组和基因数据库中基本上是不存在的。因此，向元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未见的DNA字符串。简单的基于相似性的方法(例如，选择最好的数据库命中作为分类标签的最佳猜测)已被证明不够准确，导致了更复杂方法的发展。需要进一步的发展来处理新兴测序技术的特点，例如大量插入和缺失的高错误率。到目前为止，元基因组分类群鉴定方法已经根据它们估计细菌分类群(种、属、科等)的分布的能力进行了评估。在一个超基因组样本中。然而，不同的科学和临床环境可能需要特定类型的分析，而这一类型的评估可能不是最适合所有环境的。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的病原体，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以前从未见过的物种。必须制定新的评估战略，专门针对应用领域的具体需求。该项目开发的所有方法都将被制成开放源码软件，供科学公众免费使用。研究人员将每年向全国各地的学生和博士后提供培训活动，并向少数民族服务机构和S女子学院开展外联项目。马里兰大学学院公园还将提供夏季REU项目。该团队将开发一个新的框架，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，他们将开发基于标记的分类群识别和丰度剖面图的新方法，这些方法可以利用多种信息源(例如，多个标记)，并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个分类剖析包，其性能优于领先的元基因组分类剖析软件，特别是对于新序列或更长、高错误的序列。最后，他们计划开发这些方法的高性能计算实现，以便能够对样本进行快速分析。分析速度在临床环境中尤其重要，在临床环境中，医疗处理可能取决于该方法返回分析的速度。速度在非医疗应用中也很重要，在非医疗应用中，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.