SCH: INT: Enabling real time surveillance of antimicrobial resistance

抗菌素耐药性（AMR）是指生物体阻止抗菌素（例如抗生素）对其起作用的能力，由于它导致抗生素无效，导致疫情变得更加频繁、广泛和严重，因此已成为对公共卫生的严重威胁。据估计，美国每年有280万人感染耐药细菌，其中超过35000人是致命的。控制这些疫情的一种方法是实时识别抗菌素耐药性。目前，鉴定抗菌素耐药性最有效的方法是对生物样本（如鼻拭子或血液样本）进行高通量测序。测序技术的进步缩小了设备的尺寸，使它们可以放在一只手上，然而生物信息学分析——需要比较数百万或数十亿的DNA序列——一直局限于具有大量内存和磁盘空间的高性能计算机。这反过来又使抗菌素耐药性鉴定在资源匮乏的环境中受到限制，如美国农村地区。该项目将通过开发生物信息学分析方法，利用便携式计算设备（如手机和平板电脑）现场实时检测抗菌素耐药性，从而克服在农村地区检测抗菌素耐药性的挑战。为了实现这一点，由于使用便携式计算设备产生的计算限制，该项目将概念化和实现新的算法和接口。这一项目的成果将是对抗菌素耐药性进行实时便携式识别，可用于大幅提高社会控制和监测疫情的效率。此外，这些技术还将有助于实现病毒物种（如COVID-19）的鉴定，这将有助于在计算和测序资源有限的地区进行快速诊断。最后，这项工作的一个直接成果将是通过马琴佛罗里达机会学者项目为得不到充分服务的学生提供研究机会，该组织旨在培养第一代大学学者的成功。在项目的每一年，调查人员将与梅琴项目的协调员合作，招募一名学生作为研究助理，并与调查人员及其学员一起实践项目。该项目的目标是利用纳米孔技术创建现场实时检测抗菌素耐药性的移动生物信息学方法。预期的方法将在设备上工作，这意味着它们将只使用便携式设备上的硬件（RAM、缓存、硬盘、处理器）。特别是，该项目将致力于：(1)创建设备上的方法来识别生物样品中的细菌；(2)创建设备上的方法来识别生物样本中的AMR基因；最后，(3)评估方法的可用性，并为其广泛传播做准备。这将通过结合缓存无关算法和空间高效数据结构的最新进展来实现。简而言之，缓存无关算法将问题的输入分成更小的子集，以便每个子集都可以在缓存中求解，并组合成原始问题的解。该建议进一步带来的进步将产生超出所述应用程序的影响。由于便携式设备带来了巨大的计算挑战，包括更小的内存、缓存、硬盘，这项工作将导致新的算法和工具开发，将简洁的数据结构与缓存无关方法相结合。接下来，这项工作将促进对抗菌素耐药性机制的认识。抗生素的使用需要了解和保存，以确保它是明智的。该项目将通过实时检测抗菌素耐药性演变、持续和传播的驱动因素，有助于获得这样的理解。最后，进一步利用具有广泛应用前景的第三次测序技术。这项工作的一个具体应用是在缺乏测序和计算设施的地区实时检测COVID-19。因此，该项目将是第一个创建从实验室到床边的生物信息系统，用于检测抗菌素耐药性和COVID等病毒株。这将加深对该技术的研究，突出具体的改进和扩展领域，并对公共卫生产生重大影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Antimicrobial resistance (AMR) refers to the ability of an organism to stop an antimicrobial (e.g., antibiotic) from working against it and has become a serious threat to public health since it causes antibiotics to be ineffective, resulting in outbreaks becoming more frequent, widespread, and severe. It is estimated that 2.8 million people per year in the United States are infected with resistant bacteria, and more than 35,000 of these infections are lethal. One manner to control these outbreaks is with real-time identification of AMR. Currently, the most effective method for identification of AMR is to apply high-throughput sequencing to a biological sample (e.g., nose swab or blood sample). Advancements in sequencing technology have shrunken the size of the devices so that they can fit into one hand, however the bioinformatics analysis – requires comparing millions or billions of DNA sequences -- has been limited to high performance computers that have significant memory and disk space. This, in turn, makes AMR identification limited in low-resource settings, such as rural areas of the U.S. This project will overcome the challenge of detection of AMR in rural areas by developing bioinformatics analysis methods for on-site, real-time detection of AMR using portable computing devices (such as phones and tablets). To realize this, the project will conceptualize and implement novel algorithms and interfaces due to computing limitations created by using portable computing devices. The outcome of this project will be a real-time portable identification of AMR, which can be used to dramatically increase the efficiency in which society can control and monitor outbreaks. In addition, these techniques will also help realize identification of viral species (such as COVID-19), which will assist in rapid diagnosis in areas with limited computing and sequencing resources. Lastly, an immediate outcome of the work will be research opportunities to under-served students through the Machen Florida Opportunity Scholars program, an organization that aims to foster the success of first-generation university scholars. For each year of the program, the investigators will work with the coordinator of the Machen program to recruit a student to be a research assistant and work hands-on the project with the investigators and their trainees. The goal of this project is to create mobile bioinformatics methods for on-site, real-time detection of AMR using Nanopore technology. The expected methods will work on-device, meaning they will only use the hardware (RAM, cache, hard disk, processors) on the portable device. In particular, the project will aim to: (1) create on-device methods to identify the bacteria in a biological samples; (2) create on-device methods to identify the AMR genes in a biological sample; and lastly, (3) evaluate the usability of the methods and prepare for their wide-spread dissemination. This will be accomplished by combining the recent advancements in cache-oblivious algorithms with that of space-efficient data structures. Briefly, cache-oblivious algorithms divide the input of a problem into smaller subsets so that each can be solved in cache and combined into a solution to the original problem. This proposal further brings advancements that will have impact beyond the stated application. Since portable devices pose significant computational challenges, including smaller memory, cache, hard disk, this work will result in novel algorithm and tool development that combine succinct data structures with cache oblivious approaches. Next, this work will advance the knowledge of AMR mechanisms. The use of antibiotics needs to be understood and preserved in order to ensure it is judicious. This project will contribute to acquiring such an understanding by detecting the drivers of AMR evolution, persistence and dissemination in real-time. Lastly, it will further the use of third sequencing technology that have broad application. One specific application of this work is the real-time detection of COVID-19 in areas that lack sequencing and computing facilities. Thus, this project will be the first in creating a benchwork-to-bedside bioinformatic system for detection of AMR and viral strains such as COVID. This will deepen the study of the technology, highlight specific areas of improvement and expansion, and have significant impact on public health.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.