SCH: INT: Enabling real time surveillance of antimicrobial resistance

SCH：INT：实现抗菌药物耐药性的实时监测

• 批准号: 2013998
• 负责人: Christina Boucher
• 金额: $ 118.78万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2013998&HistoricalAwards=false
• 关键词: SCH; INT; Enabling; real; time

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.

抗菌耐药性（AMR）是指制止有机体阻止抗菌剂（例如抗生素）对其进行抗衡的能力，并且已经对公共卫生构成了严重威胁，因为它会导致抗生素无效，从而导致疫情变得越来越频繁地变得越来越频繁，宽大，宽大，严重。据估计，美国每年有280万人感染了抗性细菌，其中35,000多人致命。控制这些暴发的一种方式是对AMR的实时识别。当前，鉴定AMR的最有效方法是将高通量测序应用于生物样品（例如鼻拭子或血液样本）。测序技术的进步已经缩小了设备的大小，以便它们可以融入一只手，但是生物信息学分析（需要比较数百万或数十亿个DNA序列）仅限于具有重要内存和磁盘空间的高性能计算机。反过来，这使AMR识别在低资源环境中有限，例如美国的农村地区，该项目将通过开发用于现场的生物信息学分析方法，使用便携式计算设备（例如电话和平板电脑）来克服农村地区检测AMR的挑战。为了实现这一目标，由于使用便携式计算设备创建的计算限制，该项目将概念化和实现新颖的算法和接口。该项目的结果将是对AMR的实时便携式识别，可大大提高社会控制和监测暴发的效率。此外，这些技术还将有助于实现病毒物种的识别（例如Covid-19），这将有助于在计算和测序资源有限的地区快速诊断。最后，这项工作的直接结果将是通过佛罗里达州佛罗里达州机会学者计划的研究机会，该组织旨在促进第一代大学学者的成功。对于该计划的每一年，调查人员将与Machen计划的协调员合作，招募学生成为研究助理，并与调查人员及其受训者一起操作项目。该项目的目的是创建移动生物信息学方法，用于使用纳米技术对AMR进行实时检测。预期的方法将在设备上起作用，这意味着它们仅在便携式设备上使用硬件（RAM，CACH，硬盘，处理器）。特别是，该项目的目的是：（1）创建设备方法以鉴定生物样品中的细菌； （2）创建设备方法以识别生物样品中的AMR基因；最后，（3）评估方法的可用性并为其广泛传播做准备。这将通过将合规算法的最新进步与空间有效的数据结构相结合来实现。简而言之，合并缓存算法将问题的输入分为较小的子集，以便每个子集都可以在缓存中求解并将其合并为原始问题的解决方案。该提案进一步带来了进步，将影响超出既定的申请。由于便携式设备带来了重大的计算挑战，包括较小的存储器，缓存，硬盘，因此这项工作将导致新颖的算法和工具开发，将简洁的数据结构与缓存的方法结合在一起。接下来，这项工作将提高AMR机制的知识。为了确保它是明智的，需要理解和保存抗生素的使用。该项目将通过实时检测AMR进化，持久性和传播的驱动因素来促进这种理解。最后，它将进一步使用具有广泛应用的第三个测序技术。这项工作的一种特定应用是在缺乏测序和计算设施的领域对Covid-19的实时检测。这是第一个创建基准对床的生物信息学系统的第一个项目，用于检测AMR和病毒菌株（例如Covid）。这将加深对技术的研究，突出特定的改进和扩展领域，并对公共卫生产生重大影响。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为通过评估被认为是珍贵的支持。

项目成果

Fast and exact quantification of motif occurrences in biological sequences.

• DOI: 10.1186/s12859-021-04355-6
• 发表时间: 2021-09-18
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Prosperi M;Marini S;Boucher C
• 通讯作者: Boucher C

Finding Maximal Exact Matches Using the r-Index.

使用 r 索引查找最大精确匹配。

• DOI: 10.1089/cmb.2021.0445
• 发表时间: 2022
• 期刊: Journal of computational biology : a journal of computational molecular cell biology
• 作者: Rossi,Massimiliano;Oliva,Marco;Bonizzoni,Paola;Langmead,Ben;Gagie,Travis;Boucher,Christina
• 通讯作者: Boucher,Christina

KARGAMobile: Android app for portable, real-time, easily interpretable analysis of antibiotic resistance genes via nanopore sequencing.

• DOI: 10.3389/fbioe.2022.1016408
• 发表时间: 2022
• 期刊: FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
• 影响因子: 5.7
• 作者: Barquero, Alexander;Marini, Simone;Boucher, Christina;Ruiz, Jaime;Prosperi, Mattia
• 通讯作者: Prosperi, Mattia

Challenges in large-scale bioinformatics projects

大规模生物信息学项目的挑战

• DOI: 10.1057/s41599-022-01141-4
• 发表时间: 2022
• 期刊: Humanities and Social Sciences Communications
• 作者: Morrison-Smith, Sarah;Boucher, Christina;Sarcevic, Aleksandra;Noyes, Noelle;O’Brien, Catherine;Cuadros, Nazaret;Ruiz, Jaime
• 通讯作者: Ruiz, Jaime

Experimental survey on power dissipation of k-mer-handling data structures for mobile bioinformatics

• DOI: 10.1109/bibm52615.2021.9669768
• 发表时间: 2021-01-01
• 期刊: 2021 IEEE international conference on bioinformatics and biomedicine
• 作者: Milicchio, F.;Prosperi, M.
• 通讯作者: Prosperi, M.