CAREER: A comprehensive computational platform for detecting yet unseen microbial pathogens

职业：用于检测尚未见过的微生物病原体的综合计算平台

• 批准号: 2239114
• 负责人: Todd Treangen
• 金额: $ 59.99万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239114&HistoricalAwards=false
• 关键词: CAREER; comprehensive; computational; platform; detecting

We are in the golden age of our ability to read and write DNA. The sequencing of genomic data found in nature is now democratized, opening the door to a digital library of countless documents of evolutionary history. In parallel, the synthesis of engineered DNA for widespread societal benefits is now automated and affordable, showing incredible promise in recent years. Indeed, recent advances in reading and writing DNA have the potential to resolve major global challenges, such as boosting crop yield to address food shortages, mitigating pollution through carbon capture, and improving pandemic response and preparedness. While these remarkable technological advances can be used for broad societal benefit, they are underutilized for tracking yet-unseen pathogens that can result in widespread economic and public harm. Our ability to read and write DNA at scale, especially with respect to uncovering yet-unseen pathogens and intentionally or unintentionally enhancing existing pathogens, has far outstripped computational tools capable of tracking and preventing misuse. To address this critical gap, the research detailed in this proposal will focus on developing computational tools to aid in detecting yet-unseen pathogens and preventing intentional or unintentional misuse of synthetic DNA. This project will advocate for a novel paradigm of pathogen detection and monitoring through the pursuit of innovative computational methods and approaches. The research methodology will be motivated by tried and tested approaches in biosurveillance while pursuing innovative computational strategies. Specifically, this project will address four fundamental computational research challenges: (1) yet-unseen pathogen characterization -- contextualizing taxonomy-based approaches with functions of concern to learn how to identify novel pathogens, (2) petabyte-scale cataloging of microbial dark matter -- combining probabilistic algorithm development with comparative genomic approaches for the query of known and rare microbial genes, (3) genetic engineering detection -- discerning engineered DNA from naturally occurring DNA through the development of graph-based pan genomes combined with codon usage bias models, and (4) implementation of the modular computational platform GuarDNA -- integrating everything together into the first-ever comprehensive platform specifically designed for both biosecurity and biosurveillance. GuarDNA will be designed following software engineering best practices, with code modularity as a key focus to facilitate community engagement. These four research challenges will be accompanied by a comprehensive test and evaluation plan, which both provides an individual assessment of each of the four research thrusts as well as continuous integration testing to provide an overarching evaluation of the GuarDNA platform. This research effort will open the door to novel computational approaches for biosecurity and biosurveillance.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.

我们正处于读取和写入 DNA 能力的黄金时代。自然界中发现的基因组数据的测序现在已经民主化，为包含无数进化历史文献的数字图书馆打开了大门。与此同时，用于广泛社会效益的工程 DNA 合成现在已实现自动化且价格低廉，近年来显示出令人难以置信的前景。事实上，DNA读写方面的最新进展有可能解决重大的全球挑战，例如提高作物产量以解决粮食短缺、通过碳捕获减轻污染以及改善流行病应对和准备。虽然这些显着的技术进步可用于产生广泛的社会效益，但它们在追踪尚未发现的病原体方面却没有得到充分利用，这些病原体可能会导致广泛的经济和公共危害。我们大规模读取和写入 DNA 的能力，特别是在发现尚未发现的病原体以及有意或无意增强现有病原体方面，远远超过了能够跟踪和防止滥用的计算工具。为了解决这一关键差距，该提案中详细介绍的研究将重点开发计算工具，以帮助检测尚未见过的病原体并防止有意或无意地滥用合成 DNA。该项目将通过追求创新的计算方法和途径，倡导病原体检测和监测的新范式。研究方法将受到生物监测中经过尝试和测试的方法的推动，同时追求创新的计算策略。具体来说，该项目将解决四个基本的计算研究挑战：(1) 尚未见过的病原体表征——将基于分类学的方法与相关功能结合起来，以学习如何识别新型病原体，(2) PB 级微生物暗物质编目-- 将概率算法开发与比较基因组方法相结合，用于查询已知和稀有微生物基因，(3) 基因工程检测 -- 通过开发基于图的泛基因组并结合密码子使用偏差，从天然存在的 DNA 中辨别工程 DNA (4) 模块化计算平台 GuarDNA 的实施——将所有内容集成到第一个专门为生物安全和生物监测而设计的综合平台中。 GuarDNA 将按照软件工程最佳实践进行设计，以代码模块化为重点，以促进社区参与。这四项研究挑战将伴随一个全面的测试和评估计划，该计划既提供对四个研究重点的单独评估，也提供持续集成测试，以提供对 GuarDNA 平台的总体评估。这项研究工作将为生物安全和生物监测的新型计算方法打开大门。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

KombOver: Efficient k-core and K-truss based characterization of perturbations within the human gut microbiome

KombOver：基于高效 k 核和 K 桁架的人类肠道微生物组扰动表征

• DOI: 10.1142/9789811286421_0039
• 发表时间: 2023
• 期刊: Pacific Symposium on Biocomputing 2024
• 作者: Sapoval, Nicolae;Tanevski, Marko;Treangen, Todd J.
• 通讯作者: Treangen, Todd J.

Leveraging Large Language Models for Predicting Microbial Virulence from Protein Structure and Sequence

利用大型语言模型根据蛋白质结构和序列预测微生物毒力

• DOI: 10.1145/3584371.3612953
• 发表时间: 2023
• 期刊: and Health Informatics
• 作者: Quintana, Felix;Treangen, Todd;Kavraki, Lydia
• 通讯作者: Kavraki, Lydia

Microbial Community Profiling Protocol with Full-length 16S rRNA Sequences and Emu.

具有全长 16S rRNA 序列和 Emu 的微生物群落分析方案。

• DOI: 10.1002/cpz1.978
• 发表时间: 2024
• 期刊: Current protocols
• 作者: Curry,KristenD;Soriano,Sirena;Nute,MichaelG;Villapol,Sonia;Dilthey,Alexander;Treangen,ToddJ
• 通讯作者: Treangen,ToddJ