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.

我们正处于读写DNA能力的黄金时代。在自然界中发现的基因组数据的排序现在是民主化的，打开了无数进化历史文件的数字图书馆的大门。与此同时，用于广泛社会效益的工程DNA合成现在是自动化和负担得起的，近年来显示出令人难以置信的前景。事实上，最近在DNA读写方面取得的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问题，通过碳捕获减轻污染，以及改善流行病的应对和防范。虽然这些显著的技术进步可以用于广泛的社会效益，但它们在追踪可能造成广泛经济和公共危害的尚未见过的病原体方面没有得到充分利用。我们大规模读写DNA的能力，特别是在发现尚未见过的病原体和有意或无意地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，本提案中详细介绍的研究将侧重于开发计算工具，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，倡导一种新的病原体检测和监测范式。研究方法将受到生物监测中经过试验和测试的方法的激励，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关注的功能结合起来，学习如何识别新的病原体；(2)微生物暗物质的pb级编目——将概率算法开发与比较基因组方法相结合，用于查询已知和罕见的微生物基因；(3)基因工程检测——通过开发基于图形的泛基因组，结合密码子使用偏差模型，从自然存在的DNA中识别出工程DNA；(4)实现模块化计算平台GuarDNA——将所有内容整合到第一个专门为生物安全和生物监测设计的综合平台中。GuarDNA将按照软件工程最佳实践进行设计，并将代码模块化作为促进社区参与的重点。这四个研究挑战将伴随着一个全面的测试和评估计划，该计划既提供了四个研究重点的单独评估，也提供了持续的集成测试，以提供GuarDNA平台的总体评估。这项研究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。

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