Collaborative Research: IIBR Informatics: Keeping up with the genomes - Continual Learning of Metagenomic Data

合作研究：IIBR 信息学：跟上基因组的步伐 - 宏基因组数据的持续学习

• 批准号: 1936791
• 负责人: Gail Rosen
• 金额: $ 32.05万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936791&HistoricalAwards=false
• 关键词: Collaborative; Research; IIBR; Informatics; Keeping

Microbiomes are communities of microscopic organisms that are found everywhere on earth and are important in help to digest food in the gut. In the intestines, they can produce vitamins (good) or toxins (bad), so we need to understand what organisms and genes are present in these microscopic communities. This project uses artificial intelligence (AI) to identify organisms and their genes that live in microbiomes. Existing works for this effort have been hampered due to very rapidly growing amount of data, which often need to be repeatedly re-analyzed as new data become available. Such a process is not only inefficient, but is increasingly unsustainable, even for our growing computational resources. This approach is unique because it uses less computing power. Instead of continuously reentering massive amounts of data, the proposed state-of-the-art system has the ability to recall and reuse prior information without requiring reentering or re-analyzing prior data,saving substantial computing time and ultimately money. The goal is to find AI methods that achieve the best cost savings while not sacrificing accuracy. Many unidentified organisms are also found in microbiome experiments and are discarded and never used to identify the same organisms in other experiments. An AI based approach will keep, remember, and reuse their information in case those new organisms show up in again later in other experiments and eventually help in their identification. If the organism is identified in the future, the method can automatically update old data and the knowledgebase effectively and efficiently.This project will develop a dynamic, scalable, and semi-supervised learning framework that continually updates a classification model, with large unlabeled, experimental data. In addition to creating richer models that can leverage both reference and experimental data, the primary innovation is that the model will identify unknown organisms and proteins and integrate them into reference database for future model updates. This framework will be validated on the hundreds of metagenomic studies (composed of potentially thousands of samples) annually submitted to the microbiome computing website MG-RAST. MG-RAST is used by scientists to upload their microbiomes to study and improve agriculture, diagnoses, medicine, making biofuels, and a variety of other applications on which microorganisms have a deep effect. This work will contribute to college student training on artificial intelligence and its application to the microbiome. Results will be shared broadly with other educators and researchers through summer workshops.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.

微生物组是在地球上到处都可以找到的微生物群落，对于帮助消化肠道中的食物非常重要。 在肠道中，它们可以产生维生素（好的）或毒素（坏的），所以我们需要了解这些微观群落中存在哪些生物体和基因。 该项目使用人工智能（AI）来识别生活在微生物组中的生物体及其基因。 由于数据量的快速增长，这一努力的现有工作受到了阻碍，这些数据往往需要在新数据可用时反复重新分析。这样的过程不仅效率低下，而且越来越不可持续，即使对于我们不断增长的计算资源也是如此。这种方法是独特的，因为它使用更少的计算能力。 代替连续地重新输入大量数据，所提出的最先进的系统具有召回和重用先前信息的能力，而不需要重新输入或重新分析先前数据，从而节省大量计算时间并最终节省金钱。 我们的目标是找到在不牺牲准确性的同时实现最佳成本节约的人工智能方法。 在微生物组实验中也发现了许多未识别的生物体，这些生物体被丢弃，并且从未用于在其他实验中识别相同的生物体。基于人工智能的方法将保留、记住和重复使用它们的信息，以防这些新生物在其他实验中再次出现，并最终帮助识别它们。 如果将来生物体被识别，该方法可以有效地自动更新旧数据和知识库。该项目将开发一个动态的，可扩展的和半监督的学习框架，不断更新分类模型，具有大量未标记的实验数据。除了创建可以利用参考和实验数据的更丰富的模型外，主要创新在于该模型将识别未知生物和蛋白质，并将其整合到参考数据库中，以供未来模型更新。该框架将在每年提交给微生物组计算网站MG-RAST的数百项宏基因组研究（可能由数千个样本组成）中得到验证。 MG-RAST被科学家用来上传他们的微生物组，以研究和改善农业，诊断，医学，制造生物燃料以及微生物对其有深刻影响的各种其他应用。 这项工作将有助于大学生对人工智能及其在微生物组中的应用的培训。 该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Visualizing and Annotating Protein Sequences using A Deep Neural Network

• DOI: 10.1109/ieeeconf51394.2020.9443364
• 发表时间: 2020-11
• 期刊: 2020 54th Asilomar Conference on Signals, Systems, and Computers
• 作者: Zhengqiao Zhao;G. Rosen

Physiological and evolutionary contexts of a new symbiotic species from the nitrogen-recycling gut community of turtle ants

• DOI: 10.1038/s41396-023-01490-1
• 发表时间: 2023-08-09
• 期刊: ISME JOURNAL
• 影响因子: 11
• 作者: Bechade，Benoit;Cabuslay，Christian S.;Russell，Jacob A.
• 通讯作者: Russell，Jacob A.

Spatiotemporal Tracking of SARS-CoV-2 Variants using informative subtype markers and association graphs

使用信息丰富的亚型标记和关联图对 SARS-CoV-2 变体进行时空追踪

• DOI: 10.1109/ieeeconf51394.2020.9443496
• 发表时间: 2020
• 期刊: Spatiotemporal Tracking of SARS-CoV-2 Variants using informative subtype markers and association graphs
• 作者: Gupta, Ananya Sen;Zhao, Zhengqiao;Rosen, Gail
• 通讯作者: Rosen, Gail

Semi-supervised and Incremental VSEARCH for Metagenomic Classification

用于宏基因组分类的半监督增量 VSEARCH

• DOI: 10.1109/ssci51031.2022.10022184
• 发表时间: 2022
• 期刊: 2022 IEEE Symposium Series on Computational Intelligence (SSCI
• 作者: Ozdogan, Emrecan;Fasino, Adriana;Nguyen, Rachel;Sokhansanj, Bahrad;Rosen, Gail;Polikar, Robi
• 通讯作者: Polikar, Robi

How Scalable Are Clade-Specific Marker K-Mer Based Hash Methods for Metagenomic Taxonomic Classification?

• DOI: 10.3389/frsip.2022.842513
• 发表时间: 2022-07-05
• 期刊: FRONTIERS IN SIGNAL PROCESSING
• 作者: Gray，Melissa;Zhao，Zhengqiao;Rosen，Gail L.
• 通讯作者: Rosen，Gail L.