IIBR Informatics: Advancing Bioinformatics Methods using Ensembles of Profile Hidden Markov Models

IIBR 信息学：使用轮廓隐马尔可夫模型集成推进生物信息学方法

• 批准号: 2006069
• 负责人: Tandy Warnow
• 金额: $ 50万
• 依托单位: University of Illinois at Urbana-Champaign
• 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: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006069&HistoricalAwards=false
• 关键词: IIBR; Informatics; Advancing; Bioinformatics; Methods

Many steps in biological research pipelines involve the use of machine learning models, and these have become standard tools for many basic problems. Elaborations on basic machine learning models ("ensembles" of machine learning models) can provide improvements in accuracy compared to standard usage, for various biological questions. However, the design of these ensembles has been fairly ad hoc, and their use can be computationally intensive, which reduces their appeal in practice. This project will advance this technology by developing statistically rigorous techniques for building ensembles of machine learning models, with the goal of improving accuracy. The project will also develop methods that use these ensembles for new biological problems, including protein structure and function prediction. Broader impacts include software school, engagement with under-represented groups, and open-source software. Profile Hidden Markov Models (i.e., profile HMMs) are probabilistic graphical models that are in wide use in bioinformatics. Research over the last decade has shown that ensembles of profile HMMs (e-HMMs) can provide greater accuracy than a single profile HMM for many applications in bioinformatics, including phylogenetic placement, multiple sequence alignment, and taxonomic identification of metagenomic reads. This project will advance the use of e-HMMs by developing statistically rigorous techniques for building e-HMMs with the goal of improving accuracy and improving understanding of e-HMMs, and will also develop methods that use e-HMMs for protein structure and function prediction. Broader impacts include software schools, engagement with under-represented groups, and open-source software. Project software and papers are available at http://tandy.cs.illinois.edu/eHMMproject.html.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.

生物研究流程中的许多步骤都涉及使用机器学习模型，这些模型已经成为许多基本问题的标准工具。对于各种生物学问题，与标准使用相比，对基本机器学习模型（机器学习模型的“集合”）的阐述可以提供准确性的改进。然而，这些集合的设计相当特别，并且它们的使用可能是计算密集型的，这降低了它们在实践中的吸引力。该项目将通过开发用于构建机器学习模型集合的统计严格技术来推进这项技术，目标是提高准确性。该项目还将开发使用这些集合来解决新的生物学问题的方法，包括蛋白质结构和功能预测。更广泛的影响包括软件学校，参与代表性不足的群体，以及开源软件。 简档隐马尔可夫模型（即，轮廓障碍）是在生物信息学中广泛使用的概率图形模型。 过去十年的研究表明，在生物信息学的许多应用中，包括系统发生位置、多序列比对和宏基因组读数的分类鉴定，谱HMM（e-HMM）的集合可以提供比单个谱HMM更高的准确性。该项目将通过开发统计上严格的技术来构建e-Hacker，以提高准确性和加深对e-Hacker的理解，促进e-Hacker的使用，还将开发使用e-Hacker进行蛋白质结构和功能预测的方法。更广泛的影响包括软件学校，与代表性不足的群体的接触，以及开源软件。项目软件和论文可在www.example.com上获得http://tandy.cs.illinois.edu/eHMMproject.html.This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

Large-Scale Multiple Sequence Alignment and the Maximum Weight Trace Alignment Merging Problem

• DOI: 10.1109/tcbb.2022.3191848
• 发表时间: 2023-05-01
• 期刊: IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
• 影响因子: 4.5
• 作者: Zaharias，Paul;Smirnov，Vladimir;Warnow，Tandy
• 通讯作者: Warnow，Tandy

WITCH: Improved Multiple Sequence Alignment Through Weighted Consensus Hidden Markov Model Alignment

• DOI: 10.1089/cmb.2021.0585
• 发表时间: 2022-05-17
• 期刊: JOURNAL OF COMPUTATIONAL BIOLOGY
• 影响因子: 1.7
• 作者: Shen, Chengze;Park, Minhyuk;Warnow, Tandy
• 通讯作者: Warnow, Tandy

MAGUS+eHMMs: improved multiple sequence alignment accuracy for fragmentary sequences.

• DOI: 10.1093/bioinformatics/btab788
• 发表时间: 2022-01-27
• 期刊: Bioinformatics (Oxford, England)
• 作者: Shen C;Zaharias P;Warnow T
• 通讯作者: Warnow T