CAREER: A Machine Learning Framework for Metagenomic Relationships

职业：宏基因组关系的机器学习框架

• 批准号: 0845827
• 负责人: Gail Rosen
• 金额: $ 67.97万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845827&HistoricalAwards=false
• 关键词: CAREER; Machine; Learning; Framework; Metagenomic

(This award is funded through the American Recovery and Reinvestment Act of 2009: Public Law 111-5).This is a CAREER award to support the research of Dr. Gail Rosen, in the Department of Computer and Electrical Engineering at Drexel University. Dr. Rosen is a third-year, tenure-track Assistant Professor.Dr. Rosen is developing a computational framework which enables identification and comparison of microorganisms to the environmental factors in their habitats. With recent technologies, DNA can be extracted directly from the millions of cells in any environment, and vast amounts of this DNA can now be sequenced from an environment, a technology known as metagenomics. The ability to analyse these metagenomic datasets lies in the problem of identifying the content of this fragmented mixture, which is composed of thousands or millions of genomes. Machine learning, with its ability to recognize patterns in complex data, is well-suited to this task. Dr. Rosen believes a machine learning approach to analyzing metagenomic datasets will allow the vast majority of the unculturable microbial species in an environment to be studied. For example, machine learning may enable biologists to determine the combinations of microbes and genetic capabilities present that promote soil health and increase crop-yield. Typically, sequenced DNA fragments are identified by scoring their alignment to previously sequenced organisms. Unfortunately, annotation protocols employed for single genome analysis do not hold for a mixture of environmental DNA. The Rosen lab is developing a general classification system to identify the genomic origin of sequenced fragments, methods to reconstruct fragment taxonomy and infer functional relationships through discriminative classification methods and a genomic word-frequency model to predict feature sparseness as a function of fragment length and database complexity. This research will also address fundamental biological questions about global genomic features and their effect on taxonomical and functional relationships.All tools development in this project will be posted on Dr. Rosen?s website:http://www.ece.drexel.edu/gailr/As a part of her CAREER plan, Dr. Rosen recognizes that this research endeavor is naturally interdisciplinary with concepts from electrical engineering, computer science, and biology. Therefore, her lab is developing an interdisciplinary graduate and undergraduate Bioinformatics curricula (in collaboration with a molecular ecologist) and K-12 modules to incorporate an NSF-funded K-12 program. A particularly creative activity includes image and audio processing applications for the classroom to illustrate math and science concepts through effects used in Photoshop and Garage Band applications. For example, the students are asked to transcribe particular musical chords and as a parallel, ?translate? codons to their amino acids. This activity illustrates the parallel of the Genetic Code to piano chords.

（该奖项由2009年美国复苏与再投资法案：公法111-5资助）。这是一个CAREER奖，用于支持Drexel大学计算机与电气工程系的Gail Rosen博士的研究。罗森博士是一名三年级的终身助理教授。罗森正在开发一种计算框架，可以将微生物与其栖息地的环境因素进行识别和比较。利用最近的技术，DNA可以在任何环境下直接从数百万个细胞中提取，大量的DNA现在可以从一个环境中测序，这种技术被称为宏基因组学。分析这些宏基因组数据集的能力在于识别这些由数千或数百万个基因组组成的碎片化混合物的内容。机器学习具有识别复杂数据模式的能力，非常适合这项任务。Rosen博士认为，分析宏基因组数据集的机器学习方法将允许对环境中绝大多数不可培养的微生物物种进行研究。例如，机器学习可以使生物学家确定微生物和遗传能力的组合，从而促进土壤健康和提高作物产量。通常，测序的DNA片段是通过对其与先前测序的生物体的比对进行评分来鉴定的。不幸的是，用于单基因组分析的注释协议并不适用于环境DNA的混合物。Rosen实验室正在开发一种通用分类系统，用于识别已测序片段的基因组起源，通过判别分类方法重建片段分类和推断功能关系的方法，以及一个基因组词频模型，用于预测片段长度和数据库复杂性的特征稀疏度。这项研究还将解决有关全球基因组特征及其对分类学和功能关系的影响的基本生物学问题。这个项目中所有的工具开发都将由罗森博士负责。作为她职业规划的一部分，罗森博士认识到，这项研究工作自然是跨学科的，涉及电子工程、计算机科学和生物学的概念。因此，她的实验室正在开发跨学科的研究生和本科生生物信息学课程（与分子生态学家合作）和K-12模块，以纳入nsf资助的K-12项目。一个特别有创意的活动包括图像和音频处理应用程序，通过在Photoshop和Garage Band应用程序中使用的效果来说明数学和科学概念。例如，学生被要求抄写特定的和弦，并作为一个平行，翻译？密码子和它们的氨基酸。这种活动说明了遗传密码与钢琴和弦的相似之处。