Hypothesis-driven Computational Genomics: Engaging Students in Lab Protocols and Bioinformatics via Inquiry

该项目利用三个跨学科课程来教育学生在基因组学，分子进化和基因和基因组的生物信息学分析领域。参加这些课程的学生来自几个专业，部门和学院，提供了一个多元化的多学科环境，具有很大的潜力，为同行学习。在发展假设的同时，学生们在分子生态学实验室和生物信息学课程中掌握了DNA序列分析的技能。他们分析他们帮助生成的宏基因组数据集，使用这些数据来测试关于微生物生态学，共生以及微生物在消化和营养中的作用的假设。这种主题策略有望积极吸引学生参与学习过程，帮助他们发展成为理解科学方法的批判性思考者。为了衡量这种方法的成功，学生们回答了一系列关于他们在整个序列中对生物信息学的印象和理解的问题。他们还在序列的开始和结束处测试他们的生物信息学知识。对参加所有三门课程、一门或两门课程或没有一门课程的学生的评估正在进行比较，以确定该计划的影响。序列的成功完成是生产谁是高素质的职业生涯在生物信息学的学生，与生物信息学家常规分析的数据集类型的工作。高通量数据集的生成和分析正在使德雷克塞尔大学的生物信息学教育现代化，三门课程（和基于案例研究的方法）有望成为其他机构的典范。

This project is utilizing three interdisciplinary courses to educate students in the realms of genomics, molecular evolution, and the bioinformatics analyses of genes and genomes. Students participating in these courses come from several majors, departments, and colleges, providing a diverse multidisciplinary environment with great potential for peer learning. While developing hypotheses, students are gaining hands on skills in DNA sequence analysis in the Molecular Ecology Laboratory and Bioinformatics courses. They analyze metagenomic datasets that they helped to generate, using these to test hypotheses about microbial ecology, symbiosis, and the roles of microbes in digestion and nutrition.This thematic strategy is expected to actively engage students in the learning process, helping them to develop as critical-thinkers who understand the scientific method. To measure the success of this approach, students are answering a series of questions about their impressions and perceived understanding of bioinformatics throughout the sequence. They are also being tested on their knowledge of bioinformatics at the beginning and end of the sequence. Assessments of students taking all three courses, one or two of the courses, or none of the courses are being compared to determine the program's impact. Successful completion of the sequence is producing students who are highly qualified for careers in bioinformatics, having worked with the types of datasets that bioinformaticians routinely analyze. The generation and analysis of high throughput datasets is modernizing bioinformatics education at Drexel, and the three-course sequence (and case-study based approach) is expected to serve as a model for other institutions.