Smith Jason T, Harris Justine C, Lopez Oscar J, Valverde Laura, Borchert Glen M
Biology Department, University of South Alabama, Mobile, Alabama, 36688.
Biochem Mol Biol Educ. 2015 May-Jun;43(3):154-61. doi: 10.1002/bmb.20848. Epub 2015 Jan 30.
The sequencing of whole genomes and the analysis of genetic information continues to fundamentally change biological and medical research. Unfortunately, the people best suited to interpret this data (biologically trained researchers) are commonly discouraged by their own perceived computational limitations. To address this, we developed a course to help alleviate this constraint. Remarkably, in addition to equipping our undergraduates with an informatic toolset, we found our course design helped prepare our students for collaborative research careers in unexpected ways. Instead of simply offering a traditional lecture- or laboratory-based course, we chose a guided inquiry method, where an instructor-selected research question is examined by students in a collaborative analysis with students contributing to experimental design, data collection, and manuscript reporting. While students learn the skills needed to conduct bioinformatic research throughout all sections of the course, importantly, students also gain experience in working as a team and develop important communication skills through working with their partner and the class as a whole, and by contributing to an original research article. Remarkably, in its first three semesters, this novel computational genetics course has generated 45 undergraduate authorships across three peer-reviewed articles. More importantly, the students that took this course acquired a positive research experience, newfound informatics technical proficiency, unprecedented familiarity with manuscript preparation, and an earned sense of achievement. Although this course deals with analyses of genetic systems, we suggest the basic concept of integrating actual research projects into a 16-week undergraduate course could be applied to numerous other research-active academic fields.
全基因组测序和遗传信息分析持续从根本上改变生物学和医学研究。不幸的是,最适合解读这些数据的人(接受过生物学训练的研究人员)通常因自身察觉到的计算能力局限而却步。为解决这一问题,我们开发了一门课程来帮助缓解这一限制。值得注意的是,除了为我们的本科生配备一套信息工具集外,我们发现我们的课程设计以意想不到的方式帮助学生为合作研究职业做好准备。我们没有简单地提供一门传统的基于讲座或实验室的课程,而是选择了一种引导式探究方法,即由教师选定一个研究问题,学生通过合作分析来研究,学生参与实验设计、数据收集和论文报告。虽然学生在课程的各个部分都学习了进行生物信息学研究所需的技能,但重要的是,学生还通过与搭档和整个班级合作以及撰写一篇原创研究论文,获得了团队合作经验并培养了重要的沟通技能。值得注意的是,在其开设的前三个学期,这门新颖的计算遗传学课程在三篇同行评审文章中产生了45篇本科生作者署名。更重要的是,参加这门课程的学生获得了积极的研究体验、新获得的信息学技术熟练程度、前所未有的论文撰写熟悉度以及一种成就感。虽然这门课程涉及遗传系统分析,但我们认为将实际研究项目整合到一门16周的本科课程中的基本理念可以应用于许多其他积极开展研究的学术领域。