COURSE OBJECTIVES 

Upon completion of this course, student researchers will be able to:

CO1. Justify the use of variables (different tissues, developmental time points, environmental conditions, etc.) when designing RNA-seq experiments for comparing transcriptomes.

CO2. Describe the different data outputs from various NGS technologies and their utility in research discovery.

CO 3. Interpret Linux-based bioinformatic code to align plant RNA sequencing reads to a reference.

CO 4. Quantify plant RNA sequencing reads and determine differentially expressed genes.

CO 5. Compare the expression patterns of orthologous gene families across model and crop species.

CO 6. Evaluate the use and limitations of a model species as a genetic resource.

CO 7. Navigate course materials, recognize how course assignments measure attainment of course objectives, and assign affective domains of learning (feelings, values, motivations, and attitudes) associated with each objective.

CO 8. Develop a research proposal using an innovative NGS approach. Learning Outcome for BIT 595 students.

Student reviews of BIT CPT

"I certainly learned a whole lot about computing from this course, but I think that the best thing I learned while enrolled in this class is how to deal with adversity and work with a team. I love working as a team, and I know that once I graduate from school and start working full-time I will be a part of a team in one way or another. Constant opportunities for team interaction made it so that our group became comfortable with each other and happy to help each other out when needed. I think that I can bring the skills I learned about teamwork from this course into my first job in a leadership position."

"As I recall my plans from the beginning of the beginning of the course, I had entered largely with a goal to obtain a specific set of skills and then be prepared to apply them to my own research. While I wouldn’t say that this goal has changed, I do believe that throughout the course a second overarching and unexpected goal was added. I found myself learning to “enjoy” the coding process, despite its frustrations, which is not something that I would have anticipated at the start of this course. With that said, I entered with one goal but finished the course happily with two."

"I learned how to effectively communicate with peers when working in data science and how to learn together as a group. I learned the general workflow of transcriptomic analysis and the necessary skills. Also, I learned how to efficiently record what code I have learned or worked on so that I could go back and review it. "

"At the beginning of the class, my main goals were to learn how to use the HPC and gain some more experience with using and analyzing experimental data. These goals were strengthened throughout the course, and the labs completed did teach me a lot about both of these objectives. However, just as much as I enjoyed learning comparative plant transcriptomics, I really grew to appreciate the collaborative nature of the class. Interdisciplinary work is very important to me and I wasn’t expecting this class to be as collaborative as it was, but throughout the duration of the semester I found my group members not only helped me learn the class content, but also offered me advice and support in my own conflicts over whether I will be able to attend graduate school, and I think that communication was just as important to me as learning the course content."

"I appreciate all the things we did. We learned drafting proposal, experimental design, review of drafts through graduate project, collaboration and communication through group work, habit of reading papers through social annotation. All of which are skills needed to be a researcher. I also got to see a good example of course design, instructors, collaboration, and patience."