Biology is becoming a data science where high-throughput methodologies produce vast amounts of heterogeneous data that require analysis, mining, synthesis and effective delivery of such results (e.g., papers, presentations, reports). This course is a workshop designed to introduce graduate students in biology and biotechnology to practical applications of bioinformatics. As any workshop, this class consists of hands-on and practical training experiences, and it is not a regular course where the instructor feeds you with pieces of information. For this purpose, we will use R as our standard coding language, but we will introduce Python at later stage of this class. During this workshop, students will follow recipes and worked examples in a dedicate resource (an online book designed specifically for this class).
You can access this here BIO/BIT 209.
This online book aims to illustrate how you can implement bioinformatic code to perform data manipulation, exploration, mining, statistics and graphics. However, this is not a fixed document, and I will add more units or change those as you explore your datasets and request new tools or recipes. Also, you might suggest adding a particular set of tools to help you in your course-long project. Therefore, this workshop is designed for active participation from the students with their computers following our code recipes under the instructor’s guidance. To achieve this, I will show you an example of how the code is implemented and you (as a student) will emulate such code on your computer. Next, you will try to come up with your own example, and you can expand your exploration by creating applications of the code learned up to that point. You are encouraged to challenge yourself on more complicated exercises; I will give you hints on how to further develop your practice.