Bioinformatics

with R

Course Details

Students will explore how R impacts biosciences and genomics. Students will also be introduced to the basics of the R/RStudio environment. In addition, they will become familiar with computational biology techniques such as RNA expression analysis, phylogenetic analysis and sequence alignment. Finally, students will learn techniques for reproducible research, which will allow them to create “reports” of the work they have conducted throughout this class. Qualifies for the BioHacker: Bioinformatics with R digital badge.

Journey Architecture

The online program is available 24x7x365 via any web browser or mobile device and includes five (5) learning paths and 50+ video lectures. Below is a list of the main topics:

Computational Biology is a valuable tool in discovering the fundamental processes of biological systems. This course will help students build a foundation in computational biology programming leveraging the R computer language. Students will start from the ground-up, learning to navigate the RStudio environment, load in data, and characterize and define variables within the environment. In addition, creating reproducible research is a key element to success in computational biology. Coding with R can include thousands of lines of code for complex analysis, and proper annotation is required for an understanding of how the code works both to outsiders, and the coder themselves. Thus, we leverage the power of RMarkdown to create functional and visually appealing reproducible research products of computational biology analyses.

After learning the basics of R, RStudio and RMarkdown, students will use the knowledge gained to begin computational biology analysis. This process starts with learning the ability to manipulate or formulate data in a way that R understands, also known as Data Wrangling. Many times data that is publicly available is not in the correct format to be analyzed in R. Thus, tools exist (such as the tidyverse package) which allow us to manipulate and clean up the data, creating a format that is compatible with the analysis. Once formatted, the students will proceed to learn computational biology analyses. These include RNA expression analysis, annotation and characterization of genomic regions, phylogenetic analysis, among others.

Completion of this course will set a foundation for success in computational biology, and build student confidence in using R for data analysis.

Skills and Resources

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