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:

• Introduction to Bioinformatics (and RStudio)
• Creating Reproducible Research
• Manipulating Data (Data Wrangling)
• Bioinformatics with R
• Final Project

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

Digital Credential

Earners of the Bioinformatics with R BioHacker credential have successfully demonstrated experiential skills in computational biology with R, genome data mining, structural bioinformatics, and annotation and characterization of genomic regions. The Bioinformatics with R badge requires 50+ hours of hands-on activities and labs across 15+ skills in biotech R&D. The Bioinformatics with R BioHacker credential was built in collaboration with popular YouTuber and Professor Josh Vandenbrink, currently at Louisiana Tech University.

Following is summary of the earning criteria for the BioHackers: Bioinformatics with R digital credential:

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Complete 15+ hands-on bioinformatics labs using live biological computing systems

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AND – Complete all required learning resources in the Bioinformatics with R online journey – 13 lessons – including, videos, articles, activities, and discussion posts

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AND – Pass short assessments (80% or better) in all lessons

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AND – Participate in weekly virtual collaboration sessions with instructor(s), mentor(s), and peers

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AND – Conduct original bioinformatics research using the skills, resources, and tools within the Bioinformatics with R journey