MOLB 7910: Practical Computational Biology for Biologists — R/R Studio

Spring Semester

A computational biology class aimed at biology PhD students. Topics covered include basic practices for coding in R, analysis of standard high-throughput genomic data to study the regulation of gene expression, introduction to modeling gene expression, data visualization, and how to communicate computational analysis/results.


Classroom time will consist of a brief lecture on the theory and principles behind the day’s material. This will be followed by interactive exercises implementing these topics and concepts. There will be assigned exercises that students will complete in the form of an Rmarkdown document.

Goals and Learning Objectives

The goal of this course is to introduce students to basic concepts in computational biology using the R programming language. Students are not expected to have prior experience in R, however there will be a list of tasks that students are expected to complete before the first class. The goal of this class is to familiarize students with the concepts and tools necessary for data-analysis. At the completion of the course, students will be able to perform basic computational biology tasks that are crucial to the analysis of the large genomic datasets now present in biology.

The primary learning objectives are therefore to work towards being able to:

  1. Retrieve genomic data from public sources and import into R
  2. Prepare data for analysis (“tidy”-ing)
  3. Conduct basic exploratory analysis- getting familiar with your dataset
  4. Test specific hypotheses using computational methods
  5. Visualize and communicate your results
  6. Practice reproducible analysis


A personal laptop for the student is not required, but if one is available, it is highly encouraged that the coursework and materials be performed on that laptop to ensure the student has continuous access. Please reach out to us ASAP if you do not have access to a laptop.

Students will write R code within R Studio to complete assignments, which will include generating reports in R markdown. Although we do not require students to have prior R or other programming experience, students must work through “week 1” of the Coursera “R Programming” course. Students only need to watch the videos (no quizzes, reading, or assignments). You do not have to pay for the course, just make sure to select the “audit” option when registering. After completion, the student should have R, R Studio, and R markdown working on their laptop/computer. There will be a short pre-course assignment to make sure that all of these requirements are met.

Important: Configuring software will not be done once the classes have begun, so if you are not able to install these software reach out to instructors well BEFORE the 1st day of the class.

Examinations and Grading

Class participation and exercises will account for 60% of the student’s grade. The remaining 40% will be comprised of a take-home final exam that will require you to synthesize all skills and concepts covered during the course and apply them to a distinct biological problem. This exam will be completed in R markdown format and must be turned in to the instructors by 5 PM on the last day of class.

To complete the exercises and final exam, students are permitted to use any materials available them online or otherwise. Students must acquire at least 50% of the possible points in the class in order to pass.

Letter Grade Thresholds-

A+ 90-100%

A 75-89.9%

A- 60-74.9%

B+ 55-59.9%

B 50-54.9%


Sujatha Jagannathan-

Neelanjan Mukherjee-



Course Materials

MOLB 7910

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