MCB 536: Tools for Computational Biology

This document is the syllabus for this course.

Class schedule

Time: 3:20PM-4:40PM, Tue & Thu, Sep 30 - Dec 9 2021

Location: Zoom (see Slack)

TA Office hours: 1:00PM-2:00PM, Mon & Wed, Oct 4 - Dec 15 2021

Location: Zoom (see Slack)

Lecture Date Instructor Topic
1 Sep 30 Rasi Subramaniam Introduction to course, markdown, git
2 Oct 5 Trevor Bedford Introduction to data
3 Oct 7 Erick Matsen Introduction to the command line
4 Oct 12 Erick Matsen Intro to the command line (continued)
5 Oct 14 Erick Matsen Introduction to remote computing
6 Oct 19 Phil Bradley Introduction to Python
7 Oct 21 Phil Bradley Intro to Python (continued)
8 Oct 26 Jesse Bloom Data structures and biological analyses using Python
9 Oct 28 Jesse Bloom Data structures/biological analyses in Python (continued)
10 Nov 2 Phil Bradley Modeling and machine learning in Python
11 Nov 4 Phil Bradley Modeling/machine learning in Python (continued)
12 Nov 9 Rasi Subramaniam Data analysis using R/tidyverse
13 Nov 16 Rasi Subramaniam Data analysis using R/tidyverse (continued)
14 Nov 18 Rasi Subramaniam Biological sequences and annotations in Bioconductor
15 Nov 23 Gavin Ha Introduction to sequencing data
16 Nov 30 Gavin Ha Genomic data in R
17 Dec 2 Manu Setty Introduction to RNA-seq
18 Dec 7 Manu Setty Single-cell RNA-seq analysis
19 Dec 9 Manu Setty Single-cell RNA-seq analysis (continued)

Materials for each lecture will be available in this repository prior to the class session; the link for each topic will take you to the folder containing materials for that class. Please note that materials are considered in draft form until the beginning of the class session in which they will be presented (or if otherwise indicated).

For further assistance, TAs Timothy Yu and Ty Bottorff will be available to offer assistance just prior to and during the regular class session.

Homework and grading

Homework Assigned Date Due Date Topic
1 Oct 7 Oct 14 Reproducible science, Git and GitHub, Markdown
2 Oct 14 Oct 21 Unix command line
3 Oct 21 Oct 28 Programming in Python
4 Oct 28 Nov 4 Python analysis, lecture 9
5 Nov 4 Nov 16 Modeling and machine learning in Python
6 Nov 16 Nov 23 Data visualization and manipulation in R
7 Nov 23 Dec 7 Genomic data in R
8 Dec 7 Dec 14 Single-cell RNA-seq analysis

Course description

This course is designed to introduce computational research methods to graduate students in biomedical science and related disciplines. We expect students will have little to no previous experience in computational methods. This course provides a survey of the most common tools in the field and you should not expect that completion of the course will make you an expert in any single programming language. Rather, you should be equipped with foundational knowledge in reproducible computational science, and can continue learning relevant tools to suit your research interests.

Course objectives: By the end of the course, students should be able to:

Resources and required materials


For general inquiries about this course, please contact rasi at

Teaching Assistants