tfcb_2020

MCB 536: Tools for Computational Biology

This document is the syllabus for this course.

Class schedule
Homework and grading
Course description
Resources and required materials
Instructors

Class schedule

Time: 1:00PM-3:00PM (1-2:40 is official class time, extra 20 minutes at end for additional questions and buffer in case of logistical issues), Tue & Thu, Sep 30 - Dec 11 2020

Location: Zoom

Lecture	Date	Instructor	Topic
1	Oct 1	Kate Hertweck	Introduction to course
2	Oct 6	Erick Matsen	Introduction to the command line
3	Oct 8	Erick Matsen	Intro to the command line (continued)
4	Oct 13	Trevor Bedford	Introduction to Git and GitHub
5	Oct 15	Trevor Bedford	Introduction to data
6	Oct 20	Phil Bradley	Introduction to Python
7	Oct 22	Phil Bradley	Intro to Python (continued)
8	Oct 27	Jesse Bloom	Data structures and biological analyses using Python
9	Oct 29	Jesse Bloom	Data structures/biological analyses in Python (continued)
10	Nov 3	Phil Bradley	Modeling and machine learning in Python
11	Nov 5	Phil Bradley	Modeling/machine learning in Python (continued)
12	Nov 10	Rasi Subramaniam	Visualize data using R/ggplot2
13	Nov 12	Rasi Subramaniam	Working with data using R/tidyverse
14	Nov 17	Rasi Subramaniam	Principles of data visualization
15	Nov 19	Gavin Ha	Introduction to sequencing data
16	Nov 24	Gavin Ha	Genomic data in R
17	Dec 1	Erick Matsen	Introduction to remote computing
18	Dec 3	Kate Hertweck	Remote computing on the command line
19	Dec 8	Rasi Subramaniam	Course summary and synthesis
20	Dec 10	Kate Hertweck	Capstone project

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 Will Hannon and Maggie Russell will be available to offer assistance just prior to and during the regular class session.

Homework and grading

A total of 8 homework assignments will be assigned on the following dates and will be due at 1pm on the dates indicated. If you need to submit a homework late, please check with the instructor at least 24 hours before the due date.
Grading criteria and instructions for submission are available in the Canvas site for this class.
You are encouraged to search online for solutions and discuss the homework with your classmates. However, the answers you submit should be written in your own words. You should also cite any online source or person that helped you arrive at your solution as inline comments in your code.
Each homework will count for 10% of your final grade. In-class participation will count for the remaining 20%, and will be assessed from the rubric presented here.
If you have a question about homework, please post it in the Slack workspace for this course (preferred) or message an instructor directly.

Homework	Assigned Date	Due Date	Topic
1	Oct 6	Oct 13	Unix command line
2	Oct 13	Oct 20	Reproducible science, Git and GitHub, Markdown
3	Oct 20	Oct 29	Programming in Python
4	Oct 27	Nov 3	Python analysis, lecture 9
5	Nov 5	Nov 12	Modeling and machine learning in Python
6	Nov 10	Nov 19	Data visualization and manipulation in R
7	Nov 19	Dec 3	Genomic data in R
8	Dec 3	Dec 16	Capstone

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:

Code in R, Python, and Unix/bash shell scripting using appropriate syntax and code convention
Select appropriate tools to perform specific programming and data analysis tasks
Apply good practices for computational research, including project organization and documentation
Analyze common forms of data generated by molecular biology experiments including high throughput sequencing, flow cytometry, and 96-well plate readers.

Resources and required materials

This course will require a laptop computer, on which you should install the required software.
Additional reading material is available for your reference.
If you are a UW student who does not possess a prior affiliation with Fred Hutch: We will request a HutchNetID for you, which will allow access to computational resources used for this class (please note that this process requires a background check).
Information about expectations for student conduct, disability resources, academic integrity, and religious accommodations can be found on this page.

Instructors

For general inquiries about this course, please contact khertwec at fredhutch.org