tfcb_2021

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: 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

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.
Submit homework solutions as Markdown text files, scripts, or PDF as appropriate for each homework through Canvas.
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 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:

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 rasi at fredhutch.org