MCB 517A: Tools for Computational Biology

• Class Schedule
• Homeworks
• Grading
• Syllabus
• Useful References

Class Schedule

Time: 320PM-440PM, Tue & Thu, Sep 27 - Oct 30 2018

Location: B1-072, Fred Hutch

Lecture Date	Instructor
Sep 27	Jesse Bloom
Oct 2	Rasi Subramaniam
Oct 4	Rasi Subramaniam
Oct 9	Rasi Subramaniam
Oct 11	Trevor Bedford
Oct 16	Phil Bradley
Oct 18	Phil Bradley
Oct 23	Jesse Bloom
Oct 25	Trevor Bedford
Oct 30	Phil Bradley

Homeworks

A total of 4 homeworks will be assigned at 12pm on the following dates and will be due 1 week later at 12pm.

Assignment Date	Due Date
Oct 4	Oct 11
Oct 11	Oct 18
Oct 18	Oct 25
Oct 25	Nov 1

You are encouraged to search online for solutions, and discuss the homework with your classmates. You should however write the assignment answers yourself. You should also cite the online source or person that helped you arrive at your solution as inline comment in your code.

Homeworks should be submitted only as PDF files through https://canvas.uw.edu. Note that you can export both Rmarkdown documents and Jupyter notebooks to PDF. Check before submission that the PDF file looks as you expect it to be.

If you need to submit a homework late, please check with the instructor at least 24 hours before the due date.

Grading

Each homework will count for 20% of your final grade.

In-class participation will count for the remaining 20% of your final grade.

Syllabus

Introduction to Course and Working Environment

Date: Sep 27

Instructor: Jesse Bloom

• Overview of course
• Philosophy of computational biology
• Introduction to Markdown
• Install dependencies for R and Python if necessary
• Run test scripts inside Jupyter and RStudio
• Be able to ssh into rhino cluster

Reading, Transforming, and Visualizing Data using R

Date: Oct 2

Instructor: Rasi Subramaniam

• Read and write tsv files
• select, filter, arrange, mutate, summarize data
• Plot point and line graphs

Grouping and Joining Data using R

Date: Oct 4

Instructor: Rasi Subramaniam

• Introduction to group_by
• Joining dataframes
• What is Tidy Data?
• Homework 1 assigned by Rasi

Working with Genomic Data using R

Date: Oct 9

Instructor: Rasi Subramaniam

• Parsing genomic annotations
• Working with deep sequencing data
• Visualizing RNA-seq fold changes

Reproducible Research, Organizing Projects and the Command Line

Date: Oct 11

Instructor: Trevor Bedford

• Reproducibility and collaborative science
• File organization and naming
• Git and GitHub
• Homework 1 due
• Homework 2 assigned by Trevor

Command Line

Date: Oct 16

Instructor: Phil Bradley

• Intro to the command line
• more commands, eg: grep, find, cat, head
• PATH
• Compiling, test on rhino, “there exists software that needs to be compiled”
• Slurm and batch jobs
• Vim (mainly for Git commit messages) and/or "emacs -nw"

Introduction to Python

Date: Oct 18

Instructor: Phil Bradley

• Data types: integer, float, string
• Lists
• Variables and constants
• For, while loops
• Functions
• Homework 2 due
• Homework 3 assigned by Phil Bradley

Biological Analyses using Python

Date: Oct 23

Instructor: Jesse Bloom

• Dictionaries
• String analysis and regular expressions
• File I/O
• Biopython

Data Structures in Python

Date: Oct 25

Instructor: Trevor Bedford

• Matplotlib
• Numpy
• Iterators / list comprehension
• Classes
• Homework 3 due
• Homework 4 assigned by Jesse Bloom

Modeling and Machine Learning using Python

Date: Oct 25

Instructor: Phil Bradley

• Pandas?
• Scipy, clustering, linear modeling
• Scikit-learn
• Homework 4 due

Useful References

• R for Data Science by Hadley Wickham
• Python Data Science Handbook by Jake Vanderplas
• Stack Overflow website for troubleshooting problems