Fall 2022 - MBB 243 D100
Data Analysis for Molecular Biology and Biochemistry (3)
Class Number: 2319
Delivery Method: In Person
Course Times + Location:
We 12:30 PM – 2:20 PM
AQ 5016, Burnaby
1 778 782-7140
Prerequisites:MBB 222 and MATH 152 or MATH 155. STAT 201 (or an equivalent statistics course) or STAT 270 is recommended.
Introductory data analysis focusing on molecular biology data sets and examples and including basic programming skills using Python and basic statistics skills using R.
The purpose of this introductory data analysis course is to teach students in molecular biology or any students who will analyze molecular data, basic knowledge of molecular biology data types, data analysis methods including basic programming skills using Python, and basic statistics skills using R.
Lecture 1 Molecular biology data: flavours and common file formats
Lecture 2 Molecular biology data: genomes and annotation
Lecture 3 Fundamentals of R and Python
Lecture 4 Getting/parsing/manipulating sequence data using shell, R and Python
Lecture 5 Regular expressions and patterns
Lecture 6 Quantitative DNA/RNA sequence analysis
Lecture 7 Genome-scale data analysis
Lecture 8 Plotting with ggplot2
Lecture 9 Techniques for tidying, merging and manipulating tabular data
Lecture 10 Basic modeling
Lecture 11 Visualization of genomic annotations
Lecture 12 Advanced data visualization methods
Lab 1 Shell competency 1: manipulating and searching plain text formats
Lab 2 Shell competency 2: installing, managing and running command-line utilities
Lab 3 Basic data types and operations in Python and R
Lab 4 Conditionals and control flow in Python and R
Lab 5 Application of regular expressions in Python and R
Lab 6 Quantitative analysis of DNA and protein sequences
Lab 7 Tidy data, data frames and data tables
Lab 8 Plotting genomic and other flavours of data
Lab 9 Cleaning and combining data sets
Lab 10 Creating and visualizing models
Lab 11 Fundamentals of Bioconductor
Lab 12 Selected topics
- In class lab tasks: In each lab, there is a list of tasks that should be accomplished in class. Results are submitted by the end of each lab. 20%
- Lab assignments: Short assignments will be handed out in lab sessions and will be due at the start of your lab one week later, unless indicated otherwise. There is a 10% per day late penalty for assignments received after the due date time. 35%
- Midterm and final exams - a mixture of multiple choice, short answer and written questions. (10% for midterm exam and 25% for final exam) 35%
- Participation 10%
Students with credit for CMPT 102, 120, 125, 126, 128 or 130 may not take this course for further credit.
Python for Biologists. Martin Jones. 2013. CreateSpace Independent Publishing Platform.
REQUIRED READING NOTES:
Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.
Department Undergraduate Notes:
- For help with writing, learning and study strategies please contact the Student Learning Commons at
- Students requiring accommodations as a result of a disability, must contact the Centre for Accessible Learning (778-782-3112 or e-mail: email@example.com)
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
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