Fall 2016 - MBB 420 D100

Selected Topics in Contemporary Biochemistry (3)

Data Management for MBB

Class Number: 6487

Delivery Method: In Person


  • Course Times + Location:

    Sep 6 – Dec 5, 2016: Wed, 12:30–2:20 p.m.

  • Exam Times + Location:

    Dec 7, 2016
    Wed, 12:00–3:00 p.m.

  • Prerequisites:

    Will be announced before the start of the term and will depend upon the nature of the topic offered.



The topics in this course will vary from term to term, depending on faculty availability and student interest.


Special Topic: Data Management/Analysis for Molecular Biology
The purpose of this introductory data analysis course is to teach students in molecular biology or any students who will use molecular data, basic knowledge of molecular biology data types, Linux operating system, data analysis methods including basic Python programming, and basic statistics skills using R.


  • Lecture 1 Introduction to molecular biology  
  • Lecture 2 Molecular Biology data types
  • Lecture 3 Linux operating systems: basic commands and text editor
  • Lecture 4 Printing and manipulating molecular sequence data  
  • Lecture 5 Representing molecular data types  
  • Lecture 6 Reading and writing molecular data files  
  • Lecture 7 Introduction to R for molecular biology  
  • Lecture 8 Lists and loops: working with large molecular data sets  
  • Lecture 9 Writing our own functions: working with complex molecular data  
  • Lecture 10 Conditional tests: analyzing molecular data  
  • Lecture 11 Regular expressions for discovering molecular features  
  • Lecture 12 Dictionaries: DNA translation  
  • Lecture 13 Analyze genome data using python and R  
  • Lab 1 Linux operating system: basic commands  
  • Lab 2 Linux operating system: text editor  
  • Lab 3 Molecular data: FASTA  
  • Lab 4 Python: string manipulation  
  • Lab 5 Python application: analyzing DNA sequences  
  • Lab 6 Python: working with files  
  • Lab 7 Python application: analyzing multiple DNA sequences  
  • Lab 8 Python: function  
  • Lab 9 Python: decision-making  
  • Lab 10 Python: regular expression  
  • Lab 11 Python application: pattern searching  
  • Lab 12 Working with complex data  
  • Lab 13 Statistics and graphics using R  



Students with credit for CMPT 102, 120, 125, 126, 128 or 130 may not take this course for further credit.


Prerequisites: MBB222 and MATH152 or MATH155



Python for Biologists.  Martin Jones.  2013. CreateSpace Independent Publishing Platform.
ISBN: 978-1492346135

Department Undergraduate Notes:

  • Students are advised to review the plagiarism tutorial found at
  • 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 Students with Disabilities (778-782-3112 or e-mail:  csdo@sfu.ca)

Registrar Notes:

SFU’s Academic Integrity web site http://students.sfu.ca/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html