Summer 2020 - MBB 243 D100

Data Analysis for Molecular Biology and Biochemistry (3)

Class Number: 2603

Delivery Method: In Person


  • Course Times + Location:

    May 11 – Jun 22, 2020: Tue, Thu, 8:30–10:20 a.m.

  • 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 and data analysis.
Lecture 2 Molecular sequences: features & composition.
Lecture 3 Gene splicing and GFF data format.
Lecture 4 Sequencing analysis using Biopython.
Lecture 5 Quantitative DNA analysis using Python conditional test.
Lecture 6 Searching for restriction sites in DNA sequences.
(Midterm exam)
Lecture 7 Searching for sequence features in protein sequences.
Lecture 8 Genetic code and DNA translation.
Lecture 9 Quantitative analysis of genes using R.
Lecture 10 Analyzing genomics big data using R data frame.
Lecture 11 Genome annotation using R data frame and R graphics.
Lecture 12 Genome analysis using Bioconductor.
(Final exam)


Lab 1Learning Python: printing and manipulating sequences.
Lab 2Reading and writing sequence files.
Lab 3Lists, loops, reading large sequence files.
Lab 4Writing our own functions for processing sequences.
Lab 5Quantitative DNA analysis using Python conditional test.
Lab 6Using regular expressions to search for sequence features in DNA sequences.
Lab 7Using regular expressions to search for sequence features in protein sequences.
Lab 8Translating DNA sequences using Python dictionaries.
Lab 9Learning R: molecular data analysis and presentation.
Lab 10Working with genome-scale sequences.
Lab 11Using R data frames and R graphics
Lab 12Technical review


  • 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 30% 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.
ISBN: 978-1492346135

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:

Registrar Notes:


SFU’s Academic Integrity web site 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.


Please note that all teaching at SFU in summer term 2020 will be conducted through remote methods. Enrollment in this course acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning ( or 778-782-3112) as soon as possible to ensure that they are eligible and that approved accommodations and services are implemented in a timely fashion.