Fall 2018 - MBB 243 D100
Data Analysis for Molecular Biology and Biochemistry (3)
Class Number: 6601
Delivery Method: In Person
Course Times + Location:
Sep 4 – Dec 3, 2018: Wed, 2:30–4:20 p.m.
Exam Times + Location:
Dec 12, 2018
Wed, 3:30–6:30 p.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.
Selected Topic: Data Analysis for Molecular Biology
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.
LecturesLecture 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.
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.
LabsLab 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 (iClicker pre-lecture quiz) 10%
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.
Department Undergraduate Notes:
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