Course Outline (F2023)

BME501: Bioinformatics

Instructor(s)Eric Harley [Coordinator]
Office: VIC 741
Phone: TBA
Office Hours: Wednesdays, 12-2 pm
Calendar DescriptionIntroduction to analysis, management, and visualization of cellular information at the molecular level. The course includes an overview of mathematical modeling and simulation, pattern matching, methods for phylogenetics, gene recognition, distributed and parallel biological computing, designing and managing biological databases (both relational and object-oriented), linking disparate databases and data, data mining, reasoning by analogy, hypothesis formation and testing by machine.
PrerequisitesBLG 601 and CEN 199 and MTH 312
AntirequisitesCPS 501


Compulsory Text(s):
  1. Bioinformatics I: Sequence Analysis and Phylogenetics, by Sepp Hochreitter, Lecture Notes, Institute of Bioinformatics, Johannes Kepler University Linz; online:
Reference Text(s):
  1. Exploring Bioinformatics, A Project-Based Approach, Second Edition by Caroline St. Clair & Jonathan E. Visick Jones & Bartlett Learning 2015. Available as an ebook from
  2. Data Mining, Practical Machine Learning Tools and Techniques, Third Edition, I.H. Witten, E. Frank, M.A. Hall, Elsevier, Morgan Kaufmann Publishersl, 2011.
Learning Objectives (Indicators)  

At the end of this course, the successful student will be able to:

  1. Develop further knowledge of science in support of application to engineering problems. (1a)
  2. Apply mathematical principles, skills, and tools to solve engineering problems, highlighting limitations or a range of applications; use algorithms and available software to solve mathematical models. (1b)
  3. Evaluate sources of information, check the feasibility of design based on obtained results, and assess the reliability of conclusions. (2a)
  4. Develop further knowledge of uses of modern instrumentation, data collection techniques, and equipment to conduct experiments and obtain valid data. (5a)
  5. Apply statistical procedures, investigate possible artefacts, verify experimental results, consider possible extensions of results to other areas, interpret results with regards to given assumptions, and assess accuracy of results. (5b)
  6. Discuss the responsibility of the engineer to protect the public interest when working with genes and genetic data. (8b)
  7. Discuss ethical protocols and risks when collecting, analyzing and sharing genetic data or modifying genes. (10a)

NOTE:Numbers in parentheses refer to the graduate attributes required by the Canadian Engineering Accreditation Board (CEAB).

Course Organization

3.0 hours of lecture per week for 13 weeks
0.0 hours of tutorial per week for 12 weeks

Teaching AssistantsIrene Miah (
 Michael Nigro (
Course Evaluation
Midterm Exam 30 %
Quizzes 5 %
Assignments 20 %
Final Exam 45 %
TOTAL:100 %

Note: In order for a student to pass a course, a minimum overall course mark of 50% must be obtained. In addition, for courses that have both "Theory and Laboratory" components, the student must pass the Laboratory and Theory portions separately by achieving a minimum of 50% in the combined Laboratory components and 50% in the combined Theory components. Please refer to the "Course Evaluation" section above for details on the Theory and Laboratory components (if applicable).

ExaminationsMidterm exam in Week 8, two hours, multiple-choice, short-answer and programming, closed book (covers Weeks 1-5).
 Final exam, during exam period, three hours, closed-book, comprehensive, in a computer lab on a computer.
Other Evaluation InformationNone
Teaching MethodsMondays: in-person lecture, 11am-12pm, TRS2166
 Thursdays: in-person lecture, 8-10 am, TRS2166
 (Teaching methods: lectures with slides, problem-based learning using laptop to teach Python, implement algorithms and run data mining algorithms, internet to do database searches. Student presentations of project results.)
Other InformationNone

Course Content



Chapters /

Topic, description




  -  Introduction to BME 501
 NCBI databases
   - Parkinson's Disease primary databases and
     metadatabases genome-wide association studies
     Data mining (Ch 1, 2) -- class attribute instance
 Chapter 1 and 2 of Bioinformatics 1; Chapter 1 of Exploring Bioinformatics




Computational Manipulation of DNA
   - Introduction to Python genetic
   - screening for cystic fibrosis
   - computational algorithms string manipulation
   - Data mining (Ch 4.1) -- 0R 1R rules
 Chapter 2 of Exploring Bioinformatics




Sequence Alignment
   - Origin of new influenza virus strains optimal global and
     local alignments of DNA alignment parameters
     Needleman-Wunsch algorithm EMBOSS implementation
     two dimensional arrays dynamic programming
     Data Mining (Ch 4.2) -- Naive Bayes
 Chapter 3 of Bioinformatics 1; Chapter 3 of Exploring Bioinformatics



D2L notes

(Reading week for CPS501 cohort)
   - protect the public interest
   - privacy issues when collecting and analyzing data
   - risks and responsibiities when modifying genes
   - CRISPR-CAS9 potential




Database Searching and Multiple Sequence Alignment
   - searching sequence databases for matches (BLAST)
     multiple sequence alignment using ClustalW alignment
     algorithms and heuristics
   - overuse of agricultural antibiotics
   - antibiotic resistance
   - dynamic programming
   - Data mining (Ch 5) -- credibility accuracy
 Chapter 4 of Bioinformatics 1; Chapter 4 of Exploring Bioinformatics



Midterm (Thursday, Oct 26, 2h) (covering Weeks 1-5)
 Monday lecture: Data mining (Ch 4.3)  Decision trees




Substitution Matrices and Protein Alignments
   - scoring matrices for protein alignment
   - deriving substitution matrices nested hash tables.
 Chapter 3 of Bioinformatics 1; Chapter 5 of Exploring Bioinformatics




Distance Measurement in Molecular Phylogenetics
   - Evolutionary relationships
   - distance metrics (Jukes-Cantor, Kimura Tamura)
   - introduction to phylogenetic trees
   - Data Mining (Ch 4.8) -- clustering
 Chapter 5 of Bioinformatics 1; Chapter 6 of Exploring Bioinformatics




Tree-building in Molecular Phylogenetics
   - How to use distance measurements
   - agglomerative clustering
   - single linkage UPGMA neighbor joining
   - probabilistic methods in phylogenetics
 Chapter 5 of Bioinformatics 1; Chapter 7 of Exploring Bioinformatics




Sequence-Based Gene Prediction
   - Prediction of genes in a resistance plasmid
   - ORF finding and promoter prediction
   - NCBI ORF Finder NEBcutter EasyGene
   - pattern matching algorithms
 Chapter 9 of Exploring Bioinformatics

University Policies

Students are reminded that they are required to adhere to all relevant university policies found in their online course shell in D2L and/or on the Senate website

Important Resources Available at Toronto Metropolitan University


Academic Accommodation Support

Academic Accommodation Support (AAS) is the university's disability services office. AAS works directly with incoming and returning students looking for help with their academic accommodations. AAS works with any student who requires academic accommodation regardless of program or course load.

Academic Accommodations (for students with disabilities) and Academic Consideration (for students faced with extenuating circumstances that can include short-term health issues) are governed by two different university policies. Learn more about Academic Accommodations versus Academic Consideration and how to access each.

Wellbeing Support

At Toronto Metropolitan University, we recognize that things can come up throughout the term that may interfere with a student’s ability to succeed in their coursework. These circumstances are outside of one’s control and can have a serious impact on physical and mental well-being. Seeking help can be a challenge, especially in those times of crisis.

If you are experiencing a mental health crisis, please call 911 and go to the nearest hospital emergency room. You can also access these outside resources at anytime:

If non-crisis support is needed, you can access these campus resources:

We encourage all Toronto Metropolitan University community members to access available resources to ensure support is reachable. You can find more resources available through the Toronto Metropolitan University Mental Health and Wellbeing website.