Course Outline (F2023)
|Instructor(s)||Eric Harley [Coordinator]|
Office: VIC 741
Office Hours: Wednesdays, 12-2 pm
|Calendar Description||Introduction 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.
|Prerequisites||BLG 601 and CEN 199 and MTH 312|
- Bioinformatics I: Sequence Analysis and Phylogenetics, by Sepp Hochreitter, Lecture Notes, Institute of Bioinformatics, Johannes Kepler University Linz; online: http://www.master-bioinformatik.at/curriculum/BioInf_I_Notes.pdf
- Exploring Bioinformatics, A Project-Based Approach, Second Edition by Caroline St. Clair & Jonathan E. Visick Jones & Bartlett Learning 2015. Available as an ebook from https://campusstore.ryerson.ca/t-accesscodes.aspx
- 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:
- Develop further knowledge of science in support of application to engineering problems. (1a)
- 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)
- Evaluate sources of information, check the feasibility of design based on obtained results, and assess the reliability of conclusions. (2a)
- Develop further knowledge of uses of modern instrumentation, data collection techniques, and equipment to conduct experiments and obtain valid data. (5a)
- 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)
- Discuss the responsibility of the engineer to protect the public interest when working with genes and genetic data.
- 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).
3.0 hours of lecture per week for 13 weeks
0.0 hours of tutorial per week for 12 weeks
|Teaching Assistants||Irene Miah (email@example.com)|
Michael Nigro (firstname.lastname@example.org)
|Midterm Exam|| 30 %|
|Quizzes|| 5 %|
|Assignments|| 20 %|
|Final Exam|| 45 %|
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).
|Examinations||Midterm 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 Information||None|
|Teaching Methods||Mondays: 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.)
- Introduction to BME 501
- 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
- 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
(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 phylogeny.fr
- 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
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