Course Outline (F2019)
|Instructor(s)||Eric Harley [Coordinator]|
Office: ENG 287B
Office Hours: Thursdays 2-4 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 600 or BLG601) and MTH312|
- “Exploring Bioinformatics, A Project-Based Approach”, Second Edition by Caroline St. Clair & Jonathan E. Visick Jones & Bartlett Learning 2015.
- 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 lab/tutorial per week for 12 weeks
|Midterm Exam|| 25 %|
|Quizzes|| 5 %|
|Assignments|| 25 %|
|Final Exam|| 45 %|
Note: In order for a student to pass a course with "Theory and Laboratory" components, in addition to earning a minimum overall course mark of 50%, 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 for details on the Theory and Laboratory components.
|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|
- Introduction to BME 501
- Parkinson's Disease primary databases and
metadatabases genome-wide association studies
Data mining (Ch 1 2) -- class attribute instance
Computational Manipulation of DNA
- Introduction to Python genetic
- screening for cystic fibrosis
- computational algorithms string manipulation
- Data mining (Ch 4.1) -- 0R 1R rules
- 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
Database Searching and Multiple 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
- protect the public interest
- privacy issues when collecting and analyzing data
- risks and responsibiities when modifying genes
- CRISPR-CAS9 potential
Midterm (Wednesday Oct 23 2h)
Data mining (Ch 4.3) – Decision tree
Substitution Matrices and Protein Alignments
- scoring matrices for protein alignment
- deriving substitution matrices nested hash tables.
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
Tree-building in Molecular Phylogenetics
- How to use distance measurements
- agglomerative clustering
- single linkage UPGMA neighbor joining
- probabilistic methods in phylogenetics
Sequence-Based Gene Prediction
- Prediction of genes in a resistance plasmid
- ORF finding and promoter prediction
- NCBI ORF Finder NEBcutter EasyGene
- pattern matching algorithms
Policies & Important Information:
- Students are required to obtain and maintain a Ryerson e-mail account for timely communications between the
instructor and the students;
- Any changes in the course outline, test dates, marking or evaluation will be discussed in class prior to being implemented;
- Assignments, projects, reports and other deadline-bound course assessment components handed in past the due date will receive a mark of ZERO, unless otherwise stated. Marking information will be made available at the time when such course assessment components are announced.
- Refer to our Departmental FAQ page for information on common questions and issues at the following link: https://www.ee.ryerson.ca/guides/Student.Academic.FAQ.html.
Missed Classes and/or Evaluations
When possible, students are required to inform their instructors of any situation which arises during the semester which may have an adverse effect upon their academic performance, and must request any consideration and accommodation according to the relevant policies as far in advance as possible. Failure to do so may jeopardize any academic appeals.
- Health certificates - If a student misses the deadline for submitting an assignment, or the date of an exam or other evaluation component for health reasons, they should notify their instructor as soon as possible, and submit a Ryerson Student Health Certificate AND an Academic Consideration Request form within 3 working days of the missed date. Both documents are available at https://www.ryerson.ca/senate/forms/medical.pdf.. If you are a full-time or part-time degree student, then you submit your forms to your own program department or school;
- Religious, Aboriginal and Spiritual observance - If a student needs accommodation because of religious, Aboriginal or spiritual observance, they must submit a Request for Accommodation of Student Religious, Aboriginal and Spiritual Observance AND an Academic Consideration Request form within the first 2 weeks of the class or, for a final examination, within 2 weeks of the posting of the examination schedule. If the requested absence occurs within the first 2 weeks of classes, or the dates are not known well in advance as they are linked to other conditions, these forms should be submitted with as much lead time as possible in advance of the absence. Both documents are available at www.ryerson.ca/senate/forms/relobservforminstr.pdf. If you are a full-time or part-time degree student, then you submit the forms to your own program department or school;
- Academic Accommodation Support - Before the first graded work is due, students registered with the Academic Accommodation Support office (AAS - www.ryerson.ca/studentlearningsupport/academic-accommodation-support) should provide their instructors with an Academic Accommodation letter that describes their academic accommodation plan.
Ryerson's Policy 60 (the Academic Integrity policy) applies to all students at the University. Forms of academic misconduct include plagiarism, cheating, supplying false information to the University, and other acts. The most common form of academic misconduct is plagiarism - a serious academic offence, with potentially severe penalties and other consequences. It is expected, therefore, that all examinations and work submitted for evaluation and course credit will be the product of each student's individual effort (or an authorized group of students). Submitting the same work for credit to more than one course, without instructor approval, can also be considered a form of plagiarism.
Suspicions of academic misconduct may be referred to the Academic Integrity Office (AIO). Students who are found to have committed academic misconduct will have a Disciplinary Notation (DN) placed on their academic record (not on their transcript) and will normally be assigned one or more of the following penalties:
- A grade reduction for the work, ranging up to an including a zero on the work (minimum penalty for graduate work is a zero on the work);
- A grade reduction in the course greater than a zero on the work. (Note that this penalty can only be applied to course components worth 10% or less, and any additional penalty cannot exceed 10% of the final course grade. Students must be given prior notice that such a penalty will be assigned (e.g. in the course outline or on the assignment handout);
- An F in the course;
- More serious penalties up to and including expulsion from the University.
The unauthorized use of intellectual property of others, including your professor, for distribution, sale, or profit is expressly prohibited, in accordance with Policy 60 (Sections 2.8 and 2.10). Intellectual property includes, but is not limited to:
- Lecture notes
- Presentation materials used in and outside of class
- Lab manuals
- Course packs
For more detailed information on these issues, please refer to the Academic Integrity policy(https://www.ryerson.ca/senate/policies/pol60.pdf) and to the Academic Integrity Office website (https://www.ryerson.ca/academicintegrity/).
Important Resources Available at Ryerson
- The Library (https://library.ryerson.ca/) provides research workshops and individual assistance. Inquire at the Reference Desk on the second floor of the library, or go to library.ryerson.ca/guides/workshops
- Student Learning Support(https://www.ryerson.ca/studentlearningsupport) offers group-based and individual help with writing, math, study skills and transition support, and other issues.