RYERSON UNIVERSITY

Course Outline (W2020)

BME808: Computations in Genetic Engineering

Instructor(s)Prathap Siddavaatam [Coordinator]
Office: EPH433
Phone: TBA
Email: prathap.siddavaatam@ryerson.ca
Office Hours: Friday 5-7 PM
Calendar DescriptionDiscusses the theory and practice of molecular database searching and sequence alignment in genetic engineering. Covers databases and Internet access, sequence homology searching, and multiple alignment and sequence motif analysis, and protein structure and function.
PrerequisitesBME 501 and BME 532 and MTH 410
Antirequisites

None

Corerequisites

None

Compulsory Text(s):
  1. “Exploring Bioinformatics, A Project-Based Approach”, Second Edition by Caroline St. Clair & Jonathan E. Visick Jones & Bartlett Learning 2015.
Reference Text(s):
  1. “Sequence and Genome Analysis”, D.W. Mount, Cold Spring Harbor Laboratory Press, 2004, ISBN 978-087969712-9
    “Data Mining”, I..H. Witten, E. Frank, M.A. Hall, Morgan Kaufmann, 2011.
Learning Objectives (Indicators)  

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

  1. Appraise the validity/reliability of bioinformatics sequence data relative to the degrees of error and limitations of sequence analysis theory and measurement. (3a)
  2. Apply selection/decision-making techniques to determine the relative value of feasible alternatives or proposed solutions in a complex sequence analysis problem. (4c)
  3. Design and develop simple software to perform given tasks as required by the problem, evaluate skills and tools to identify their limitations with respect to the project needs, and evaluate results using several skills and tools to determine the one that best explains ‘reality’. (5a)
  4. Gain a working knowledge of the literature of sequence analysis in the field of bioinformatics and how sequences are produced, annotated and analyzed. (12b)

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
2.0 hours of lab/tutorial per week for 12 weeks

Teaching AssistantsTBA
Course Evaluation
Theory
Midterm 30 %
Final 45 %
Laboratory
Research Project 15 %
Labs 10 %
TOTAL:100 %

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.


ExaminationsMidterm exam in Week 8, 2.0 hours, closed book (covers Weeks 1-6 of lecture,
 assignment and laboratory material).
 Final exam, during exam period, 3.0 hours, closed book (covers all the course
 material).
 Exams will be done in a computer lab, partly on computer and partly on paper.
Other Evaluation InformationLabs: Weekly, excluding the first week.
 
 Participation: Based on in-class exercises and in-class presentations of recent advances in biotechnology.
 
 Research Project: Review and presentation of a scientific paper.
 The research project combines two separate components: a written component
 and an oral presentation component. The objective of this project is to study a
 specific topic in bioinformatics literature and to become familiar with the research community and history of bioinformatics. You must select a publication
 that presents either a specialized bioinformatics algorithm or its application.
 A 12 minute presentation and a two page technical report will be used to
 evaluate your project, as well as the technical merit and the skill with which the
 student communicates his or her message.
 Papers in (peer-reviewed) journals and conference proceedings are the main
 resources for this project. The last Lab/Tutorial sessions will be dedicated
 to the presentation of student projects.
 
Other InformationNone

Course Content

Week

Hours

Chapters /
Section

Topic, description

1

3

Exploring Bioinformatics Chapters 1 2 D. Mount: Chapter 12

Introduction to Bioinformatics and Computational Genomics:
     Structure of nucleic acids DNA RNA
     Role of mRNA tRNA and ribosome
     Gene transcription translation protein genetic code
     Bioinformatics Databases structure type data quality
     Introduction to Python (review of string analysis)
 
 Data mining:
     Review of terminology 0R 1R models evaluation


2

3

Exploring Bioinformatics: Chapter 8

DNASequencing:
     Deep sequencing of clinical samples
     Assembly and Mapping
     Algorithm for determining largest overlap
     Next generation sequencing
     Methods: Sanger Shotgun 454 Illumina Solid
 
 Data Mining:
     Review of Naive Bayes model


3

3

Exploring Bioinformatics: Chapters 3 5 D Mount: Chapter 3 4

Sequence Alignment:
     Fundamentals of sequence Alignment
     Scoring Alignments
     Substitution matrices and scoring
     Dynamic Programming Alignment algorithms
     Sequence similarity databases
     Alignment score significance: probability
     Data mining: Decision trees


4

3

Exploring Bioinformatics: Chapters 4 5 D. Mount: Chapter 5

Multiple sequence alignment:
     Global and local sequence alignments
     Profile alignments
     Hidden Markov Models (Forward and Viterbi algs.)
 
 Database Homology Searching:
     Data mining: clustering methods


5

3

Exploring Bioinformatics: Chapters 9 D. Mount: Chapter 7

Gene Prediction:
     Sequence-Based


6

3

Exploring Bioinformatics: Chapters 10 D. Mount: Chapter 7

Gene Prediction:
     Advanced


7

3

CRISPR/Cas system:
     Mechanism
     Applications


9

4

Exploring Bioinformatics: Chapter 11 D. Mount: Chapter 10 (pp 417-434)

Proteins:
     Primary secondary and Tertiary Structures
     Ramachandran plot
     Classes of protein structure
     Protein databases
     Motifs folds domains
     Accessing files of sequences from databases


10

3

Exploring Bioinformatics: Chapter 11 D. Mount: Chapter 10 (pp 435-467)

Protein Homology modeling:
     Secondary structure prediction
     Ab initio modeling homology modeling
     Tertiary structure prediction


11

3

Exploring Bioinformatics: Chapter 12 D. Mount: Chapter 8

Nucleic Acid Structure Prediction


12

3

D. Mount: Chapter 13

Microarrays:
     Introduction
     Analysis


13

3

Data Mining and Machine Learning:
     Perceptron
     Neural Nets
     Significance
     Applications in Bioinformatics


Laboratory/Tutorials/Activity Schedule

Week

Lab

Description

2

LAB 1: Exploring bioinformatics database on the internet

Students will be familiarized with key features of the bioinformatics databases.

3

LAB 2: Python tutorial

Students will familiarize themselves with this scripting language and use it to write simple bioinformatics applications.

4

LAB 3: Sequencing DNA

Gaining experience with DNA sequencing data and software that analyzes it.  Example: the human gut metagenome in NCBI trace archives.

5

LAB 4: Dynamic programming algorithm Pairwise Sequence Alignment

Students will implement the dynamic programming algorithm and gain a better understanding of pairwise sequence alignment.

6

LAB 5: Assembly of DNA sequence data

Writing a simulator to generate synthetic DNA sequencing data (fragments)

7

LAB 6: Data Mining

Students practice with Weka Data Mining software

8

LAB 7: Multiple sequence alignment

Practice with online software (CLUSTAL) and with Hidden Markov Models on paper.

9

LAB 8: Gene annotation

Implementation of CpG approach to finding the promoter region.

10

LAB 9: RNA Secondary Structure

Using online software to predict RNA structure.

11

LAB 10: Classification of proteins

Students will experiment with support vector machines and attribute selection to classify protein according to structure (all alpha all beta or mixed).

12

LAB 11: Predicting protein secondary structure

Implementation and testing of Chou-Fasman alg.

13

Research project: Review and presentation of a scientific paper

Students will learn to present and research on papers from (peer-reviewed) journals and conference proceedings for communicating scientific information.

Policies & Important Information:

  1. Students are required to obtain and maintain a Ryerson e-mail account for timely communications between the instructor and the students;
  2. Any changes in the course outline, test dates, marking or evaluation will be discussed in class prior to being implemented;
  3. 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.
  4. 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.

  1. 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;
  2. 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;
  3. 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.

Academic Integrity

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:

  1. 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);
  2. 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);
  3. An F in the course;
  4. 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:

  1. Slides
  2. Lecture notes
  3. Presentation materials used in and outside of class
  4. Lab manuals
  5. Course packs
  6. Exams

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

  1. 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
  2. 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.