TORONTO METROPOLITAN UNIVERSITY

Course Outline (W2025)

BME808: Computations in Genetic Engineering

Instructor(s)Adam Lim [Coordinator]
Office: TBA
Phone: TBA
Email: adam.lim@torontomu.ca
Office Hours: Wednesday | 11am - 12pm | Virtual
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 978-087969712-9Data Mining, I..H. Witten, E. Frank, M.A. Hall, Morgan Kaufmann, 2011.
  2. Reproducible Bioinformatics with Python, Ken Youens-Clark, Released July 2021, Publisher(s): O'Reilly Media, Inc. ISBN: 9781098100889
  3. Bioinformatics with Python Cookbook, 2nd Edition Paperback November 2018
Learning Objectives (Indicators)  

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

  1. Apply specialized engineering knowledge to predict functional regions in genetic data, such as exon-intron borders and promoter regions. (1d)
  2. Appraise the validity/reliability of bioinformatics sequence data relative to the degrees of error and limitations of sequence analysis theory and measurement. (3a)
  3. Apply selection/decision-making techniques to determine the relative value of feasible alternatives or proposed solutions in a complex sequence analysis problem. (4c)
  4. 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)
  5. 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
1.0 hours of lab per week for 12 weeks
1.0 hours of tutorial per week for 12 weeks

Teaching AssistantsDaniel Genkin (daniel.genkin@torontomu.ca)
 Mukhesh Reddicherla (mukhesh.reddicherla@torontomu.ca)
Course Evaluation
Theory
Midterm 25 %
Final 45 %
Laboratory
Research Project 10 %
Labs/Tutorials 20 %
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, 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).
Other Evaluation InformationLabs: From Week 2 onward.
 
 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.
Other InformationNone

Course Content

Week

Hours

Chapters /
Section

Topic, description

1

3

Exploring Bioinformatics Chapters 1 2

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
     
 
 


2

3

Exploring Bioinformatics: Chapter 8

DNA Sequencing:
     Deep sequencing of clinical samples
     Assembly and Mapping
     Algorithm for determining largest overlap
     Next generation sequencing
     Methods: Sanger Shotgun 454 Illumina Solid
     Introduction to the Greedy Algorithm
 
 
 


3

3

Exploring Bioinformatics: Chapters 3 5

Sequence Alignment:
     Fundamentals of sequence Alignment
     Scoring Alignments
     Substitution matrices and scoring
     Dynamic Programming Alignment algorithms
     Needleman-Wunsch Algorithm
     Sequence similarity databases
     Alignment score significance: probability
     Longest overlap algorithm using Python


4

3

Exploring Bioinformatics: Chapters 4 5

Sequence Alignment:
     Smith-Waterman Algorithm
     Dot-Matrix method
 Multiple sequence alignment:
     Global and local sequence alignments
     Word or k-tuple method
     ClustalW
     BLAST
 Introduction to protein sequence Alignment:


5

3

Exploring Bioinformatics: Chapters 9 D. Mount: Chapter 9

Gene Prediction Part-1:
     Structure of genes in Prokaryotes vs. Eukaryotes
     Consensus sequences in Prokaryotes vs. Eukaryotes
     Alignment-Based Algorithms
     Sequence-Based Algorithm
       Pattern Matching Algorithm using Python
     Content-Based Algorithm introduction
     Probabilistic Algorithm introduction
 Protein sequence Alignment:
     Sequence Alignment using Substitution matrices
     Hydrophobicity matrix
     PAM Matrix
     BLOSUM matrix
     


6

3

Exploring Bioinformatics: Chapters 10

Gene Prediction Part-2:
     Content-Based Algorithm
        CpG Island Prediction Algorithm using Python
     Probabilistic Algorithm
        Exon-intron boundaries
        Hidden Markov Model (HMM)
        Neural Networks introduction
         


7

0

Study week - No class
 
 Groups assigned for Final Project


8

2

Midterm Exam

Midterm-exam


9

3

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

Hidden Markov Models (HMM):
       Predicting Exon-Intron Boundary
       Setting up Viterbi matrices
       Hidden Markov Models  Evaluation problem
       Hidden Markov Models  Learning problem
       Hidden Markov Models  Decoding problem
 


10

3

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

Proteins:
     Primary, secondary and Tertiary Structures
     Protein databases
     Homology modeling
     Threading
     Chou-Fasman Algorithm
     Chou-Fasman: find alpha
     Chou-Fasman: find beta-strand
     Chou-Fasman: find beta-turn
 


11

3

Exploring Bioinformatics: Chapter 12 D. Mount: Chapter 8

Nucleic Acid Structure Prediction:
      Stem and loop structures
      Folded Structure
      Secondary Structure
      Nussinov-Jacobson Algorithm


12

3

Phylogenetics

Phylogenetics


13

3

Presentation

Students will present their course research project


Laboratory(L)/Tutorials(T)/Activity(A) Schedule

Week

L/T/A

Description

2

LAB 1: Exploring bioinformatics database on the internet

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

3

LAB 2: Assembly of sequence data

Students will familiarize themselves with Python and use it to write simple bioinformatics applications. Greedy Algorithm using Python

4

LAB 3: Sequencing

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. Needleman-Wunsch and Smith-Waterman

6

LAB 5: Primer Design

Students will utilize their learnings of Multiple Sequence Alignment to develop primer sequences for identifying SARS-CoV-2 virus variants

9

LAB 6: CpG Algorithm

Implementation of CpG approach to finding the promoter region.

10

LAB 7: Gene Annotation

Implementation of the Pattern algorithm which is good for gene annotation in prokaryotes.

11

LAB 8: RNA Secondary Structure

Explore a web application which deals with prediction of RNA secondary structure. Also write python code for generating complement and reverse complements of nucleic acid strands.

12

LAB 9: Chou Fasman Algorithm

Python implementation and testing of Chou-Fasman algorithm

13

LAB 10: Nussinov Jacobson Algorithm

Python implementation and testing of Nussinov Jacobson algorithm

University Policies & Important Information

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