


Computational
Biology Methods (COMP462) 2019 Computational
Biology Methods and Research(COMP561) 2019 Monday and Wednesday
2:304:00, on Zoom (see zoom link on MyCourses). 


3 credits
(462) and 4 credits (561) 


Mathieu Blanchette 


Trottier 3107 


McGill Centre for Bioinformatics 


McGill University 


Montreal, Quebec, Canada 





blanchem@cs.mcgill.ca http://www.cs.mcgill.ca/~blanchem/462 http://www.cs.mcgill.ca/~blanchem/561 


Telephone: 5143985209 





Course Abstract: 




Computational biology is the subdiscipline of Bioinformatics that is closest in spirit to pure computer science. The main efforts in this field are twofold. Firstly, we are concerned with creating models for problems from the biosciences (biology, biochemistry, medicine) that are both biologically and mathematically sound. Secondly, we are interested in the design and analysis of efficient, and accurate algorithms that solve these problems in practice and strategies for validation of results. 





This course is designed to introduce upperyear undergraduate students and graduate students to this area by examining several classic problems from the field. The intention of the course is to act as a gateway whereby, upon completion of the course, students will have the necessary biology, mathematics and computer science background to attend graduate level courses in bioinformatics geared towards specific topics (phylogenetics, genomic evolution, functional genomics, proteomics). The course is designed in such a manner that no previous formal training in biology is required of the students. 





The necessary mathematical background consists of the lower level discrete structures and probablity courses, since topics such as maximum likelihood estimation, hidden Markov models, and dynamic programming will be used repeatedly throughout the material. (Both maximum likelihood and hidden Markov models will be introduced at a basic level however.) Students will be required to have already taken the lower level algorithms/datastructures, numerical computing and theoretical computer science courses. 
Prequisities: 




COMP 251 Data Structures and Algorithms 


MATH 323 Probability Theory (or equivalent) 






Office Hours: 




M. Blanchette: Monday and Wednesday 4:005:00pm, via Zoom (same link as lectures) Dongjoon Lim Zichao Yan Yanlin Zhang 
Book and Material: 




Bernhard Haubold and Thomas Wiehe. Introduction
to Computational Biology: An Evolutionary Approach , Burkhauser Basel, 2007 


Probably the best introductory book
out there. Its level is ideal for the course, but it does not go much beyond
this. 





Durbin, Eddy, Krogh, Michinson, Biological
Sequence Analysis,
Cambridge, 1998. 





Also not required, this book is particularly good
for learning some of the basics of statistical inference/machine learning. 





Jones, N.C. and Pevzner, P. An Introduction to Bioinformatics Algorithms, MIT press, 2004 


You are not required to buy this book, however it is a good book for understanding some of the classic problems in computational biology and the algorithms used to solve these problems. 





Campbell, A. M., and Heyer, L.J. Discovering genomics, proteomics, and bioinformatics, Benjamin Cummings, 2002 


Also not required, this is a good primary for computer scientists that covers the basics of genomics, genetics, and proteomics. 





Alberts, Johnson, Lewis, Raff, Roberts, Walter Molecular Biology of the Cell, Garland, 2002 


This is a widely used and comprehensive book covering the biology of the cell. It is a good place to start when you want to explore a new topic. 
Evaluation for COMP 462: 



4 assignments 
40% total (10% each) 

Takehome Midterm, October 19, held remotely 
20% 

Take Home Final Exam, during exam period, held remotely 
40% 
Evaluation for COMP 561: 



3 assignments (the first three) 
30% total (10% each) 

Project 
20% 

Takehome Midterm, October 19, in class 
20% 

Takehome Final Exam, during exam period 
30% 
Computer Science/mathematics topics: 




Basic probability and statistics (ubiquitous) 


Dynamic programming (sequence alignment) 


Approximation algorithms (string alignment) 


Advanced data structures (suffix trees) 


Numerical techniques (least squares fits) 


Experimental design 


Programming 
Concepts from biology and biotechnologies 




Models of evolution 


Sequence comparison 


Phylogenetics 


Gene expression and regulation 


Peptide identification 


RNA secondary structure 


DNA sequencing 


Population genetics 
Course
outline 
Lecture 1,2: Introduction to molecular biology and genomics. 




Topics: Basic Questions, Basic Strategies, Introduction to molecular biology and genomics. 


Background Reading: Chapter 1 of Artificial Intelligence and Molecular Biology , by L. Hunter 


Online Resources: Lecture notes by Dudoit and Gentleman 
Lecture 37: Sequence evolution and sequence
alignment. 




Topics: Introduction to sequence evolution. Global and local alignment;
Gapping; BLAST algorithm; Multiple Alignments. 


Background Reading: Chapter 6 of Jones, Pevzner; Chapter 6.2 of Ewans,
Grant. 


Math/Algorithms: Dynamic Programming 


Applications: Gene finding. 


Online Resources: 


Additional material for COMP 561:
Chapter 6 of Durbin and Eddy. 
Lecture 811: Evolutionary models and phylogenetic
Tree construction. 




Topics: Discrete and continuous nucleotide and amino acid substitution models Distancebased
methods; Parsimony; Maximum Likelihood. 


Background Reading: Chapters 78 of Durbin et al. or Chapter 8 and 9.19.5 of Haubold and Wiehe. 


Math/Algorithms: Discrete algorithm design; Maximum likelihood. 


Online Resources: 
Midterm exam, October 19, in class (open book). 


Lecture 1316: Hidden Markov Models. 




Topics: Forward, backward, Viterbi, BaumWelch algorithms. Genefinding HMMs. Profile HMMs. 


Background Reading: Chapters 3 and 5 of Durbin et al. 


Math/Algorithms: Markov processes; Dynamic programming; Parameter estimation. 


Application: Gene finding 


Online Resources: 
Lecture 1718: Motif discovery. 




Topics: Modelling and searching for signals in DNA.. 


Background Reading: Chapter 5 of Ewans, Grant; Chapter 4 of Jones, Pevzner 


Math/Algorithms: Probability theory; Markov processes; exhaustive search; Gibb's sampling (intro) 


Applications: Searching for repeats. Identifying transcription factor binding sites. 


Online Resources: 
Lecture 1920: Gene Expression Analysis. 




Topics: Class distinction; Class prediction; Class discovery. 


Background Reading: Chapter 10 of Jones, Pevzner. 


Math/Algorithms: Differential expression; Principal Component Analysis; Clustering; Graph theory. 


Online Resources: 
Lecture 2122: Sequence assembly and Protein Identification. 




Topics: DNA sequencing; Sequence assembly problem; Peptide identification; Peptide sequencing; Protein Identification. 


Background Reading: Chapter 8.108.15 of Jones, Pevzner. 


Math/Algorithms: Graph theory; Dynamic programming.. 


Online Resources: 
Lecture 23,24: Introduction to population genetics 




Topics: Polymorphisms, haplotypes. Genotyping by sequencing. Phasing. 


Background Reading: TBD 


Math/Algorithms: 
Final exam, during the exam session. 



Whereas, McGill University values academic integrity; 
Whereas, every term, there are new students who register for the first time at McGill and who need to be informed about academic integrity; 
Whereas, it is beneficial to remind returning students about academic integrity; 
Be it resolved that instructors include the following statement on all course outlines: McGill University values academic integrity. Therefore all students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Code of Student Conduct and Disciplinary Procedures (see www.mcgill.ca/integrity for more information). 
Be it further resolved that failure by an instructor to include a statement about academic integrity on a course outline shall not constitute an excuse by a student for violating the Code of Student Conduct and Disciplinary Procedures. 
20200902
