Computational Biology Methods (COMP-462)

Computational Biology Methods and Research (COMP-561)

Mon/Wed 10:05am-11:25am,
Trottier 2120


3 credits (462) and 4 credits (561)



Jérôme Waldispühl and David Becerra



Trottier 3106 and 3140



McGill Centre for Bioinformatics



McGill University



Montreal, Quebec, Canada






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Telephone: 514-398-5018







Course Abstract:





Computational biology is the sub-discipline of Bioinformatics that is closest in spirit to pure computer science. The main efforts in this field are two-fold. 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 upper-year 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/data-structures, numerical computing and theoretical computer science courses.







308 - 251 Algorithms and data structures



189 - 323 Probability Theory







Important Note for undergraduate students: If a student does not have the prerequisities for this course, the Faculty of Science will delete this course from their record.





Office Hours:





JW: Mon/Wed 11:30-12:30; DB: Tue: Tue/Thu 11:00-13:00.


Book and Material:





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




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






Peter Clote and Rolf Backofen. Computational Molecular Biology: An introduction , Wiley, 2000




Not required. A good introductory book for anyone interested in the mathematical fondations of computational molecular biology.






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)


Class participation






Final Exam



Evaluation for COMP 561:




3 assignments (the first three)

30% total (10% each)


Class participation









Final Examrd



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





Concepts from biology and biotechnologies





Models of evolution



Sequence comparison






Gene expression and regulation



Peptide identification



RNA secondary structure



Protein structure



DNA sequencing



Population genetics



System biology


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



On-line Resources: Lecture notes by Dudoit and Gentleman


Lecture 3-6: Sequence evolution and sequence alignment.





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



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



Math/Algorithms: Dynamic Programming



Applications: Gene finding.



On-line Resources:



Additional material for COMP 561: Chapter 6 of Durbin and Eddy.


Lecture 7-8: Fast pairwise alignment methods and their statistics.





Topics: The Blast algorithm and its variations .



Background Reading: TBD



Math/Algorithms: Prob. theory; Combinatorics;



Applications: Genomic sequence alignment



On-line Resources:



Additional material for COMP 561: Original Blast paper


Lecture 9-12: Evolutionary models and phylogenetic Tree construction.





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



Background Reading: Chapters 7-8 of Durbin et al.



Math/Algorithms: Discrete algorithm design; Maximum likelihood.



On-line Resources:



Midterm exam, October 30th.







Lecture 14-16: Profile Hidden Markov Models.





Topics: Forward, backward, Viterbi, Baum-Welch algorithms.



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



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



Application: Gene finding



On-line Resources:


Lecture 17-18: 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.



On-line Resources:




Lecture 19-20: 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.



On-line Resources:


Lecture 21-22: Genetic algorithms.





Topics: Evolution-inspired optimization algorithms and their applications to computational biology methods.



Background Reading:






On-line Resources:


Lecture 23: RNA secondary structure prediction





Topics: Nussinov algorithm and Zuker algorithm



Background Reading: Durbin and Eddy, Chapter 8



Math/Algorithms: Dynamic programming algorithms



On-line Resources:



Additional material for COMP 561: Chapter 10 of Durbin and Eddy.


Lecture 24: Introduction to population genetics





Topics: Polymorphisms, haplotypes



Background Reading: TBD





Final exam (oral).




Statement on academic integrity

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 for more information).

Use of French in assignments and exams

In accord with McGill University’s Charter of Students’ Rights, students in this course have the right to submit in English or in French any written work that is to be graded.



Jérôme Waldispühl (based on Mike Hallett's and Mathieu Blanchette's syllabus for COMP 462)