CSI 5126 (COMP 5108). Algorithms in Bioinformatics
Marcel Turcotte
University of Ottawa
School of Electrical Engineering and Computer Science (EECS)
[email protected]
Fall 2018
[ PDF ]
Course Hours
The first lecture will be held on September 6, 2018. Information about the schedules, lecture notes, and more can
be found on the course Web page.
Professor
Calendar Description
Fundamental mathematical and algorithmic concepts underlying computational molecular biology; physical and
genetic mapping, sequence analysis (including alignment and probabilistic models), genomic rearrangements,
phylogenetic inference, computational proteomics and systemic modeling of the whole cell. Prerequisites: CSI
3105, COMP 3804 or equivalent.
Course Learning Outcomes
Upon completion of the course, student will be able to:
- List and describe the fundamental algorithms in bioinformatics
- Articulate the trade-offs behind algorithms in bioinformatics
- Write computer programs for solving large scale bioinformatics problems
- Critically review scientific publications in this field
- Locate and critically evaluate scientific information
- Apply one of the paradigms presented in class to solve real-world problems
- Present scientific content to a small technical audience
Course Calendar
Here is a draft of the week by week content.
- Syllabus and introduction, course objectives and students expectations.
- Essential cell biology.
Wiesława Widłak (2013). Molecular Biology: Not Only for Bioinformaticians (Vol. 8248). Springer.
Chapters 1, 2, and 3.
- Essential Cell Biology.
Wiesława Widłak (2013). Molecular Biology: Not Only for Bioinformaticians (Vol. 8248). Springer.
Chapters 4, 5, and 9.
- Suffix Trees.
Bernhard Haubold and Thomas Wiehe (2006). Introduction to computational biology: an
evolutionary approach. Birkhäuser Basel. Pages 43-53.
Dan Gusfield (1997) Algorithms on strings, trees, and sequences : computer science and computational
biology. Cambridge University Press. Chapters 5, 6 (optional), 7.
- Suffix Trees.
Bernhard Haubold and Thomas Wiehe (2006). Introduction to computational biology: an
evolutionary approach. Birkhäuser Basel. Pages 43-53.
Dan Gusfield (1997) Algorithms on strings, trees, and sequences : computer science and computational
biology. Cambridge University Press. Chapters 8 (optional), 9.
- Molecular Sequence Alignment.
Bernhard Haubold and Thomas Wiehe (2006). Introduction to computational biology: an
evolutionary approach. Birkhäuser Basel. Pages 11-15, 30-33.
- Molecular Sequence Alignment.
Bernhard Haubold and Thomas Wiehe (2006). Introduction to computational biology: an
evolutionary approach. Birkhäuser Basel. Pages 35-39.
- Substition Matrices.
Warren J. Ewens, Gregory R. Grant (2001) Statistical Methods in Bioinformatics: An Introduction.
Springer. Pages: 238-249.
- Inferring Phylogenies.
Bernhard Haubold and Thomas Wiehe (2006). Introduction to computational biology: an
evolutionary approach. Birkhäuser Basel. Pages 143-168.
- Inferring Phylogenies.
Bernhard Haubold and Thomas Wiehe (2006). Introduction to computational biology: an
evolutionary approach. Birkhäuser Basel. Pages 143-168.
- RNA Secondary Structure. Dynamic programming.
- RNA Secondary Structure. Cocke-Younger-Kasami.
- Sequence Motif (Median).
- Sequence Motif (Hidden Markov Model).
Bernhard Haubold and Thomas Wiehe (2006). Introduction to computational biology: an
evolutionary approach. Birkhäuser Basel. Pages 101-116.
- MotifGP.
- ModuleInducer.
Evaluations
- Assignments (2) (20%)
- Midterm (20%)
- Review and oral presentation of a scientific publication (10%)
- Project (50%)
- Outline (10%)
- Presentation (10%)
- Report (30%)
Oral Presentations
Papers in (refereed) journals and conference proceedings are the main vehicules for communicating scientific
information. You must select a publication that presents either a specialized application or a more efficient
algorithm on a topic that has been presented in class.
Objectives
- Thoroughly study of a specific topic in bioinformatics
- Familiarity with the modes of communicating research
- Develop your presentation skills
Deliverable
- One or two pages summary of the publication
- 15–20 minutes presentation
Project
Objectives
- Thorough study of a specific bioinformatics topic
- Learning to study autonomously
- Develop your presentation skills
Deliverable
The format of the projects is quite flexible. I foresee three broad types of work: the development of a novel
application, the analysis of a new data set, or a (thorough) review of the literature on a specific topic. For all three
types of work, I would like to see a review of the literature, sample data and a prototype implementation (where
applicable). The main difference between each type of work will be the relative importance of each of the
components.
Teamwork
Teams will be made of 1 or 2 members. Larger teams will have to produce proportionally more work!
Complementary work between teams is also welcomed, i.e. two or more teams working on a related but
complementary topic, leading to a more realistic application.
Report
Reports should be sufficiently detailed that it should be possible to implement the approach on the basis of
the text alone. Having said that, you should also make every conceivable effort to keep the report
concise. Assuming a team of size 2, a 10–15 page report should be appropriate. Structure for the
reports:
- Introduction
- Background
- Problem definition
- Describing the data
- Methods
- Results
- Conclusions
- Future research
- Full list of references
Material and Resources
Lecture notes (slides) and complementary resources will be posted on the course Web site:
http://www.eecs.uottawa.ca/~turcotte/teaching/csi-5126/lectures.
Monographs
There is no required textook. However, the following book is particularly relevant in terms of the topics that it
covers and the emphasis on algorithms.
- Wing-Kin Sung (2010) Algorithms in Bioinformatics: A Practical Introduction. Chapman &
Hall/CRC. QH 324.2 .S86 2010
The third edition of the following textbook has been published at the end of August 2018. I will certainly draw
inspiration from that book:
The following two monographs are part of my short list of essential bioinformatics books.
- Durbin, R. et al (1998, 2000) Biological sequence analysis: probabilistic models of proteins and nucleic
acids. Cambridge University Press.
(QP 620 .B576 1998)
- Gusfield, D. (1997) Algorithms on strings, trees, and sequences: computer science and computational
biology. Cambridge University Press.
(QA 76.9 .A43 G87 1997)
Documents on the Web
Springer Link provides our community with access to journals, books, series, protocols and reference documents
(access restricted to University of Ottawa based on your IP address).
- Bernhard Haubold and Thomas Wiehe (2006). Introduction to computational biology: an evolutionary
approach. Birkhäuser Basel.
- Böckenhauer, H.-J. and Bongartz, D. (2007) Algorithmic Aspects of Bioinformatics: Springer
Ebooks
- Sperschneider, Volker (2008) Bioinformatics: Problem Solving Paradigms. Springer.
- Aluru S. (2006) Handbook of Computational Molecular Biology. Chapman & Hall/CRC.
(QH 324.2 .H357 2006)
- Warren J. Ewens, Gregory R. Grant (2001) Statistical Methods in Bioinformatics: An Introduction.
Springer.
Essential Cell Biology
- Wiesława Widłak (2013). Molecular Biology: Not Only for Bioinformaticians (Vol. 8248). Springer.
- Brown, T. A. (2007) Genomes. Oxford: BIOS Scientific Publishing.
(QH 447 .B76 2007)
- Rodden Robinson, T. (2005) Genetics For Dummies: For Dummies.
Plagiarism
Academic fraud is an act by a student that may result in a false evaluation (including papers, tests, examinations,
etc.). It is not tolerated by the University. Any person found guilty of academic fraud will be subject to severe
sanctions.
Here are some examples of academic fraud:
- Plagiarism or cheating of any kind;
- Present research data that has been falsified;
- Submit a work for which you are not the author, in whole or part;
- Submit the same piece of work for more than one course without the written consent of the professors
concerned.
- Please consult this webpage: it contains regulations and tool to help you avoid plagiarism.
An individual who commits or attempts to commit academic fraud, or who is an accomplice, will be penalized.
Here are some examples of possible sanctions:
- Receive an “F” for the work or in the course in question;
- Imposition of additional requirements (from 3 to 30 credits) to the program of study;
- Suspension or expulsion from the Faculty.
- You can refer to the regulations on this webpage.
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About the Instructor
I have been conducting research in bioinformatics since 1989. I am particularly interested in the inference of
structural motifs, mostly for ribonucleic acids.