CISC-471: Computational Biology

(also known as: CMPE-471)

(Fall 2018)


Last Modified:


Education is not the filling of a pail, but the lighting of a fire. Plutarch.

Quick Links

Class Hours and Locations
Text
Grading
Outline and Schedule

Course Instructor

David Rappaport
GOODWIN HALL Room 532
E-MAIL: daver AT cs dot queensu dot ca
OFFICE HOURS: Thursday 13:30 - 15:30
Or contact me after class or by e-mail to make an appointment.

Class Hours

Monday 8:30-10:00 Kingston Hall RM 208
Thursday 10:00-11:30 Kingston Hall RM 208

Text

Neil C. Jones and Pavel A. Pevzner (2004) An Introduction to Bioinformatics Algorithms, MIT Press.
I will be following this book and referring to it as the "text". If you want to purchase a textbook this is the one that I would recommend as a first choice.
There are powerpoint slides available on-line to supplement this text book.
Bioinformatics Algorithms: An Active Learning Approach is an enhanced electronic textbook developed by the author of the course text. You can access some free videos and Powerpoint slides.
Rosalind is a platform for learning bioinformatics through problem solving.

Arthur M. Lesk (2002) Introduction to Bioinformatics, Oxford University Press.

An electronic copy of this book is available from the Queen's Library .
Other books that I have on my desk are:
Wing-Kin Sung (2010) Algorithms in Bioinformatics: A Practical Introduction, CRC Press
Pavel A. Pevzner (2001) Computational Molecular Biology: An Algorithmic Approach

Course Description

Calendar Description of CISC-471

Advanced computational approaches to the problems in molecular biology. Techniques and algorithms for sequence analysis and alignment; molecular databases; protein structure prediction and molecular data mining.

Prerequisites: CISC-271/3.0, CISC-352/3.0, CISC-365/3.0, BCHM 218/3.0 (or MBIO-218/3.0), BIOL-334/3.0 (or BCHM-315/3.0).

This course is required in the Biomedical Computing (BMCO) program.

My Description of CISC-471(David Rappaport)

Advances in computer hardware and algorithmic techniques have opened many avenues of inquiry that are driven by computation. The fields of Biology and Medicine are two high profile examples where stunning scientific breakthroughs have occurred within the past few years. In this course I will follow the course text An Introduction to Bioinformatics Algorithms by Neil C. Jones and Pavel A. Pevzner by reviewing standard algorithmic techniques and how they are applied to scientific discovery in biology and bioinformatics. The emphasis will be on the strengths and limitations of these techniques.

Learning outcomes

Knowledge of state of the art algorithms in Bioinformatics with emphasis on the strengths and limitations of these techniques.
Ability to distill information provided by a biologist and discern appropriate and effective algorithmic solutions.
By the end of the course students should be able to:
critically assess an algorithm for its correctness and efficiency, and argue about the algorithms correctness, performance bounds, and approximation ratio as appropriate
obtain an ability to make sensible pragmatic algorithmic choices of standard packages or implementations for specific applications

Outline and Schedule

Chapters numbers correspond to Neil C. Jones and Pavel A. Pevzner (2004) An Introduction to Bioinformatics Algorithms, MIT Press.

Week 1-2: September 6-13 - Introductory topics. Chapters 1-3.
Week 3-4: September 17-27 - Exhaustive search, branch and bound. Chapter 4.
Week 5-6: October 1-11 - Greedy algorithms. Chapter 5.
Week 7-8: October 15-25 - Dynamic programming, sequence alignment Chapter 6.
Week 9-10: October 29 - November 8 - Divide and conquer, sequence alignment Chapter 7.
Week 11-13 November 12-29. To be discussed, and review.

The topics covered this year will be similar to last year, but may differ slightly at times. You can see a fairly detailed record on last year's web page: http://research.cs.queensu.ca/home/daver/471/index2017W.html
The following table will be updated as the term progresses.

Monday Thursday
Week 1
Introduction.
September 3
First Class is on Thursday, Sept. 6.
September 6
Introduction/ Finding most frequent k-mers
Watch this video: The Search for Hidden Messages in the Replication Origin (Part 1). In particular near the 10 minute mark he discusses different approaches to solve the most frequent k-mer problem. Homework 1.

Week 2
Introduction.
September 10
Finding most frequent k-mers, continued. Review of Big-Ο, Big-Ω, Big-Θ Making Change See Section 2.3 of the text.
September 13
Solutions to Homework #1

Week 3
Exhaustive Search. (Chapter 4)
September 17
Restriction Mapping. See section 4.1-4.3 of the text. Homework 2.
Be prepared to share your solutions with classmates in class on Thursday September 20
September 20
Solutions to Homework #2
Motif finding. See section 4.4-4.9 of the text.
Homework 3.
Be prepared to share your solutions with classmates in class on Thursday September 27.
Week 4
Greedy Algorithms (Chapter 5)
September 24
Median, motif, branch and bound, sorting by reversals.
September 27
Solutions to Homework #3
Sorting by reversals see Chapter 5.
Homework 4.
Be prepared to share your solutions with classmates in class on Thursday Oct. 4.
Week 5
Dynamic Programming Algorithms (Chapter 6)
October 1
October 4
Solutions to Homework #4
Week 6
October 8
THANKSGIVING. No class.
October 11
Quiz #1 base on homework 1, 2, 3, and 4
Week 7
Dynamic Programming Algorithms (Chapter 6)
October 15
Homework 5. Due October 18.

October 18

Be prepared to share your homework #5 solutions in class.
Week 8
Dynamic Programming Sequence Alignment (Chapter 6)
October 22
Global Alignmet, Local Alignment.
Homework 6. Due November 1.

October 25
FALL BREAK. No class.
Week 9
Dynamic Programming Sequence Alignment (Chapter 6)
October 29
November 1
Be prepared to share your homework #6 solutions in class.
Space efficient sequence alignment.
Homework 7. Due November 7.
Week 10
Graph Algorithms.
November 5
Sequencing by Hybridization, Euler tours
November 8
Euler tours
Homework 8. Due November 15.
Week 11
November 12
Class moved to Jeffry 102
November 15
Class moved to Jeffry 422.
Week 12
November 19
November 22
Quiz #2 Based on homework 5,6,7,8.
Week 13
November 26
November 29

Grading

Grades will be made up of classroom participation, midterm quizzes and a final. Assigned homework will include some programming exercises. A large component of the course will involve students solving assigned problems in class.
In class participation: 15%
Two in class midterm quizzes, each worth 22.5%, total: 45%
Final exam: 40%
The quizzes will be scheduled as follows:
Quiz 1: Thursday, October 11.
Quiz 2: Thursday November 22.
Please note the use of a calculator will not be needed and will not be permitted for any of the quizzes. Quizzes will be held in Class.

All components of this course will receive numerical percentage marks.The final grade you receive for the course will be derived by converting your numerical course average to a letter according to Queen's Official Grade Conversion Scale:

Numeric Range Letter Grade GPA
90-100 A+ 4.3
85-89 A 4.0
80-84 A- 3.7
77-79 B+ 3.3
73-76 B 3.0
70-72 B- 2.7
67-69 C+ 2.3
63-66 C 2.0
60-62 C- 1.7
57-59 D+ 1.3
53-56 D 1.0
50-52 D- 0.7
0-49 F 0

Location and Timing of Final Examinations

As noted in Academic Regulation 8.2.1, "the final examination in any class offered in a term or session (including Summer Term) must be written on the campus on which it was taken, at the end of the appropriate term or session at the time scheduled by the Examinations Office." The exam period is listed in the key dates prior to the start of the academic year in the Faculty of Arts and Science Academic Calendar and on the Office of the University Registrar's webpage. A detailed exam schedule for the Fall Term is posted before the Thanksgiving holiday; for the Winter Term it is posted the Friday before Reading Week, and for the Summer Term the window of dates is noted on the Arts and Science Online syllabus prior to the start of the course. Students should delay finalizing any travel plans until after the examination schedule has been posted. Exams will not be moved or deferred to accommodate employment, travel /holiday plans or flight reservations.

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Students are responsible for familiarizing themselves with the regulations concerning academic integrity and for ensuring that their assignments conform to the principles of academic integrity. Information on academic integrity is available in the Arts and Science Calendar (see Academic Regulation 1 ), on the Arts and Science website (see Academic Integrity ), and from the instructor of this course. Departures from academic integrity include plagiarism, use of unauthorized materials, facilitation, forgery and falsification, and are antithetical to the development of an academic community at Queen's. Given the seriousness of these matters, actions which contravene the regulation on academic integrity carry sanctions that can range from a warning or the loss of grades on an assignment to the failure of a course to a requirement to withdraw from the university.

Accommodation Statement

Queen's University is committed to achieving full accessibility for persons with disabilities. Part of this commitment includes arranging academic accommodations for students with disabilities to ensure they have an equitable opportunity to participate in all of their academic activities. If you are a student with a disability and think you may need accommodations, you are strongly encouraged to contact Student Wellness Services (SWS) and register as early as possible. For more information, including important deadlines, please visit the Student Wellness website at: http://www.queensu.ca/studentwellness/accessibility-services/