CISC-471: Computational Biology

(also known as: CMPE-471)

(Fall 2019)

Last Modified:

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

Quick Links

Class Hours and Locations
Outline and Schedule

Course Instructor

David Rappaport
E-MAIL: daver AT cs dot queensu dot ca
OFFICE HOURS: Tuesday 12:30 - 14:30
Or contact me after class or by e-mail to make an appointment.

Class Hours

Monday 8:30-9:30 JEFFERY RM 102
Tuesday 10:30-11:30 JEFFERY RM 102
Thursday 9:30-10:30 JEFFERY RM 102


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

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:
The following table will be updated as the term progresses.

Monday Tuesday Thursday
Week 0
September 2
First Class is on Thursday, Sept. 5.
September 3
First Class is on Thursday, Sept. 5.
September 5
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 1
September 9
Introduction/ Finding most frequent k-mers
September 10
Review of complexity analysis, recursive algorithms.
September 12
Today we will go over solutions of HWK 1.
Homework 2.

Week 2
Exhaustive Search. The Partial Digest Problem (Chapter 4, sections 4.1, 4.2, 4.3)
September 16
Restriction Mapping. See section 4.1-4.3 of the text.
September 17
Restriction Mapping. See section 4.1-4.3 of the text.
September 19
Solutions to Homework #2
Homework 3.
Week 3
Exhaustive Search. Motifs in DNA Sequences. (Chapter 4, sections 4.4-4.9)
September 23
Scoring k-mers, to find Motifs in DNA sequences.
September 24
Hamming distance and Median strings in DNA sequences.
September 26
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. 3.
Week 4
Greedy Algorithms (Chapter 5)
September 30
October 1
October 3
Solutions to Homework #4
Introduction to dynamic programming algorithms.
Week 5
Dynamic Programming Algorithms (Chapter 6)
October 7
October 8
October 10
Quiz #1 base on homework 1, 2, 3, and 4
Week 6
Dynamic programming algorithms for sequence alignment (Chapter 6)
October 14
Thanksgiving no class.
October 15
Sequence alignment.
October 17
Homework 5. Due October 28.
Solutions to quiz #1.
Week 7
Dynamic Programming Sequence Alignment (Chapter 6)
October 21

October 22
October 24
FALL BREAK. No class.
Week 8
Divide and Conquer. (Space efficient Sequence Alignment, Chapter 7)
October 28
Solutions to Homework #5 Homework 5 submit. This is a programming homework that must be submitted by Nov.8 for participation credit.
October 29
Homework 6.
Due November 4.
October 31
Week 9
Graph Algorithms. (Chapter 8)
November 4
Be prepared to share your homework #6 solutions in class.
November 5
Homework 7.
Due November 11.
November 7
Week 10
Pattern Matching. (Chapter 9)
November 11
Be prepared to share your homework #7 solutions in class.
November 12
Homework 8.
Due November 18.
November 14
Week 11
November 18
Be prepared to share your homework #8 solutions in class.
November 19
November 21
Quiz #2 Based on homework 5,6,7,8.
Week 12
November 25
November 26
Guest Lecture by Professor Amber Simpson who is cross appointed to the School of Computing and Dept of Biomedical & Molecular Sciences.
November 28


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 10.
Quiz 2: Thursday November 21.
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

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