Instructor: Dimitris Papamichail
Office: Ungar 403
Email: d.papamichail@miami.edu
Office Hours: 3:30pm-4:30pm Tuesday and Friday, and by appointment.
Course Time: To be announced
Place: To be announced
Prerequisites: (CSC120 or CSC210) and (BIL104 or BIL150 or BIL352 or BIL552) or equivalent. Prerequisite requirements may be waived with permission of the instructor. Attendees of this course will be expected to have some basic knowledge of biology/genomics and/or basic knowledge of programming/algorithms. An effort will be made to pair up computational and life scientists, in order to complement their strengths and encourage interdisciplinary knowledge exchange.
Textbook: The textbook for the course will be:
Other useful books in the field are:
H.-J. Bockenhauer, D. Bongartz: Algorithmic Aspects of Bioinformatics, Springer 2007
A. Lesk: Introduction to Bioinformatics, Oxford University Press 2008
J. Xiong: Essential Bioinformatics, Cambridge University Press 2006
D. W. Mount: Bioinformatics: Sequence and Genome Analysis, CSHL Press 2004
N. Christiani, M. Hahn: Computational Genomics: A case studies approach, Cambridge University Press 2007
R. Durbin, S. R. Eddy, A. Krogh, G. Mitchison, Biological Sequence Analysis, Cambridge U. Press, 1998.
P. Higgs, T. Attwood: Bioinformatics and Molecular Evolution, Blackwell Publishing 2005.
A. D. Baxevanis, B. F. F Ouellette: Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Wiley 2004
S. Gopal, A. Haae, R.P. Jones, P. Tymann: Bioinformatics, A Computing Perspective, McGraw Hill 2009.
S. Mitra, S. Datta, T. Perkins, G. Michailidis, Introduction to Machine Learning and Bioinformatics, CRC Press 2008.
Additionally, for people interested in working in the area of bioinformatics, the following books provide excellent guides to resources and tools:
C. Bigas, P. Jambeck: Bioinformatics Computer Skills, O’Reilly 2001
S. Markel, D. Leon: Sequence Analysis, O’Reilly 2003
J.-M. Claverie, C. Notredame: Bioinformatics for Dummies, For Dummies Press 2003
I. Korf: Blast, O’Reilly 2003
J. Tisdall: Beginning Perl for Bioinformatics, O’Reilly 2001
R. A. Dwyer: Genomic Perl, From Bioinformatics Basics to Working Code, Cambridge 2003.
Material: This is a course in bioinformatics, focusing in past, current and potential future problems in genomics. Our emphasis will be algorithmic, on analyzing appropriate combinatorial algorithm problems arising in the life sciences and the techniques to solve them. Topics include:
Introduction to the field of Bioinformatics.
Biology primer for Computational Scientists, Computer Science primer for Life Scientists.
Time and space complexity of computational problems. Why some problems are hard and how to deal with them. Approximations and heuristics.
Pattern matching and sequence homology. Global, local alignment and sequence classification based on oligonucleotide distributions. BLAST and other heuristics.
Genome assembly. Theory and applications of suffix trees and arrays.
Transcription Factor Binding Site recognition and motif finding. Profiles and multiple alignment.
Gene prediction with ad hoc and machine learning techniques, Hidden Markov Models and their algorithmics.
Phylogeny, ultrametric and additive trees, construction techniques and properties.
Microarray analysis, hierarchical agglomerative and k-means clustering.
RNA folding, dynamic programming algorithms. Protein folding, homology, de novo and threading based approaches.
Gene design and synthesis, codon bias, codon pair bias and protein overlapping
This course will complement the Bioinformatics Tools course BIL552, which covers similar subjects, but focuses on usage and understanding of tools and their functionality, instead of analyzing, designing and implementing them.
Grading: Grades will be assigned as follows:
Homework Assignment – 20%
Semester Project - Proposal 5%
Semester Project - Progress Report 10%
Semester Project - Final Report 35%
Final Exam - 30%
Homework Assignments: There will be only one homework over the course of the semester. Part will be algorithmic in nature and part biological. The level of bias towards disciplines will be determined by the composition of the class. Problems might involve light programming and/or web search.
Semester Project: This is your opportunity to study some aspect of bioinformatics in depth. Suitable projects will be original research in select topics, implementations or reviews/presentations. A list of possible topics will be distributed about a month into the semester, although you are encouraged to devise your own.
A project proposal will be due about half a month later. A progress report will be due sometime in the beginning of November. Both will be graded, to provide motivation not to leave the project to the last week of the semester.
Instructions and consultation will be provided when the projects are announced. I will also schedule brief discussions with each individual/group to ensure understanding of the project topic they select.
Final Exam: There will be a final exam to encourage students to review the material taught.
Rules of the Game:
This course reflects a bias toward the algorithmic aspects of computational biology. You should not take this course if you have not had the equivalent of CSC120, unless you can prove to me you have some computational background or are willing to cover on your own some computer science topics.
The course may involve programming, using a programming language of your choice. A few projects may require use of a specific programming language, in which case it may be Java or Perl. There will be projects available involving no programming.
I strongly encourage interest from students with a life sciences background. My vision is to pair up computational and life scientists for projects and the homework as much as possible, so as to help each other get a more balanced view of the topics covered.
Course handouts, material, homework assignments, projects, etc. will be available in blackboard after being presented in class, along with the latest announcements. Please check it out often. I will try to accompany important announcements with email notifications.
Because a primary goal of the course is to teach professionalism, any academic dishonesty will be viewed as evidence that this goal has not been achieved, and will be ground for receiving a failing grade. For details, please refer to the Honor Code of the University of Miami at:
http://www.miami.edu/dean-students/pdf/undergrad_honorcode.pdf
This course is relatively new, so there could be changes/adjustments to the material, homework assignment, projects and exams. Since the class is small, I am open to suggestions to improve and direct the subjects covered.
The required textbook ‘Introduction to Bioinformatics Algorithms’ will be followed to some extent, but we may deviate in several subjects. It is recommended that you study the relevant sections from the book, since it is well written and provides nice examples. My presentations and other notes will be made available on blackboard.