BCB/GDCB/STAT/COM S 594 [568] Brendel/Huang Spring 2007

Bioinformatics II. Advanced Genome Informatics.

BCB/GDCB/STAT/COM S 594 [568] Brendel/Huang Spring 2007

Time & Location:    Tu, Th 9:30A - 10:50A (Units: 3); 1424 MBB
Instructors:    Volker Brendel (2112 MBB; Tel.: 294-9884)    Xiaoqiu Huang (226 Atanasoff; Tel.: 294-2432)
Teaching Assistant:    Matthew Wilkerson (2256 MBB; Tel.: 294-1518)
Teaching Philosophy:     emphasizes integration of research and textbook learning and interdisciplinary study.
Email:    VB, vbrendel@iastate.edu; XH, xqhuang@cs.iastate.edu; WS, mwilkers@iastate.edu
WWW:     http://gremlin1.gdcb.iastate.edu/~volker/
Office Hours:    by appointment
Grades:    will be determined as described below.
Schedule:     http://gremlin1.gdcb.iastate.edu/~volker/teaching/bcb594schedule.html
Computing Resources:     You will need access to UNIX or LINUX based computers for your project assignments.

Synopsis

Precipitated by an enormous increase in molecular sequence data (both DNA and protein), computational tools have become essential to molecular biology and genome research. Expertise in computational biology/bioinformatics is in great demand, and some level of proficiency in the subject is expected of anyone engaged in biological research at the molecular level. This course seeks to provide a general introduction to the subject as well as a discussion of several current research topics, with emphasis on statistical concepts and approaches. In this respect, this course is complementary to other courses offered at ISU that emphasize algorithmic issues and solutions (BCB 548, BCB 549, BCB 551). Lectures will cover the biological motivation of various problems and the theoretical foundations of modeling solutions. Homework assignments will include excercises and programming tasks for practical applications. Topics will include: statistical sequence models, dynamic programming methods for pairwise sequence alignment, multiple sequence alignment, Hidden Markov models, score-based sequence analysis, amino acid substitution scoring matrices, gene structure prediction, construction of phylogenetic trees from sequence data. The goal of the class is to prepare students to critically read and contribute to the relevant research literature.

Prerequisites

This interdisciplinary course is primarily directed at graduate and advanced undergraduate students in biology, computer science, statistics, or related disciplines who aspire to a professional career in this field. Familiarity with basic concepts and knowledge in molecular biology and statistics as well as programming experience (Perl, C, or C++) are assumed. Prerequisite courses are BCB 548 [567], BBMB 301, Biol 315, Stat 401, Stat 432, and credit or enrollment in Gen 411. Good sources for fundamental probability and statistics concepts are

Taylor, H.M. & Karlin, S. (1998) An Introduction to Stochastic Modeling. 3rd edition. Academic Press, San Diego, USA.

and

Feller, W. (1971) An Introduction to Probability Theory and its Applications. Vol. 1, 2nd edition. John Wiley & Sons, New York.

A suitable text for the genetics and molecular biology background is

Atherly, A.G., Girton, J.R. & McDonald, J.F. (1999) The Science of Genetics. Saunders College Publishing, Fort Worth, USA.

Textbooks

There are now a growing number of good textbooks on bioinformatics and computational biology. We will not follow any particular text. However, some of the material in the course is covered in the textbooks, typically with different emphasis and exposition. Students are expected to take notes during lectures and to study the material independently outside of the classroom using textbooks of their choice or original sources. The following texts will be available in the BCB office, MBB 2014, for perusal and short-term borrowing:

Baldi, P. & Brunak, S. (1998) Bioinformatics - The machine learning approach. MIT Press, Cambridge MA.
Durbin, R., Eddy, S., Krogh, A. & Mitchison, G. (1998) Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press, UK.
Ewens, W.J. & Grant, G.R. (2001) Statistical methods in bioinformatics. An introduction. Springer, New York.
Gusfield, D. (1997) Algorithms on strings, trees, and sequences. Cambridge University Press, UK.
Mount, D.W. (2001) Bioinformatics: Sequence and Genome Analysis. Cold Spring Harbor Laboratory, NJ.
Setubal, J.C. & Meidanis, J. (1997) Introduction to Computational Molecular Biology, PWS, Boston MA.
Waterman, M.S. (1995) Introduction to Computational Biology: maps, sequences and genomes. Chapman & Hall, London, UK.

Copies of some articles of interest will be compiled in 594 class folders that are also available in the BCB office.

Selected Journals

Students will be expected to read current research literature in the field. The following list provides a selected relevant journals that are electronically accessible from ISU accounts. For more choices, see e-Journals @ ISU.

Assignments

Homework assignments will be posted regularly to deepen understanding of the lecture material (4-5 assignments total). Written answers will be due two weeks after the assignment is posted (unless specified otherwise).

Grading

In addition to the graded homework, there will be an oral examination at the end of the class in which the students will individually discuss the homework assignments and the class material with the instructions. Approximate weights for the class grade: homework, 60%; oral examination, 40%.