BCB 568. Bioinformatics II (Advanced Genome Informatics). (Cross-listed with
GDCB, STAT, COM S.) (3-0) Cr. 3. S. Prereq: BCB 567, BBMB 301, Biol 315, Stat 430,
credit or enrollment in Gen 411. Advanced sequence models. Basic methods in molecular
phylogeny. Hidden Markov models. Genome annotation. DNA and protein motifs.
Introduction to gene expression analysis.
Spring 2011
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, in particular the prerequisite BCB 567
course. Lectures will cover the biological motivation of various problems and the
theoretical foundations of modeling solutions. Homework assignments will include
exercises and programming tasks for practical applications.
Topics to be covered include: statistical sequence models, Markov models, Hidden
Markov models, score-based sequence analysis, gene structure prediction and other tasks
of genome annotation, basic methods in molecular phylogeny, computational approaches
to comparative and functional genomics, introduction to gene expression analysis.
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 567, BBMB 301, Biol 315, Stat
430, and credit or enrollment in Gen 411.
Please address any special needs or special accommodations with the instructor at the
beginning of the semester or as soon as you become aware of your needs. Those seeking
accommodations based on disabilities should obtain a Student Academic
Accommodation Request (SAAR) from the Disability Resources (DR) office (515-2946624). DR is located in Room 1076 of the Student Services Building.
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.

Bioinformatics
o View current issue

Genome Research
o View current issue


Journal of Molecular Evolution
Molecular Biology and Evolution
o View current issue

Nucleic Acids Research
o View current issue

Plant Physiology
o View current issue

PNAS
o
View current issue
Half Sheet Synopsis of BCB 568 Topics
Core topics (italicized: not covered S2008):
 Sequence models: random sequences; sequence space; sampling,
permutation/randomization tests.
 Applications of sequence models: codon usage; discrete and continuous models of
nucleotide substitution; synonymous and nonsynonymous nucleotide substitutions.
 Markov models; Interpolated Markov Models; Markov Random Fields; applications to
genome annotation; genome rearrangements.
 Advanced sequence models: Random walks; score-based sequence analysis (BLAST
statistics).
 Basic methods in molecular phylogeny: phylogenetic trees; distance matrix methods;
maximum parsimony methods; maximum likelihood methods.
 Hidden Markov Models: theory; training; applications to gene structure annotation,
 sequence alignment, and protein classification.
 DNA and protein motifs: weight matrices; word-based methods; EM algorithm, Gibbs
sampling, and simulated annealing; Bayesian methods.
 Introduction to gene expression analysis, mRNA and protein expression data analysis,
multiple comparisons.
Guest lecture topics (4-6 lectures):
Plant and animal genomics; applications (S2008: Tuggle, Duvick, Reecy, Lűbberstedt)
Special topics (2-6 lectures):
Current papers of interest (varies from year to year)
BCB program/Orientation/2011/BCB568-Description.doc