Preliminary Syllabus

Advanced tools and questions in bioinformatics

Why the push towards translational medicine?
How can bioinformatics enable translational medicine?
What is Systems Biology?
More than thirty large scale measurement and experimental modalities can be used in molecular biology: what are they? Microarrays, SAGE, 2D-PAGE, quantitative proteomics, etc.
What makes these measurement modalities nearly comprehensive?
What kind of data do these measurement modalities represent?
How are some of these biological experiments actually done?

Introduction and review of eukaryotic molecular biology for the bioinformatician, as viewed through the measurement modalities available
DNA, RNA, Proteins
Genetic code
Signal peptides
Signal transduction
Genomes and disease

The different types of genome maps and how each is used in research and diagnostics
Physical maps: sequence tagged sites, radiation hybrid maps
Cytogenetic maps
Genetic maps
SNP maps
Haplotype maps
Disease/association maps
Sequence maps: BACs, shotgun sequencing
Annotating maps with ESTs
The human genome landscape: copy number variants, CpG islands, GC content, repeat census and dating, recombination, horizontal transfer, pseudogenes

Medical focus: maturity-onset diabetes of the young. Examples of how disruptions in gene regulation can cause disease.
Prediction of transcription factor binding sites
Chromatin immunoprecipitation data: how experiments are performed and how data is interpreted
How would you prove these types of findings using biomedical experiments? How are binding sites actually found?

Medical focus: Leukemia. Examples of how applying supervised algorithms to microarrays can be used for medical diagnostics: recapitulating pathological classification schema.
Starting with hypotheses
Normalization methods
Fold ratios and their confidence intervals
Supervised algorithms: nearest-neighbor, voting schemes
How would you prove these types of findings using biomedical experiments?

Medical focus: B-cell lymphoma. Examples of how applying unsupervised algorithms to microarrays can be used in disease discovery: new disease sub-types.
Unsupervised algorithms: clustering, self-organizing maps
Principal components analysis
Cross-validation, permutation testing, q-values
How would you prove these types of findings using biomedical experiments?

Medical focus: Ovarian cancer. How proteomics can be used to find biomarkers for diseases for which none exist.
Different spectroscopic methods
Data produced by each method
Protein identification, quantitation, function
The US Food and Drug Administration criteria for biomarkers
Future technologies

Medical focus: Alzheimer’s Disease. How protein-protein interactions are disrupted in neurological diseases.
Protein-protein interactions
Protein-DNA interactions
How are each studied biologically?

Medical focus: diabetes. How polymorphisms are associated with insulin resistance syndromes.
Study designs for genetic analysis
Calculating linkage disequilibrium
Haplotypes
Complex traits
What constitutes biological proof of the action of a polymorphism?

Medical focus: asthma and Leigh syndrome. How gene expression and polymorphism sequencing can lead to genes involved in these two diseases.
Nomenclature and identification
Reconciling modalities of measurements
Problems with intersection