1. Introduction to Computational Biology Topics

2. Molecular Data Definition of data  DNA/RNA  Protein  Expression Basics of programming in Matlab  Vectors  Matrices  Loops  Conditions  Functions

3. Sequence Alignment Dot Plots Dynamic Programming  global alignment  local alignment K-tuple Methods  Fasta  Blast

4. Molecular Evolution Patterns of substitutions  Synonymous vs. nonsynonymous Estimation of substitutions  Jukes-Cantor model  Kimura’s two-parameter model Molecular clock  Relative rate test

5. Phylogenetics Homology: orthologs vs. paralogs Phylogenetic Trees Distance-based methods  UPGMA  Neighbor-joining Character-based methods  Parsimony  Tree confidence Bootstrapping

6. Genomics Genomic content  Hidden Markov Models (HMMs): CpG islands Motif finding  Gibbs Sampling Transcriptional factor binding sites Phylogenetic footprinting  Vista Plot

7. Microarray Technology cDNA vs. oligo arrays Labeling Hybridization Scanning Analysis

8. Normalization and Quality Control Array normalization methods  Global normalization  Lowess normalization  Quantile normalization Quality control  Data distributions Histograms boxplots

9. Differential Gene Expression Experimental Design  Replication  Pooling Two-sample Comparisons  Case-studies  Single slides  Replicate slides T-test and ANOVA P-values Adjusted p-values

10. Clustering Microarray Data Dissimilarity Measures Clustering Methods  Hierarchical methods  Partitioning methods K-means Self-organizing maps (SOMs)

11. Clustering Microarray Data Multivariate analysis  Principal Components Analysis  Singular Value Decomposition

12. Genetic Variation Alleles, frequencies, inheritance Population genetics: HWE SNPs: Single Nucleotide Polymorphisms  Databases  Patterns  Analysis

13. QTL: Quantitative Trait Loci Complex diseases Haplotype Analysis  Linkage  Association