Introduction to Computational Biology - PowerPoint PPT Presentation

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Introduction to Computational Biology

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Kimura's two-parameter model. Molecular clock. Relative rate test. Phylogenetics ... Quantile normalization. Quality control. Data distributions. Histograms. boxplots ... – PowerPoint PPT presentation

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Title: Introduction to Computational Biology


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
  • Kimuras 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
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