1. 341- INTRODUCTION TO BIOINFORMATICS Overview of the Course Material 1

2. Introduction to Biology Biological Terms: DNA, RNA, Gene, Gnome, Codon, Protein, Proteome, Amino acids Central Dogma: – The transcription of DNA to mRNA. – Translation of the mRNA to a protein 2

3. Sequencing and Sequence Alignment Sequences, Mutations, and Evolution Sequencing technologies: – Sanger Sequencing – Next Generation Sequencing Sequence Alignment: – Why is it needed? – Sequence Similarity Scoring Matrices; e.g. BLOSUM – BLAST Algorithm – Accuracy, Sensitivity and Selectivity of sequence searching – Dynamic Programming based Sequence Alignment Algorithms: Needleman-Wunsch (global alignment), Smith-Waterman(local alignment) 3

4. Functional Genomic and Microarray Analysis What is gene expression? What are microarrays? What are the steps of microarray experiments? Statistical significance tests that are used for evaluating the results of microarray experiments; e.g., t -test. Data clustering / classification – Distance Measures – Hierarchical clustering – Decision trees – K-means/K-medoids clustering – K-nearest neighbourhood clustering 4

5. Introduction to Graph Theory Basic definitions on graphs and graph types; e.g. bi -partite graphs, cycles, trees, clique, path, subgraphs, connected component, isomorphism, automorphism, automorphism orbits. Minimum spanning trees – Kruskal’s Algorithm – Prim’s Algorithm Graph representations: – Adjacency Matrix – Adjacency List Running times of Algorithms; upper bounds, lower bounds. Complexity classes of problems: P, NP, NP-Hard Graph Traversal Algorithms: – Breadth-first search – Depth-first search Shortest Path Algorithms – Dijsktra’s Algorithm – Floyd-Warshall algorithm 5

6. Introduction to Biological Networks Different types of biological networks: – Metabolic Networks – Transcription Regulation Networks – Cell Signalling networks – Protein-protein interaction Networks – Genetic interaction Networks – Protein Structure Networks What do they represent? What are the relations among these network types? What are the available sources of data for obtaining these networks? What are the possible sources of bias in these networks? 6

7. Network Properties The global network properties: – Degree / Degree Distribution – Clustering Coefficient – Diameter / Shortest Path Length Distribution – Centrality Measures: Degree Centrality Betweenness Centrality Closeness Centrality Eccentricity Centrality The local network properties: – Network motifs – Graphlets / Graphlet Degree Distributions 7

8. Network Models Different types of network models: – Erdos-Renyi Networks (ER) – Small-world Networks – Scale-free Networks (SF) – Hierarchical Networks – Geometric Networks (GEO) – Generalized Random Networks (ERDD) – Geometric Networks with Gene Duplication / Divergence Events(GEOGD) – Scale-free Networks with Gene Duplications/Divergence Events (SFGD) – Stickiness-index based Networks (STICKY) 8

9. Network Comparison / Alignment Paralogy / Orthology Network: alignment, integration, querying Network Alignment Algorithms: – Global / Local Alignment – Pairwise Alignment / Multiple Alignment – Functional vs. topological information Alignment quality evaluations measures: o Edge Correctness, o Size of Common Connected Subgraphs, o attributed to chance, o biological quality 9

10. 10 Network Comparison / Alignment Key algorithmic components: – Similarity between nodes Functional Topological – Search (identification of high-scoring alignments) Seed-and-extend Finding an optimal alignment 10

11. Protein 3D Structure Four levels of protein structure Available data The structure function paradigm Rigid and flexible structure comparison – Algorithms 11

12. Network Data Integration Today’s lecture 12