Introduction

• What is bioinformatics?
• Definitions and concepts
• Genome Projects
• Biological complexity
• The role of bioinformatics

Biological Databases

• Sequence databases (EMBL, GenBank, DDBJ, SWISS-PROT, PIR, TrEMBL)
• Protein family/domain databases (PROSITE, PRINTS, Pfam, SMART)
• Cluster databases (Prodom, Systers)
• Specialist databases (Flybase, Kegg)
• Database technologies (flat-file, relational, object)
• Search engines (SRS, Entrez)

Principles of Sequence Analysis I - Pairwise Comparison

• Sequence comparison algorithms
• Sequence comparison scoring systems
• Sequence database similarity searching algorithms (BLAST & FASTA family of programs)
• Similarity searching scores and their statistical interpretation

Principles of Sequence Analysis II - Motifs and Domains

• Algorithms for global multiple alignment
• Biological motifs (concensus, regular expressions, profiles, PSSMs, HMMs)
• The local multiple alignment problem and motif interference
• Applications for biological sequence similarity searching (PSI- & PHI-BLAST, motifs, patterns, databases)

Functional Genomics I - The Genome

• Data production and data flow (mapping, DNA sequencing, generation of scaffolds & contigs)
• Gene prediction (ab initio & similarity based)
• Genome annotation (pipelines, databases)
• Technology platforms

Functional Genomics II - The Transcriptome

• Strategies for generating ESTs and full length inserts
• EST clustering and assembly
• EST databases (DBEST, UNIGene, TGI, STACK, EGI)
• SAGE
• Statistical analysis of EST data
• Microarrays (target selection/design, image analysis, data validation, statistical analysis)
• Data resources

Functional Genomics III - The Proteome

• 2D gel data (image analysis)
• Mass spec data (principles, analysis, information integration, data validation & peptide sequence determination)

Molecular Evolution and Phylogeny

• Biological foundations and phylogenetic methods
• Transformation of biological characteristics and associated probabilities
• Terminology (homology, homoplasy, orthology & paralogy)
• Methodologies (cladistic maximum parsimony, bootstrapping, branch & bound methods)

Ontologies in Bioinformatics

• The need for ontologies (gene naming, functional classifications, references schemes)
• Gene ontology (GO model & tools)
• EcoCyc

Principles of Protein Structure Prediction

• Secondary structure analysis (secondary structure profiles)
• Protein structure and fold classification databases (PDB, CATH, SCOP)
• Principles of molecular dynamics
• Ab initio prediction
• Homology modelling
• Threading

Basic Informatics

• Introduction of information theory
• Basic statistics