Capita selecta in bioinformatics

URL study guide

Description

• Introduction and Preliminaries
  • Introduction, Motivation Examples
  • Algorithms, Complexity, Hardware, Parallel Computing
• Databases
  • Overview of the most widely used databases
  • File formats
  • Basic design principles and search algorithms
• Pattern Matching
  • Pairwise Sequence Alignment, Local vs Global Alignment, Multiple Sequence Alignment, Substitution Matrix, Dynamic Programming, Substitution Matrix
• Computational Gene Hunting
  • High Throughput Gene Sequencing
  • Algorithms behind the human genome mapping
  • Single Nucleotide Polymorphism, Haplotype Identification
• Networks
  • Graphs, Basic Elements of Graph Theory, Basic Graph Algorithms
  • Characterization of Biological Networks, Scale Free Networks
  • Network Inference
• Protein Structure
  • Classical methods, e.g., Markov Models, Hidden Markov Chains, overview of protein structure databases
• RNA Seq Data Analysis
  • Overview of some standard tools
  • Basic algorithms
• Microarray expression analysis
• Drug Discovery
  • Chemo-informatics

Objectives

• obtains a concise yet comprehensive overview and knowledge of the basic topics in bioinformatics,
  • •  e.g., sequence analysis, protein structure, network analysis
• understands some of the most used standard methods and tools in bioinformatics,
  • • e.g., gene finding, gene similarity comparison, protein structure prediction
• can adapt and apply those methods in a standard way in new problems and situations to real data
• can find, validate and recommend alternatives for those methods
• can analyze and identify patterns in typical bioinformatics problems and use them to formulate an algorithm
• can develop and implement efficient algorithmic based solutions to novel problems
• can critically evaluate and validate these solutions and recommend prospective improvements

Method of Assessment