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Introduction to Bioinformatics and its Applications

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Title: Introduction to Bioinformatics and its Applications


1
Introduction to Bioinformatics and its
Applications
  • Mohamad Rabbath
  • Msc. Inf. Freiburg University Software Engineer
    in OFFIS

2
Outline
  • Introduction
  • State of the Art in Bioinformatics
  • Bioinformatics Applications
  • Sequences Alignment Phylogenetic Trees
  • RNA Algorithms
  • Protein Structure Prediction Studies
  • Summary
  • References
  • Demo (Optional), http//cpsp.informatik.uni-freibu
    rg.de8080/index.jsp

3
Introduction
  • What is Bioinformatics?

Biological Data
Computing Power

4
Intorduction
  • Why do we need computers to analyze biological
    data?
  • A very large amount of biological data needs fast
    algorithms and computational resources
  • Exponential growth of biological data

5
Intorduction
http//www.ncbi.nlm.nih.gov/Genbank/genbankstats.h
tml
6
Intorduction
  • Why do we need computers to analyze biological
    data?
  • A Very large ammount of biological data needs
    fast algorithms and computational resources
  • Exponential growth of biological data
  • Growing gap between known sequences and known
    structures (RNA and Proteins)
  • Structure prediction means lower cost

7
Intorduction
  • What skills are required for Bioinformatics?
  • Deep knowledge in Database design and
    implementation
  • Extensive ability of programming and designing
    complex software systems and algorithms (
    languages like c, perl, python, java, are
    widely used in bioinformatics)
  • Strong ability of mathematical knowledge and
    statistics
  • Some background in biology

8
State of the Art in Bioinformatics
  • Bioinformatics was the key element in completing
    the Genome project in 2003
  • Currently many researches study the RNA and
    Protein structures
  • Sequencing human genome (23andme project is a
    very good example)

9
Bioinformatics Applications
  • Drugs discovery and design
  • Sequences Alignment
  • RNA secondary structure prediction
  • Proteins structures prediction
  • Reduction of the cost of the Healthcare System
    (Ex. Early discovery of genetic diseases)

10
Sequences Alignment
  • Arranging regions of similarity that may be a
    consequence of functionality
  • Evolutionary relationships
  • Very similar to Natural language processing
    alignment problems

11
Sequences Alignment
  • A T G A A C C G C C T A A G C G G C A -- G
  • A T G -- -- C C G A C T A -- A C G G A A G
  • Three operations are defined
  • Substitution
  • Insertion
  • Deletion

12
Sequences Alignment Needleman-wunsch
  • Three steps in dynamic programming
  • Initialization
  • Matrix fill (scoring)
  • Traceback (alignment)
  • Mi,j MINIMUM Mi-1, j-1 Si,j (substitution),
    Mi,j-1 w (deletion),
  • Mi-1,j w (insertion)

13
Sequences Alignment Needleman-wunsch
14
RNA Secondary Structure Prediction
  • RNA plays main rule in Biological Information Flow

15
RNA Secondary Structure Prediction
  • RNA GGGCGUGGGCGUAGUCGU
  • RNA Structure

16
RNA Secondary Structure Prediction
  • C G
  • A U
  • CAGUCCGGCUGC ..

17
RNA Secondary Structure PredictionNussinov
  • Idea Maximizing the number of base pairs

18
RNA Secondary Structure PredictionNussinov
G C A C G A
C G
G
C
A
C
G
A
C
G
19
RNA Secondary Structure PredictionNussinov
G C A C G A
C G
G
C
A
C
G
A
C
G
G C A C G A C G
20
RNA Secondary Structure Prediction Limitation of
Nussinov
  • Base pair maximization does not yield
    biologically relevant structures
  • Only one structure predicted
  • Crossing structures can not be predicted

21
RNA Secondary Structure Prediction Zucker
  • Idea Energy minimization

22
Protein Structure Prediction
  • The primary structure is a sequence of amino
    acids
  • 3D Structure function

23
Protein Structure Prediction HP
(Hydrophobic-Polar) Model
  • The chemical group R (the side chain of the amino
    acid) gives the unique properties
  • The hydrophobic amino acids tend to cluster
    together
  • HP model restricts the 20 amino acids to two
    classes

24
Protein Structure Prediction3D Lattices
Representation
Cubic Lattice
FCC Lattice
25
Protein Structure PredictionHow to achieve
prediction?
  • Folding Simulation
  • Hidden Markov Model and other stochastic models?
  • Statistical model does not guarantee optimality
  • Polynomial time required
  • Constraint Programming Approach
  • Optimality is guaranteed
  • NP Completeness makes it time consuming

26
Protein Structure PredictionWhy Why constraint
approach?
  • The Solution is not unique (optimality is
    targeted)
  • The Space of the problem is relatively small
  • Offline problem

27
Protein Structure Prediction Enhanced constraint
approach
28
Protein Structure PredictionWhy Degenerecy and
protein-like sequences
Protein-like sequences in cubic lattice
Degeneracy
29
Summary
  • Bioinformatics is a promising branch resulted
    from the marriage of computer science and biology
  • The exponatioal growth of biological data
    requires machine analysis
  • Prediction problems are still open especially in
    RNA Proteins
  • Designing fast algorithms, high ability of
    programming skills and Databases design are the
    main skills required to develope software in
    bioinformatics
  • Both statistical and constraint approaches are
    used to tackle problems in bioinformatics

30
References
  • Computational Molecular Biology An Introduction
    Computational Molecular Biology An
    Introduction, Peter Clote and Rolf Backofen
  • CPSP-web-tools a server for 3D lattice protein
    studies (Martin Mann, Mohamad Rabbath ,Cameron
    Smith, Marlien Edwards, Sebastian Will, Rolf
    Backofen )
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