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Bioinformatics

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Bioinformatics Predrag Radivojac INDIANA UNIVERSITY – PowerPoint PPT presentation

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Title: Bioinformatics


1
Bioinformatics
Predrag Radivojac Indiana University
2
Basics of Molecular Biology
Eukaryotic cell
  • Can we understand how cells function?

3
Bioinformatics is multidisciplinary!
  • What is Bioinformatics?
  • Integrates computer science, statistics,
    chemistry, physics, and molecular biology
  • Goal organize and store huge amounts of
    biological data and extract knowledge from it
  • Major areas of research
  • Genomics
  • Proteomics
  • Databases
  • Practical discipline

Some major applications Drug design
Evolutionary studies Genome
characterization
4
Interesting Problems
  • Sequence
  • Alignment

5
Interesting Problems
6
Interesting Problems
  • Sequence assembly

Goal solve the puzzle, i.e. connect the pieces
into one genomic sequence
7
Interesting Problems
  • Proteomics

Mass spectrometry
8
Interesting Problems
  • Microarray data

9
Interesting Problems
  • Gene Regulation
  • Functional Genomics

10
Diseases are interconnected
Goh et al. PNAS, 104 8685 (2007).
11
Disease
  • Development of tools that can be used to
    understand and treat human disease
  • Prediction of disease-associated genes
  • Important from
  • biological standpoint
  • medical standpoint
  • computational standpoint
  • Background
  • human genome
  • low-throughput data
  • high-throughput data
  • ontologies for protein function at multiple levels

The Time is Right!
www.cancer.gov
12
Alzheimers disease
Top PhenoPred hits 1) CDK5 2) NTN1
AUC 77.5
13
Loss/Gain of function and disease
E6V
4hhb
2hbs
Sickle Cell Disease Autosomal
recessive disorder E6V in HBB causes
interaction w/ F85 and L88 Formation
of amyloid fibrils Abnormally shaped
red blood cells, leads to sickle cell anemia
Manifestation of disease vastly different
over patients
Pauling et al. Science 110 543 (1949). Chui
Dover. Curr Opin Pediatr, 13 22 (2001).
http//gingi.uchicago.edu/hbs2.html
14
Lipitor (ATORVASTATIN)
E6V
15
Proteins chains of amino acids
  • biomolecule, macromolecule
  • more than 50 of the dry weight of cells is
    proteins
  • polymer of amino acids connected into linear
    chains
  • strings of symbols
  • machinery of life
  • play central role in the structure and function
    of cells
  • regulate and execute many biological functions

a) amino acid b) amino acid chain
Introduction to Protein Structure by Branden and
Tooze
16
Protein structure
  • peptide bonds are planar and strong
  • by rotating at each amino acid, proteins adopt
    structure

Introduction to Protein Structure by Branden and
Tooze
17
Protein function
  • Multi-level phenomenon
  • biochemical function
  • biological function
  • phenotypical function
  • Example kinase
  • biochemical function transferase
  • biological function cell cycle regulation
  • phenotypical function disease
  • Function is everything that happens to or through
    a protein (Rost et al. 2003)

18
Protein contact graph
C??- C??lt 6A
  • Myoglobin 1.4A X-ray PDB 2jho 153 residues

19
Protein contact graph
20
Protein contact graph
21
Residue neighborhood
Notation
S113 of isocitrate dehydrogenase
G (V, E) f V ? A A A, C, D, W,
Y g V ? ?1, 1
22
S
Graphlets are small non-isomorphic connected
graphs. Different positions of the pivot vertex
with respect to the graphlet correspond to
graph-theoretical concept of automorphism orbits,
or orbits.
Przulj et al. Bioinformatics 20 3508 (2004).
23
Results
24
Key insight Efficient combinatorial enumeration
of graphlets / orbits over 7 disjoint cases
breadth-first search
  • 2-graphlets 01
  • 3-graphlets 011, 012
  • 4-graphlets 0111, 0112
  • 0122, 0123

25
02
01
01 A o2 A2 o5, o6, o11 A3 o3, o4
? A 0, 1 00, 01
10, 11 (3) A 0, 1, 2 00, 11, 22,
01 10, 02 20, 12 21 (6) binomial
(multinomial) coefficients A 20,
dimensionality 1,062,420
26
Graphlet kernel
  • Inner product between vectors of counts of
    labeled orbits
  • where
  • K is a kernel because matrices of inner products
    are symmetric and positive definite (proof due to
    David Haussler).

?i(x) is the number of times labeled orbit i
occurs in the graph
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