Y.C. Wong Public Lecture 040611 - PowerPoint PPT Presentation

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Y.C. Wong Public Lecture 040611

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Title: Y.C. Wong Public Lecture 040611


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Outline
  • DNA and RNA
  • Genome, genes, and diseases
  • Palindromes and replication origins in viral
    genomes
  • Mathematics for prediction of replication origins

Cytomegalovirus (CMV) Particle
4
DNA and RNA
  • DNA is deoxyribonucleic acid, made up of 4
    nucleotide bases Adenine, Cytosine, Guanine, and
    Thymine.
  • RNA is ribonucleic acid, made up of 4 nucleotide
    bases Adenine, Cytosine, Guanine, and Uracil.
  • For uniformity of notation, all DNA and RNA data
    sequences deposited in GenBank are represented as
    sequences of A, C, G, and T.
  • The bases A and T form a complementary pair, so
    are C and G.

5
Genes and Genome
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Genes and Diseases
7
Virus and Eye Diseases
CMV Particle
  • CMV Retinitis
  • inflammation of the retina
  • triggered by CMV particles
  • may lead to blindness

Genome size 230 kbp
8
Replication Origins and Palindromes
  • High concentration of palindromes exists around
    replication origins of other herpesviruses
  • Locating clusters of palindromes (above a minimal
    length) on CMV genome sequence might reveal
    likely locations of its replication origins.

9
Palindromes in Letter Sequences
Odd Palindrome
  • A nut for a jar of tuna

ANUTFORA
AROFTUNA
J
Even Palindrome
Step on no pets
STEPON
NOPETS
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DNA Palindromes
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Association of Palindrome Clusters with
Replication Origins
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Computational Prediction of Replication Origins
  • Palindrome distribution in a random sequence
    model
  • Criterion for identifying statistically
    significant palindrome clusters
  • Evaluate prediction accuracy
  • Try to improve

13
Random Sequence Model
  • A mathematical model can be used to generate a
    DNA sequence
  • A DNA molecule is made up of 4 types of bases
  • It can be represented by a letter sequence with
    alphabet size 4
  • Adenosine
  • Cytosine
  • Guanine
  • Thymine

Wheel of Bases (WOB)
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Random Sequence Model
Each type of the bases has its chance (or
probability) of being used, depending on the base
composition of the DNA molecule.
  • Adenosine
  • Cytosine
  • Guanine
  • Thymine

Wheel of Bases (WOB)
15
Random Sequence Model
Each type of the bases has its chance (or
probability) of being used, depending on the base
composition of the DNA molecule.
  • Adenosine
  • Cytosine
  • Guanine
  • Thymine

Wheel of Bases (WOB)
16
Poisson Process Approximation of Palindrome
Distribution
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Use of the Scan Statistic to Identify Clusters of
Palindromes
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Measures of Prediction Accuracy
  • Attempts to improve prediction accuracy by
  • Adopting the best possible approximation to the
    scan statistic distribution
  • Taking the lengths of palindromes into
    consideration when counting palindromes
  • Using a better random sequence model

19
Markov Chain Sequence Models
  • More realistic random sequence model for DNA and
    RNA
  • It allows neighbor dependence of bases (i.e., the
    present base will affect the selection of bases
    for the next base)
  • A Markov chain of nucleotide bases can be
    generated using four WOBs in a Sequence
    Generator (SG)

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Sequence Generator (SG)
Wheels of Bases (WOB)
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Sequence Generator (SG)
Wheels of Bases (WOB)
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Sequence Generator (SG)
Wheels of Bases (WOB)
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Sequence Generator (SG)
Wheels of Bases (WOB)
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Sequence Generator (SG)
Wheels of Bases (WOB)
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Sequence Generator (SG)
Wheels of Bases (WOB)
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Sequence Generator (SG)
Wheels of Bases (WOB)
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Sequence Generator (SG)
Wheels of Bases (WOB)
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Sequence Generator (SG)
Wheels of Bases (WOB)
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Sequence Generator (SG)
Wheels of Bases (WOB)
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Sequence Generator (SG)
Wheels of Bases (WOB)
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Sequence Generator (SG)
Wheels of Bases (WOB)
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Sequence Generator (SG)
Wheels of Bases (WOB)
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Results Obtained for Markov Sequence Models
  • Probabilities of occurrences of single
    palindromes
  • Probabilities of occurrences of overlapping
    palindromes
  • Mean and variance of palindrome counts

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Related Work in Progress
  • Finding the palindrome distribution on Markov
    random sequences
  • Investigating other sequence patterns such as
    close repeats and inversions in relation to
    replication origins

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Other Mathematical Topics in Genes and Diseases
  • Optimization Techniques prediction of molecular
    structures
  • Differential Equations molecular dynamics
  • Matrix Theory analyzing gene expression data
  • Fourier Analysis proteomics data
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