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Evolution of MicroRNA Targets

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Subtracting signal to noise, estimated functional targets for each ... a motif across different UTRs, look for avoidance of the motif across different UTRs ... – PowerPoint PPT presentation

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Title: Evolution of MicroRNA Targets


1
Evolution of MicroRNA Targets
  • Kai-How Farh
  • 9/21/2005

2
Nonconserved MicroRNA targets
  • A typical mammalian microRNA has
  • 1000-3000 targets in one genome
  • About 15-25 of these sites are conserved across
    all mammals
  • Subtracting signal to noise, estimated functional
    targets for each microRNA around 100-300
  • What about the other 90?

3
MicroRNA targeting specificity
  • MicroRNAs target a huge number of genes
  • Only 10 of targets are conserved
  • There is no difference in conserved
    nonconserved target sites
  • Additional determinants of targeting?
  • Location and timing of expression?

4
MicroRNAs are tissue specific
  • Many microRNAs have highly tissue specific
    profiles
  • Expression analysis underestimates specificity of
    miRNAs due to combining tissues

5
Can tissue specificity account for microRNA
targeting specificity?
Profile of MicroRNA miR-1s targets, ranked by
their expression in muscle
Black histogram of all genes on the chip, ranked
by relative expression Blue histogram of
microRNA targets, ranked by relative expression
6
Kolmogorov-Smirnoff Test
  • Measures the difference between two cumuluative
    distributions
  • P-values calculated by permutation

7
Kolmogorov-Smirnoff Test Demonstration
8
Control for length dinucleotide frequency
  • Two factors contribute to the likelihood of
    matching a target site
  • Length of UTR (affects the likelihood of matching
    a site in the UTR)
  • Dinucleotide frequency (affects the likelihood of
    matching any given 7mer within the UTR)
  • Probability of getting a match
  • 1-((1-prob_7mer)length)

9
Incorporating information from other genomes
  • Instead of looking for conservation of a motif
    across different UTRs, look for avoidance of the
    motif across different UTRs
  • Combine UTRs from orthologous mammalian UTRs
  • Need to control for long conserved regions

10
Building a tissue-specificity map
Create a matrix of tissues vs microRNAs. Each
entry in the matrix represents the p-value of the
microRNAs targets in the tissue, according to
the KS statistic. In this case, we are using
miR-7, which gives a strong signal for pituitary,
as well as weaker signals for other brain
tissues. Next, fill out the rest of the matrix
with the p-values for different microRNAs.
11
Tissue-specificity of microRNAs as revealed by
target avoidance
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