Title: Evolution of MicroRNA Targets
1Evolution of MicroRNA Targets
2Nonconserved 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?
3MicroRNA 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?
4MicroRNAs are tissue specific
- Many microRNAs have highly tissue specific
profiles - Expression analysis underestimates specificity of
miRNAs due to combining tissues
5Can 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
6Kolmogorov-Smirnoff Test
- Measures the difference between two cumuluative
distributions - P-values calculated by permutation
7Kolmogorov-Smirnoff Test Demonstration
8Control 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)
9Incorporating 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
10Building 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.
11Tissue-specificity of microRNAs as revealed by
target avoidance