Transcript Tutorial_9_NEW

Tutorial 9
RNA Structure Prediction
RNA Structure Prediction
• RNA secondary structure prediction
RNAfold, RNAalifold
• microRNA prediction
TargetScan –
Cool story of the day:
How viruses use miRNAs to attack humans
RNA secondary structure prediction
GGGCUAUUAGCUCAGUUGGUUA
GAGCGCACCCCUGAUAAGGGUGA
GGUCGCUGAUUCGAAUUCAGCAU
AGCCCA
Base pair
probability
RNA structure prediction by Vienna RNA
package
RNAfold server
Minimum free energy structures and base pair probabilities from
single RNA or DNA sequences.
RNAalifold server
Consensus secondary structures from an alignment of several
related RNA or DNA sequences. You need to upload an alignment.
http://rna.tbi.univie.ac.at/
RNAfold
• Gives best stabilized structure (structure with
minimal free energy (MFE))
• Uses a dynamic programming algorithm that
exploits base pairing and thermodynamic
probabilities in order to predict the most likely
structures of an RNA molecule.
RNAfold - input
RNA sequence
RNAfold - output
Minimal free energy
structure
Structure prediction
Free energy of the
ensamble
Best “average”
structure
Graphic representation
MFE
structure
An average,
may not
exist in the
ensemble
RNAalifold
Structure prediction based on alignments
Alignment
RNAalifold - output
Understanding the color scheme
C-G
C-G
U-A
C-G
C-G
C-G
G-C
U-A
A-U
C-C
http://www.almob.org/content/pdf/1748-7188-6-26.pdf
MicroRNAs
miRNA
gene
mature
miRNA
Target gene
MicroRNA in Cancer
Sun et al, 2012
The challenge for Bioinformatics:
- Identifying new microRNA genes
- Identifying the targets of specific microRNA
How to find microRNA genes?
Searching for sequences that fold to a hairpin ~70 nt
-RNAfold
-other efficient algorithms for identifying stem loops
Concentrating on intragenic regions and introns
- Filtering coding regions
Filtering out non conserved candidates
-Mature and pre-miRNA is usually evolutionary conserved
How to find microRNA genes?
A. Structure prediction
B. Evolutionary Conservation
Predicting microRNA targets
MicroRNA targets are located in 3’ UTRs, and complementing
mature microRNAs
•Why is it hard to find them ??
– Base pairing is required only in the seed sequence
(7-8 nt)
– Lots of known miRNAs have similar seed sequences
Very high probability to find by chance
mature miRNA
3’ UTR of Target gene
Predicting microRNA target genes
• General methods
- Find motifs which complements the seed
sequence (allow mismatches)
– Look for conserved target sites
– Consider the MFE of the RNA-RNA pairing
∆G (miRNA+target)
– Consider the delta MFE for RNA-RNA
pairing versus the folding of the target
∆G (miRNA+target )- ∆G (target)
http://www.targetscan.org/
Sum of phylogenetic
branch lengths
between species
that contain a site
More negative
scores represent
a more favorable
site
The stability of
of a miRNAtarget duplex
A score reflecting the
probability that a site
is conserved due to
selective
maintenance of
miRNA targeting
rather than by chance
or any other reason.
Mir 136
Mir 136 - conserved* microRNA
* conserved across most mammals, but usually not beyond placental mammals
How to evaluate our results
True positive (TP) = correctly identified
(miRNA targets correctly identified)
False positive (FP)= incorrectly identified
(non miRNA targets miss identified as targets)
True negative (TN)= correctly rejected
(non miRNA targets correctly not identified)
False negative (FN) = incorrectly rejected
(miRNA targets not identified)
Sensitivity (recall)= True positive Rate = TP /(TP+FN)
Specificity (precision)= True Negative Rate = TN /(TN+FP)
Cool Story of the day
How viruses use miRNAs to attack
humans?
The group developed an algorithm for predicting
miRNA targets and applied it to human
Cytomegalovirus (hcmv) miRNAs
MICB, an immunorelated gene, was among the
highest ranking predicted targets and the top
prediction for hcmv-miR-UL112.
They found that hcmv-miR-UL112 specifically
down-regulates MICB expression during viral
infection, leading to reduced killing
by Natural Killer cells (A human virus-defense
mechanism)
Natural Killer (NK) cells
• NK cells are cytotoxic lymphocyte that kill virus-infected cells
and tumor cells.
• In order to function they should be activated through
receptors. One of these is NKG2D.
• MICB is a stress-induced ligand of NK cells through the NKG2D
receptor).
Cerwenka et al. Nature Reviews Immunology 2001