View
28
Download
1
Category
Tags:
Preview:
DESCRIPTION
ncRNA detection w/ multiple alignments. Comparative detection of ncRNA. Given a pairwise alignment, QRNA decides if it is RNA, coding or Other The key to detecting RNA is covarying mutations. Multiple alignment should provide more information on covarying mutations. RNAz. - PowerPoint PPT Presentation
Citation preview
March 2006 Vineet Bafna
ncRNA detection w/ multiple alignments
March 2006 Vineet Bafna
Comparative detection of ncRNA
• Given a pairwise alignment, QRNA decides if it is RNA, coding or Other
• The key to detecting RNA is covarying mutations.
• Multiple alignment should provide more information on covarying mutations.
March 2006 Vineet Bafna
RNAz
• Computes the probability of ncRNA in a multiple alignment.
• RNAz computes two ‘novel’ statistics: – Min. Free Energy of sequences (MFE)– Conserved secondary structure (SCI)
• Train an SVM using the following features– MFE– SCI– Mean pairwise identity– Number of sequences in the input
March 2006 Vineet Bafna
SCI
• Apply min. energy folding to a multiple alignment.
• The score of a pair of column is dependent upon base-pairing as well as compensatory mutations.
• Let EA denote the consensus fold energy.• Let E denote the average MFE of all sequences
– SCI = EA / E
– Claim : Low SCI is bad, high is good– Q: What is the SCI for diverged (random) sequences?– What is the SCI for identical sequences?
March 2006 Vineet Bafna
MFE
• Compute a z-score for a sequence with MFE=m
• Z = (m-)/• Instead of computing , by shuffling,
and computing (slow)• Use regression to predict , from
sequence length and base composition.
March 2006 Vineet Bafna
Non-linear classification
• The z-statistic and SCI capture different properties.
• Green is good (native), red is bad (shuffed).
• Is SCI a good statistic, given different levels of sequence identity?
March 2006 Vineet Bafna
Using RNAz to predict ncRNA
• Applying RNAz to conserved regions results in a discovery of 30k putative RNA.
• Is this list complete? Is it valid?
March 2006 Vineet Bafna
Structural Alignment
X07545 ..ACCCGGC.CAUA...GUGGCCG.GGCAA.CAC.CCGG.U.C..UCGUUM21086 ..ACCCGGC.CAUA...GCGGCCG.GGCAA.CAC.CCGG.A.C..UCAUGX05870 ..ACCCGGC.CACA...GUGAGCG.GGCAA.CAC.CCGG.A.C..UCAUUU05019 ..ACCCGGU.CAUA...GUGAGCG.GGUAA.CAC.CCGG.A.C..UCGUUM16530 ..ACCCGGC.AAUA...GGCGCCGGUGCUA.CGC.CCGG.U.C..UCUUCX01588 ..ACCCGGU.CACA...GUGAGCG.GGCAA.CAC.CCGG.A.C..UCAUUAF034619 ...GGCGGC.CACA...GCGGUGG.GGUUGCCUC.CCGU.A.C..CCAUCL27170 AGUGGUGGC.CAUA...UCGGCGG.GGUUC.CUCCCCGU.A.C..CCAUC
X05532 AGGAACGGC.CAUA...CCACGUC.GAUCG.CAC.CACA.U.C..CCGUC#=GC <<<<<<<<<........<<.<<<<.<...<.<...<<<<.<.<.......
Conserved sequences, and conserved structure are more apparent in multiple alignments.
March 2006 Vineet Bafna
RNA multiple alignments
• Detection of RNA depends upon reliable prediction of covarying mutations, as well as regions of conserved sequence
• Precomputing multiple alignments based on sequence considerations is probably not sufficient (should be tested).
• How can structural alignments be computed?
March 2006 Vineet Bafna
Computing Structural Alignments
• Analogy: In sequence alignment, the score for aligning a column is position independent.
• In profiles, or HMMs, position specific scoring is used to distinguish conserved positions from non-conserved positions
• Similar ideas can be used for RNA.
G U G G C C GG C G G C C GG U G A G C GG U G A G CG G C G C C GG U G A G C GG C G G U G GU C G G C G GC C A C G U C
1
321
3
4
2
Pr(G|1) = 0.8
March 2006 Vineet Bafna
Covariance models=RNA profiles
AAAAU
UUU-A
AAAU-
---AU
S
W1
a W2
W3 b
a W4 b :
:
a W’2 b
Terminal symbols correspond to columns
March 2006 Vineet Bafna
Aligning a sequence to a covariance model
• We align each node of the covariance model (it is tree like, but may be a graph).
• The alignment score follows the same recurrence as in Lecture 7, but with position specific probabilities.
• Example:– A[Wi,(i,j)] = -log (Pr[Wi->s[i] Wj s[j] )+A[Wj,(i+1,j-1)]
• If we wish to compute the probability that a sequence belongs to a family, we compute the total likelihood (sum over all probabilities)
• If we wish to compute the structure of an unknown sequence by comparison to a covariance model, we compute the max likelihood parse in this graph.
March 2006 Vineet Bafna
Covariance models and ncRNA discovery
• Given a family of ncRNA sequences, scan a genomic sequence with a covariance model and retrieve all high scoring sub-sequences.
• This is the most common method, but it is expensive.
• Assume covariance model has m states, and the substring has at most n symbols, and the database has L symbols.
• Alignment cost = O(n2m1+n3m2)• Total time =?
March 2006 Vineet Bafna
Computing covariance models
• If we are given a CM, a multiple structural alignment is ‘easy’. – In turn, align each sequence to the CM.
• If we are given a multiple alignment, computing the covariance model is easy
• For simultaneous prediction, a Bayesian iterative approach is used– Compute a seed alignment– Use the alignment to compute a CM– Use the CM to compute a new alignment– Iterate
March 2006 Vineet Bafna
Open
• Compute a structural multiple alignment.• Existing methods do not work well without
good seed alignment, and require excessive hand curation.
• Here, we solve a simpler problem– Predict conserved structure in unaligned sequences.
March 2006 Vineet Bafna
Motivation to a new approach
– Base-pairs appear in ‘clusters’: we call them stacks, which is energetically favorable.
– Most of the stability of the RNA secondary structure is determined by stacks.
ACCUU AAGGA
p = (1/4)5 < 0.001.
March 2006 Vineet Bafna
Statistics of the stacks in Rfam database
• Most base-pairs are stacked up
Fraction of true stacks missed
00.10.20.30.40.50.60.70.80.9
1
1 2 3 4 5 6 7 8 9 10
length of stacks
March 2006 Vineet Bafna
Using stacks as anchors for predictions
• The idea of anchors as constraints has been used in multiple genomic sequence alignment.
– MAVID (Bray and Pachter, 2004)– TBA (Blanchette et al., 2004)
Several heuristic methods have been developed by finding anchored stacks:
– Waterman (1989) used a statistical approach to choose conserved stacks within fixed-size windows.
– Ji and Stormo (2004) and Perriquet et al. (2003) use primary sequence conservation of the stacks and the length of loop regions to reduce the searching space.
– stack anchor has low sequence similarity.
– It’s hard to find correct anchors
March 2006 Vineet Bafna
Problem
• Selecting one stack at a time may cause wrong matching stacks.
March 2006 Vineet Bafna
A global approach: configuration of stacks
• RNA secondary structure can be viewed as stacks plus unpaired loops. (no individual base-pairs)
• The energy of the structure is the sum of the energies of stacks and loops.
• Stack configuration:– Nested stacks– Parallel stacks– Crossing stacks (pseudo
knots)• More generalized stacks can
include mismatches in the stacks.
March 2006 Vineet Bafna
RNA Stack-based Consensus Folding (RNAscf) problem
• Find conserved stack configurations for a set of unaligned RNA sequence.
• Optimize both stability (free energy) of the structure and sequence similarity computed based on these common stacks as anchors.
March 2006 Vineet Bafna
RNA stack-based consensus folding for pairwise sequences
March 2006 Vineet Bafna
A matching stack-configurations on two sequences
Weights of different costs.Energy of the consensus structureSequence similarity of stacksSequence similarity of unpaired regions
March 2006 Vineet Bafna
RNA Stack-based Consensus Folding for multiple sequences
March 2006 Vineet Bafna
Cost function for multiple sequences
A1,1 A1,2 A1,3 A1,4 A1,5 A1,6 A1,k-2 A1,k-1 A1,k
…
…
…
...
A2,1 A2,2 A2,3 A2,4 A2,5 A2,6 A2,k-2 A2,k-1 A2,k
As,1 As,2 As,3 As,4 As,5 As,6 As,k-2 As,k-1 As,k
March 2006 Vineet Bafna
Compute an optimal stack configuration for two sequences
• Dynamic programming algorithm is used to align RNA sequences and find an optimal configuration at the same time.
– The algorithm is similar to prior work (Sankoff 1985, Bafna et al. 1995)
– Differences: • We use stacks as the basic structural elements. • Prior work used individual base pairs.
– The computational time is O(n4) (n is the number of stacks). • Sankoff’s algorithm is O(m6), (m is the length of the sequences).• The number of possible stacks (size >= 4) is much smaller than
the length of the sequence.• It’s much faster.
March 2006 Vineet Bafna
For any pair of stacks, there are three choices:
PA
PB
hairpin loop
PA
PB
Loop(PA)
Loop(PB)PA
PB
PX
PY
interior loop/bulge
PA
PB
PiA
PjB
P1A
P1B
multi-loop
March 2006 Vineet Bafna
The score of matching stacks:
PA
PB
March 2006 Vineet Bafna
The score of matching hairpin loops:
PA
PB
Loop(PA)
Loop(PB)
March 2006 Vineet Bafna
The score of matching interior loops or bulges:
PA
PB
PX
PY
Loop(PX,PA)
Loop(PY,PA)
March 2006 Vineet Bafna
The score of matching two multi-loops:
PA
PB
PiA
PjB
P1A
P1B
Loop(Pi,PA)
Loop(Pi,PB)
March 2006 Vineet Bafna
Consensus folding for multiple sequences
• We use a heuristic method based on the notion of star-alignment.
– Compute an optimal configuration from a random seed pair.– Align all individual sequences to this configuration.– Choose the conserved stack configuration in all sequences.– Allow some stacks to be partially conserved (at least appear in a certain
fraction of the sequences).
March 2006 Vineet Bafna
Compute the stack configuration for multiple sequences: RNAscf(k,h,f)
.
..
.........
March 2006 Vineet Bafna
Iterative procedure for RNAscf
1. P = RNAscf(k, h, f).2. In each sequence, extract the unpaired regions according to the loop regions in P.3. Predict additional putative stacks that are not crossing with P using smaller k’ and h’.4. Recompute the alignment for with additional putative stacks using RNAscf(k’,h’,f).
March 2006 Vineet Bafna
Test dataset
• We choose a set of 12 RNA families from Rfam database:– 20 sequences chosen from the families. (except for CRE and glms, we
choose 10 sequences) with annotated structures.– There are 953 stacks.– We compare RNAscf with 3 other programs that are available online for
RNA folding:• RNAfold (energy based minimization) (Hofacker 2003)• COVE (covariance model) (Eddy and Durbin 1994)
– Cove need a staring seed alignment which is produced by ClustalW.• comRNA (computing anchors in multiple sequences) (Ji, Xu and Stormo 2004).
– Sensitivity: the fraction of true stacks that overlapped with predicted stacks.
– Accuracy: the fraction of predicted stacks that overlapped with true stacks
March 2006 Vineet Bafna
Test results
March 2006 Vineet Bafna
Test results
Sensitivity
00.10.20.30.40.50.60.70.80.9
1
5s_rRNACRE_220(*)
ctRNA_236(+)
glmS(*)hammer_3intron_II(+)
lysinepurinesam_ribo
thiamine(+)
tRNA
ykok_element
RNAfold
COVE
comRNA
RNAscf
March 2006 Vineet Bafna
Test results
Accuracy
00.10.20.30.40.50.60.70.80.9
1
5s_rRNACRE_220(*)
ctRNA_236(+)
glmS(*)hammer_3intron_II(+)
lysinepurinesam_ribo
thiamine(+)
tRNA
ykok_element
RNAfold
COVE
comRNA
RNAscf
March 2006 Vineet Bafna
Performance improves when the number of sequences increases
0.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0 10 20 30 40 50 60 70 80
# of input sequences
Sensitivity
Accuracy
(Using Thiamine riboswitch subfamily (RF00059))
March 2006 Vineet Bafna
RNAscf always finds the right consensus stack configuration.
(Sam riboswitch (RF00162))
March 2006 Vineet Bafna
Conclusion and future work
• RNAscf is a valid approach to RNA consensus structure prediction.– Use stack configuration to represent RNA secondary
structure.– Propose a dynamic programming algorithm to find optimal
stack configuration for pairwise sequences.– Use both primary sequence information and energy
information.– Use a star-alignment-like heuristic method to get the
consensus structure for multiple sequences.
March 2006 Vineet Bafna
Conclusion
• There is a signal due to to covarying mutations that is a good predictor of RNA structure.
• Can RNAscf scores be used as a statistic to discover ncRNA in ‘unaligned’ sequences?
• How good are sequence based alignments? Do they preserve structure?– Not for diverged families– Possibly for orthologous regions
March 2006 Vineet Bafna
ncRNA discovery for specific families
March 2006 Vineet Bafna
Case study: miRNA
• dsRNA, and siRNA can be used to silence genes in mammalian tissue culture.
• miRNA is a new member of this class of endogenous interfering RNA
• RNA interference (RNAi) is a pwerful new technique to study gene function.
March 2006 Vineet Bafna
Case Study: miRNA
• ncRNA ~22 nt in length• Pairs to sites within the 3’ UTR,
specifying translational repression.• Similar to siRNA (involved in RNAi)• Unlike siRNA, miRNA do not need
perfect base complementarity• No computational techniques to
predict miRNA• Most predictions based on cloning
small RNAs from size fractionated samples
March 2006 Vineet Bafna
miRNA (vs. siRNA)
• Derived from transcripts that form local hairpin structures.
• Sequences of the precursor, and processed miRNA is evolutionarily conserved
• Usually distinct, and distant, from other genes• siRNA (by contrast)
• Not evolutionarily conserved• Correspond to sequences of known or predicted mRNAs,
transposons, or regions of heterochromatic DNA.
March 2006 Vineet Bafna
MiRscan
• Predicts miRNA • Start with evolutionarily conserved region. Ex:
C. elegans and C. briggsae• 36000 hairpins were found (including 50/53
known miRNA).• 50 known miRNA were used to train and score
the 36000 hairpins
March 2006 Vineet Bafna
Computational identification of miRNA
• 7 features are scored1. miRNA base-pairing2. Base-pairing of the rest of the
fold-back3. Stringent sequence conservation
in the 5’ end of fold back4. Sequence conservation in the 3’
end of fold back5. Sequence bias in the first 5 bases
of miRNA6. Tendency to form symmetric
internal loops7. Presence of 2-9 consensus base-
pairs between miRNA and terminal loop region
• Red: Conserved with C. briggsae• Blue: varying residues that maintain their
predicted paired or unpaired states
March 2006 Vineet Bafna
MiRscan scoring
• 35 previously unannotated hairpins exceeded the Median score
March 2006 Vineet Bafna
Molecular identification of miRNA
• Initial cloning and sequencing identified 300 clones representing 54 unique miRNA
• 10 fold scale up of the procedure identified 3423 clones as miRNA. These contain 77 distinct miRNA genes
• 77-54=23 novel miRNAs found• 20 were scored by MiRscan (yellow). 10
were among the top 35
March 2006 Vineet Bafna
MiRscan results
• 35 Predictions• 10 identified with a high throughput screen
(sequencing of 3423 clones)• 6 identified using a PCR assay.• 4 identified as false positives PCR hybridized to
larger ncRNAs• 15 unknown• Evolutionary conservation is important for ncRNA
detection• >97% of all miRNA had significant conservation between
C. briggsae, and C. elegans
Recommended