1. Multiply Aligning RNA Sequences
-Phylogeny
-SAR
-Re-Sequencing
Cédric Notredame
Comparative Bioinformatics Group
Bioinformatics and Genomics Program

2. Open Questions in Multiple Sequence Alignments
Aligning Protein Sequences
Aligning RNA Sequences

3. Accurately Aligning Protein Sequences
Remains Challenging with sequences less than 20% identity
These sequences can be structurally homologues
Correct alignments can help discovering functional sites
Expresso/3D-Coffee is currently the most accurate way of combining sequence and structural information
Available on

4. Comparing ncRNAs

5. ncRNAs Comparison And ENCODE said…
“nearly the entire genome may be represented in primary transcripts that extensively overlap and include many non-protein-coding regions”
Who Are They?
tRNA, rRNA, snoRNAs,
microRNAs, siRNAs
piRNAs
long ncRNAs (Xist, Evf, Air, CTN, PINK…)
How Many of them
Open question
is a common guess
Harder to detect than proteins .

6. Detecting ncRNAs in silico: a long way to go…
RNAse P (Not in ENCODE)

7. UCSC RFAM prediction Search (CMsearch) Genome RNAalifold RFAM
Lizard GG--TGGAGACTAGTCTGAATTGGGTTATGAAG--CCA--
Rat GGCGG--GGGAGAGTAGTCTGAATTGGGTTATGAGG--CCC--
Hedgehog GACGG--GGGAGAGTAGTCTGAATTAGGTTATGGGG--CCC--
Shrew GACGG-CGGGAGAGTAGTCTGAATTGGGTTATGAGG--CCC--
Medaka GTGAG--TGGAGAGTAGTCTGAATTGGGT TCT--
X.tropicalis AGCGG-CGGGAGAGTAGTCTGACTTGGGTTATGAGG--TGC--
Cat GACGG--GGGAGAGTAGTCTGAATTGGGTTATGAGGCCCCC--
Dog
Rhesus GGCGG--GGGAGAGTAGTCTGAATTGGGTTATGAGG--TCC--
Mouse GGCGG--GGGAGAGTAGTCTGAATTGGGTTATGAGG--CCC--
Chimp GGCGG--AGGAGAGTAGTCTGAATTGGGTTATGAGG--TCC--
Human GGCGG--AGGAGAGTAGTCTGAATTGGGTTATGAGG--TCC--
TreeShrew GCGCG--GGGAGAGTAGTCTGAATTGGGTTATGAGG--CCC--
UCSC
RFAM
prediction
RNAalifold
RFAM
Search (CMsearch)
Genome

8. Results for RNase P UCSC Predicted Nothing RFAM OK Mammalian alignment
Vertebrate alignment
Structure
Results
UCSC
Predicted
Nothing
RFAM OK
Matthias Zytneki

9. Results for RNase P Better Alignments = Better Predictions
Qualitative
Improvement
Matthias Zytneki
Thomas Derrien
Roderic Guigo
Ramin Shiekhattar
Quantitative
Improvement

10. ncRNAs can have different sequences and Similar Structures

11. ncRNAs Can Evolve Rapidly
GAACGGACC
CTTGCCTGG G A C
CTTGCCTCC
GAACGGAGG G A C
CCAGGCAAGACGGGACGAGAGTTGCCTGG
CCTCCGTTCAGAGGTGCATAGAACGGAGG
** *--**---*-**------**

12. ncRNAs are Difficult to Align
Same Structure Low Sequence Identity
Small Alphabet, Short Sequences  Alignments often Non-Significant

13. Obtaining the Structure of a ncRNA is difficult
Hard to Align The Sequences Without the Structure
Hard to Predict the Structures Without an Alignment

14. The Holy Grail of RNA Comparison: Sankoff’ Algorithm

15. The Holy Grail of RNA Comparison Sankoff’ Algorithm
Simultaneous Folding and Alignment
Time Complexity: O(L2n)
Space Complexity: O(L3n)
In Practice, for Two Sequences:
50 nucleotides: 1 min M.
100 nucleotides 16 min M.
200 nucleotides hours G.
400 nucleotides 3 days 3 T.
Forget about
Multiple sequence alignments
Database searches

16. The next best Thing: Consan
Consan = Sankoff + a few constraints
Use of Stochastic Context Free Grammars
Tree-shaped HMMs
Made sparse with constraints
The constraints are derived from the most confident positions of the alignment
Equivalent of Banded DP

17. Going Multiple….
Structural Aligners

18. Game Rules Using Structural Predictions
Produces better alignments
Is Computationally expensive
Use as much structural information as possible while doing as little computation as possible…

19. Adapting T-Coffee To RNA Alignments

20. T-Coffee and Concistency…

21. T-Coffee and Concistency…

22. T-Coffee and Concistency…

23. T-Coffee and Concistency…

24. Consistency: Conflicts and InformationX Y X Y X X Z Z Y Y W Z W Z
Y-Z is unhappy
X-W is unhappy Y Z X W Y W X Z Y Z X W
Partly Consistent 
Less Reliable
Fully Consistent 
More Reliable

25. R-Coffee: Modifying T-Coffee at the Right Place
Incorporation of Secondary Structure information within the Library
Two Extra Components for the T-Coffee Scoring Scheme
A new Library
A new Scoring Scheme

26. Progressive Alignment Using The R-Score RNAplfold
RNA Sequences
Secondary
Structures
Primary Library
R-Coffee Extended
Progressive Alignment
Using The R-Score
RNAplfold
Consan or
Mafft / Muscle / ProbCons
R-Coffee
Extension
R-Score

27. R-Coffee Extension
TC Library C G
G G Score X
C C Score Y C G C G C G
Goal: Embedding RNA Structures Within The T-Coffee Libraries
The R-extension can be added on the top of any existing method.

28. R-Coffee Scoring Scheme
R-Score (CC)=MAX(TC-Score(CC), TC-Score (GG)) C G C G

29. Validating R-Coffee

30. RNA Alignments are harder to validate than Protein Alignments
Protein Alignments  Use of Structure based Reference Alignments
RNA Alignments No Real structure based reference alignments
The structures are mostly predicted from sequences
Circularity

31. BraliBase and the BraliScore
Database of Reference Alignments
388 multiple sequence alignments.
Evenly distributed between 35 and 95 percent average sequence identity
Contain 5 sequences selected from the RNA family database Rfam
The reference alignment is based on a SCFG model based on the full Rfam seed dataset (~100 sequences).

32. BraliBase SPS Score Number of Identically Aligned Pairs SPS=
RFam
MSA
SPS=
Number of Aligned Pairs

33. BraliBase: SCI Score R N A p f o l d RNAlifold Average DG Seq X Cov
Covariance
(((…)))…((..)) DG Seq1
(((…)))…((..)) DG Seq2
(((…)))…((..)) DG Seq3
(((…)))…((..)) DG Seq4
(((…)))…((..)) DG Seq5
(((…)))…((..)) DG Seq6
RNAlifold
Average DG Seq X Cov
SCI=
(((…)))…((..)) ALN DG
DG ALN

34. BRaliScore
Braliscore= SCI*SPS

35. R-Coffee + Regular Aligners
Method Avg Braliscore Net Improv.
direct +T +R +T +R
Poa Pcma Prrn ClustalW Mafft_fftnts ProbConsRNA Muscle Mafft_ginsi
Improvement= # R-Coffee wins - # R-Coffee looses

36. RM-Coffee + Regular Aligners
Method Avg Braliscore Net Improv.
direct +T +R +T +R
Poa Pcma Prrn ClustalW Mafft_fftnts ProbConsRNA Muscle Mafft_ginsi
RM-Coffee / / 84

37. R-Coffee + Structural Aligners
Method Avg Braliscore Net Improv.
direct +T +R +T +R
Stemloc Mlocarna Murlet Pmcomp T-Lara Foldalign
Dyalign
Consan
RM-Coffee / / 84

38. How Best is the Best…. Method vs. R-Coffee-Consan RM-Coffee4 Poa
241 ***
217 ***
T-Coffee
199 ***
Prrn
232 ***
198 ***
Pcma
218 ***
151 ***
Proalign
216 ***
150 **
Mafft fftns
206 ***
148 *
ClustalW
203 ***
136 ***
Probcons
192 ***
128 *
Mafft ginsi
170 ***
115
Muscle
169 ***
111
M-Locarna
234 ***
183 **
Stral
169 *** 62
FoldalignM
146 61
Murlet
130 *
-12
Rnasampler
129 *
-27
T-Lara
125 *
-30

39. Range of Performances
Effect of
Compensated
Mutations

40. Split Alignments and RNA
Few of the new long RNAs are reported with a secondary structure
Two explanations
They do not have a secondary structure
It is hard to predict the structure
To predict the structure
One needs an Homologues to build an MSA
To find homologues one needs to find them

41. Split Alignments and RNA
-Protein Split Alignments
-Guided by Primary structure
Transcript
genome

42. Split Alignments and RNA
CCAGGCAAGACGGGACGAGAGTTGCCTGG
CCTCCGTTC
AGAGGTGCATA
GAACGGAGG

43. Split Alignments and RNA
Homology appears through secondary structures
One needs to evaluate all possible secondary structures
Very computationaly intensive

44. Conclusion/Future Directions
T-Coffee/Consan is currently the best MSA protocol for ncRNAs
Testing how important is the accuracy of the secondary structure prediction
Going deeper into Sankoff’s territory: predicting and aligning simultaneously
Solving the split alignment problem

45. Credits and Web Servers
Andreas Wilm (UCD)
Des Higgins (UCD)
Sebastien Moretti (SIB)
Ioannis Xenarios (SIB)
Matthias Zytneki (CRG)
Thomas Derrien (CRG)
Roderic Guigo (CRG)
Ramin Shiekhattar (CRG)
CGR, SIB, UCD