1. March 2006Vineet Bafna ncRNA detection w/ multiple alignments

2. March 2006Vineet 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.

3. March 2006Vineet 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

4. March 2006Vineet 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 E A denote the consensus fold energy. Let E denote the average MFE of all sequences – SCI = E A / 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?

5. March 2006Vineet 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.

6. March 2006Vineet 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?

7. March 2006Vineet 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?

8. March 2006Vineet Bafna Structural Alignment X07545..ACCCGGC.CAUA...GUGGCCG.GGCAA.CAC.CCGG.U.C..UCGUU M21086..ACCCGGC.CAUA...GCGGCCG.GGCAA.CAC.CCGG.A.C..UCAUG X05870..ACCCGGC.CACA...GUGAGCG.GGCAA.CAC.CCGG.A.C..UCAUU U05019..ACCCGGU.CAUA...GUGAGCG.GGUAA.CAC.CCGG.A.C..UCGUU M16530..ACCCGGC.AAUA...GGCGCCGGUGCUA.CGC.CCGG.U.C..UCUUC X01588..ACCCGGU.CACA...GUGAGCG.GGCAA.CAC.CCGG.A.C..UCAUU AF034619...GGCGGC.CACA...GCGGUGG.GGUUGCCUC.CCGU.A.C..CCAUC L27170 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.

9. March 2006Vineet 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?

10. March 2006Vineet 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 G G C G G C C G G U G A G C G G U G A G C G G C G C C G G U G A G C G G C G G U G G U C G G C G G C C A C G U C 1 321 3 4 2 Pr(G|1) = 0.8

11. March 2006Vineet Bafna Covariance models=RNA profiles AAAAUAAAAU UUU-AUUU-A AAAU-AAAU- ---AU---AU S W 1 a W 2 W 3 b a W 4 b : a W’ 2 b Terminal symbols correspond to columns

12. March 2006Vineet 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[W i,(i,j)] = -log (Pr[W i ->s[i] W j s[j] )+A[W j,(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.

13. March 2006Vineet 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(n 2 m 1 +n 3 m 2 ) Total time =?

14. March 2006Vineet 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

15. March 2006Vineet 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.

16. March 2006Vineet 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. ACCUUAAGGA p = (1/4) 5 < 0.001.

17. March 2006Vineet Bafna Statistics of the stacks in Rfam database Most base-pairs are stacked up

18. March 2006Vineet 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

19. March 2006Vineet Bafna Problem Selecting one stack at a time may cause wrong matching stacks.

20. March 2006Vineet 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.

21. March 2006Vineet 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.

22. March 2006Vineet Bafna RNA stack-based consensus folding for pairwise sequences

23. March 2006Vineet Bafna A matching stack-configurations on two sequences Weights of different costs. Energy of the consensus structure Sequence similarity of stacks Sequence similarity of unpaired regions

24. March 2006Vineet Bafna RNA Stack-based Consensus Folding for multiple sequences

25. March 2006Vineet Bafna Cost function for multiple sequences A 1,1 A 1,2 A 1,3 A 1,4 A 1,5 A 1,6 A 1,k-2 A 1,k-1 A 1,k … … …... A 2,1 A 2,2 A 2,3 A 2,4 A 2,5 A 2,6 A 2,k-2 A 2,k-1 A 2,k A s,1 A s,2 A s,3 A s,4 A s,5 A s,6 A s,k-2 A s,k-1 A s,k

26. March 2006Vineet 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(n 4 ) (n is the number of stacks). Sankoff’s algorithm is O(m 6 ), (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.

27. March 2006Vineet Bafna For any pair of stacks, there are three choices: PAPA PBPB hairpin loop PAPA PBPB Loop(P A ) Loop(P B ) PAPA PBPB PXPX PYPY interior loop/bulge PAPA PBPB PiAPiA PjBPjB P1AP1A P1BP1B multi-loop

28. March 2006Vineet Bafna The score of matching stacks: PAPA PBPB

29. March 2006Vineet Bafna The score of matching hairpin loops: PAPA PBPB Loop(P A ) Loop(P B )

30. March 2006Vineet Bafna The score of matching interior loops or bulges: PAPA PBPB PXPX PYPY Loop(P X,P A ) Loop(P Y,P A )

31. March 2006Vineet Bafna The score of matching two multi-loops: PAPA PBPB PiAPiA PjBPjB P1AP1A P1BP1B Loop(P i,P A ) Loop(P i,P B )

32. March 2006Vineet 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).

33. March 2006Vineet Bafna Compute the stack configuration for multiple sequences: RNAscf(k,h,f)............

34. March 2006Vineet 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).

35. March 2006Vineet 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

36. March 2006Vineet Bafna Test results

37. March 2006Vineet Bafna Test results

38. March 2006Vineet Bafna Test results

39. March 2006Vineet Bafna Performance improves when the number of sequences increases (Using Thiamine riboswitch subfamily (RF00059))

40. March 2006Vineet Bafna RNAscf always finds the right consensus stack configuration. (Sam riboswitch (RF00162))

41. March 2006Vineet 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.

42. March 2006Vineet 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

43. March 2006Vineet Bafna ncRNA discovery for specific families

44. March 2006Vineet 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.

45. March 2006Vineet 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

46. March 2006Vineet 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.

47. March 2006Vineet 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

48. March 2006Vineet Bafna Computational identification of miRNA 7 features are scored 1. miRNA base-pairing 2. Base-pairing of the rest of the fold-back 3. Stringent sequence conservation in the 5’ end of fold back 4. Sequence conservation in the 3’ end of fold back 5. Sequence bias in the first 5 bases of miRNA 6. Tendency to form symmetric internal loops 7. 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

49. March 2006Vineet Bafna MiRscan scoring 35 previously unannotated hairpins exceeded the Median score

50. March 2006Vineet 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

51. March 2006Vineet 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