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Tracking down ncRNAs in the genomes

Tracking down ncRNAs in the genomes. How to find ncRNA gene. The stability of ncRNA secondary structure is not sufficiently different from the predicted stability of a random sequence. [Rivas and Eddy Bioinformatics (2000)]. RNA secondary structure prediction problem.

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Tracking down ncRNAs in the genomes

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  1. Tracking down ncRNAs in the genomes

  2. How to find ncRNA gene • The stability of ncRNA secondary structure is not sufficiently different from the predicted stability of a random sequence. [Rivas and Eddy Bioinformatics (2000)].

  3. RNA secondary structure prediction problem • Algorithms/programs to compute the minimum energy: • Nussinov et al (1978), Waterman (1978), Smith and Waterman (1978), and Zuker and Sankoff (1984). • Mfold (Zuker 2003) and RNAfold (ViennaRNA) (Hofacker 2003). • RNA folding via energy minimization has its shortcomings: • Prediction depends on correct energy parameters. • Sometimes, the true structure does not have the minimum energy.

  4. How to find ncRNA genes from a multiple alignment • RNAs with similar functions often have similar structures. • Sequence changes can be tolerated by covarying mutations. GCAUCGGugGUUCagu--gguaGAAU---//---CCGAUGCa UCUAAUAugGCAUauu-----aGUGC---//---UAUUAGAa GGGGAUGuaGCUUagu--gguaGAGC---//---UAUCCCCa GCCGCCGuaGCUCagcccgggaGAGC---//---CGGCGGCa GGGCCCGuaGCUUagcucgguaGAGC---//---CGGGCCCa

  5. RNAalifold • If we have correct multiple alignments, looking for covarying mutations and finding consensus structure is a good way to do structure prediction. • RNAalignfold (Hofacker et al. 2002) • The consensus structure prediction is more accurate. • To find energetically stable consensus structure is more statistically significant. • Still compute the MFE. • Covariance information is incorporated into the energy model by rewarding compensatory and consistent mutations.

  6. Loop based energy models • Stacks (contiguous nested base pairs) are the dominant stabilizing force – contribute the negative energy • Unpaired bases form loops contribute the positive energy. • Hairpin loops, bulge/internal loops, and multiloops. • Take into account covariance contribution: • Take into account inconsistent sequences: • Put together:

  7. Mountain representation of E. coli 16 S rRNA

  8. How to used it to detect the new ncRNA? • To find energetically stable consensus structure is more statistically significant. • MFE can be used to compute the statistical significance. • MFE: m • Mean: ų • Standard: δ • Z-score: z = (m- ų)/ δ • We need randomize the multiple sequence alignment • Shuffle the columns of the input alignment • Not destroy the gap structure. • Certain sequence pattern.

  9. Alifoldz

  10. Distribution of z-scores for the tRNA test sets

  11. Sensitivity depends sequence divergence and the quality of alignment (Based on pairwise alignments of SRP RNAs)

  12. Sensitivity on known ncRNAs in S. cerevisiae • Use MultiPipMaker to generate the multiple alignment ofS. cerevisiae and other 6 related yeast genome. • Extracted the regions of annotated ncRNAs • Refine the poor aligned regions • Window size = 150, slide 20. • False-positive rate: 0.25%. • 30 CPU days.

  13. Some issues about Alifoldz • Time consuming to compute the z-score • If the sequence identity is too high (>95%), it would not work. • If the sequence identity is too low (<60%), it would not work too.

  14. RNAz (PNAS, 2005) • z-score (for individual sequence) • Using Support Vector Machine (SVM) regression. • Using 1000 random sequences of each of ~10,000 point to compute the distribution. • Same length. • Same base composition. • Mean (ų) and standard deviation (δ) • Are functions of the length and base composition. • Z-score: z = (m- ų)/ δ • For an alignment, using the mean of the z-scores.

  15. z-scores calculated by SVM vs. sampled z-scores

  16. Structure conservation index (SCI) • It is a measure for the structure conservation based on the computing a consensus structure • Using RNAalifold. • If SCI -> 0, RNAalifold does not find a consensus structure. • If SCI-> 1, having conserved consensus structure.

  17. Classification based on both scores • Estimate a probability (P) if the alignment is classified as a functional RNA, based on • SCI • z-score • Average pairwise identity • Number of sequences. • It is also done by SVM.

  18. Classification based on z scores and SCI by a SVM

  19. Some test families

  20. Comparison with other methods

  21. Screening the Comparative Regulatory Genomics (CORG) Database

  22. Using RNAz screening human genome • Nature Biotechnology  23, 1383 - 1390 (Nov. 2005), “Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome” • Input: • Genome-wide alignments of vertebrates from UCSC genome browser. • Using PhastCons program to find the most conserved • Adjacent conserved regions (<50 distances) are joined together. • All regions > 50 bps. • Remove all “known genes” and “Refseq genes” • Output: • Predicted structured RNA elements in the human genomes using RNAz

  23. Results

  24. Statistical analysis of predicted RNAs

  25. Comparison with some RNA database

  26. Problem: • Correctly aligning multiple and divergent RNA sequences without taking into account the structural information is difficult. • To get covarying mutation, we need an alignment. • Do the multiple alignment and structure prediction at the same time.

  27. RNA consensus folding problem RNA consensus folding problem: computing the common secondary structure for a set of unaligned RNA sequences • Sankoff (1985) first proposed an algorithm simultaneously align RNA sequences and find the optimal common fold. • Time complexity is 0(n6) for two sequences with length n. • Implemented as Dynalign (Mathews and Turner 2002) • It’s not practical for multiple sequences. • Eddy and Durbin (1994) and other groups used stochastic context-free grammars to predict the consensus structure. • Start from a seed alignment. • Stochastic iteration to improve the alignment and predict structure. • Need a good seed alignment.

  28. p = (1/4)5 < 0.001. ACCUU AAGGA 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. • Stacks are much less likely to occur by chance.

  29. Statistics of the stacks in Rfam database

  30. 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

  31. Problem: • Selecting one stack at a time may cause wrong matching stacks.

  32. 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.

  33. 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.

  34. RNA stack-based consensus folding for pairwise sequences

  35. Sequence similarity of unpaired regions Weights of different costs. Sequence similarity of stacks Energy of the consensus structure A matching stack-configurations on two sequences

  36. RNA Stack-based Consensus Folding for multiple sequences

  37. … 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 Cost function for multiple sequences

  38. 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.

  39. PA Loop(PA) PA Loop(PB) PB PA PX hairpin loop PA P1A PiA PB PY PB interior loop/bulge P1B PjB PB multi-loop For any pair of stacks, there are three choices:

  40. The score of matching stacks: PA PB

  41. PA Loop(PA) Loop(PB) PB The score of matching hairpin loops:

  42. The score of matching interior loops or bulges: Loop(PX,PA) PA PX PY PB Loop(PY,PA)

  43. The score of matching two multi-loops: Loop(Pi,PA) PA PiA P1A P1B PjB PB Loop(Pi,PB)

  44. 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 leastappear in a certainfraction of the sequences).

  45. . . . . . . . . . . . . Compute the stack configuration for multiple sequences: RNAscf(k,h,f)

  46. Iterative procedure for RNAscf • P = RNAscf(k, h, f). • In each sequence, extract the unpaired regions according to the loop regions in P. • Predict additional putative stacks that are not crossing with P using smaller k’ and h’. • Recompute the alignment for with additional putative stacks using RNAscf(k’,h’,f).

  47. 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

  48. Test results

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