Finding, Aligning and Analyzing Non Coding RNAs

1. Finding, Aligning and AnalyzingNon Coding RNAs Cédric Notredame Comparative Bioinformatics Group Bioinformatics and Genomics Program

2. They are Everywhere… • 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 • 30.000 is a common guess • Harder to detect than proteins .

3. Searching “…When Looking for a Needle in a Haystack, the optimistic Wears Gloves…”

4. ncRNAs can have different sequences and Similar Structures

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

6. ncRNAs are Difficult to Align CCAGGCAAGACGGGACGAGAGTTGCCTGG CCTCCGTTCAGAGGTGCATAGAACGGAGG **-------*--**---*-**------** Regular Alignment --CCAGGCAAGACGGGACGAGAGTTGCCTGG CCTCCGTTCAGAGGTGCATAGAACGGAGG-- * * *** * * *** *

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

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

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

10. The Holy Grail of RNA ComparisonSankoff’ Algorithm • Simultaneous Folding and Alignment • Time Complexity: O(L2n) • Space Complexity: O(L3n) • In Practice, for Two Sequences: • 50 nucleotides: 1 min. 6 M. • 100 nucleotides 16 min. 256 M. • 200 nucleotides 4 hours 4 G. • 400 nucleotides 3 days 3 T. • Forget about • Multiple sequence alignments • Database searches

11. 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

12. Consan for Databases: Infernal • Infernal is a Faster version of Consan • For Database Search • Sill Very Slow Receiver operating characteristic (ROC) Comparison of Infernal with BLAST

13. Consan for Databases: Infernal • BLAST: 360 s. • Fast Infernal: 182 000 s. • Slow Infernal: 5 320 000 s.

14. Searching Databases for New RNAs

15. Rfam: In practice • Rfam contains RNA families • Families  Multiple Sequence Alignment  Models • Models are like Pfam Profiles • Use Consan or Cmsearch rather than HMMer • Much Slower • Too expensive to search the models • Models are used to build Rfam • People usually BLAST Rfam

16. Where do Rfam Families Come From? • Infernal Requires a Model • Models requires an MSA • The MSA requires a Family • It all starts with a BlastN Rfam, Gardner et al. NAR 2008

17. Can we make BlastN more accurate ? • BlastN is not very accurate because: • Poor substitution models for Nucleic Acids • Low information density (4 symbols) • BlastN assumes • Equal evolution rates for all nucleotides • Independence form Neighbors

18. Love Thy Neighbor Measured Nearest Neighbor Dependencies on Rfam sequences

19. High Rate of CpG mutations

20. Measuring Di-Nucleotide Evolution • Each Nucleotide can be made more informative • It can incorporate the “name” of its Neighbor • AA => a • AG => b • AC => c • AT => d • … • A 16 Letter alphabet can be used to recode all nucleotide sequences • We name these extended Nucleotides

22. Blosum-R and eRNA

23. Substitutions ?? • How much does it cost to turn one nucleotide into another one ? • Blosum/Pam style matrix • Matrices estimated on Rfam families

24. Blosum-R and eRNA

25. Using BlastR • When Nucleic Acids look like Proteins • They can be aligned with Protein Methods • BlastN  BlastP • BlastP with eRNA is BlastR

26. Validating Blast-R

27. Benchmarking BlastR PP Query PN E V A L U E S Blast Rfam

28. Benchmarking BlastR Blast Rfam 001 Rfam 001 ROC Blast Rfam 002 Rfam 002 Blast Rfam … Rfam …

29. Benchmarking BlastR False Positives Bad Good True Positive Good Bad

30. Benchmarking BlastR False Positives Bad Good Area Under Curve Small AUC  Better True Positive

31. BlastR vs The World

32. The 3 Components of Blast R • BlastP is better than BlastN • BlosumR makes BlastP a little bit better Blast: wuBlast

33. The 3 Components of Blast R • BlastP is better than BlastN • BlosumR makes BlastP a little bit better • And Faster

34. BlastR and Clustering Sensitivity • Given all Rfam in Bulk • How good is BlastR at reconstituting all the families 1-Specificty

35. BlastR and Clustering Sensitivity • Given all Rfam in Bulk • How good is BlastR at reconstituting all the families 1-Specificty

36. BllastR: In Practice

37. BllastR: In Practice BlastR -20 E-Value Threshold: 10 BlastN

38. Take Home • Searching Nucleotides is Difficult • BlastN is not a very good algorithm • Simple Adaptations can improve the situation • Changing the algorithm (BlastP) • Changing the Scoring Scheme (BlastP-Nuc) • Changing the alphabet (BlastR)