1. Phylogenetic Reconstruction based on RNA Secondary Structural Alignment Benny Chor, Tel-Aviv Univ. Joint work with Moran Cabili, Assaf Meirovich, and Metsada Pasmanik-Chor

2. Phylogenetic Trees Based on What ? Morphology (1800 - ) Single gene sequence (DNA or AA) (1960 - )

3. Phylogenetic Trees Based on What ? Whole genomes (2002 - )

4. 1. Find a reliable metric between pairs of objects. 2.Design / choose / modify a good algorithm for determining metric (pairwise distances). 3.Compute distance matrix. 4.Construct a Neighbor Joining tree from the distance matrix. 5. As a sanity check, compare resulting tree to “standard & accepted” ones. NJ More Sources to Base Phylogeny On? A Proposed, Metric Induced Approach

5. Metric Induced Approach Was already applied (fairly successfully), e.g. for constructing phylogenies based on whole genomes/proteomes (Burstein et al., 2005), and others, based on metabolic networks (Tuller et al., 2006). Of course distances that are appropriate to each domain must be applied (or especially designed). NJ

6. Can phylogenetic reconstruction be based on RNA secondary structures ? Our Question

7. Answer: Yes, And Even Quite Well Archaea Eukarya Bacteria Our tree, based on secondary structs. of 16s rRNA from 91 species

8. 1.Find an efficient alignment algorithm (similarity based) pair-wise RNA secondary structures. 2. Transform similarity to distance. 3.Use RNA databases to get the RNA molecules and structures. Apply the algorithm to compute the distance for each pair of molecules. 4. Run NJ to produce trees. Metric Induced Approach: Specifics

9. -We chose to use RSmatch: A sophisticated dynamic programming algorithm, based on the “dot bracket” representation of the secondary structure. J. Liu, J.T. Wang, J. Hu, B. Tian. BMC Bioinformatics 2005, 6:89. -RSmatch sorts each dot and bracket to components, and then compares components according to their order in the secondary structure. -RSmatch employs both sequences and structures. -Complexity: O(nm), where n and m are the lengths of the two RNA molecules that are compared. TAATTATCGGAAGCAGTGCCTTCCATAATTA ( ( ( ( ( ( (. ( ( ( ( (...... ) ) ) ) ) ) ) ) ) ) ) ) The Alignment Algorithm Chosen

10. From Similarity to Distance In transforming the scoring matrix from similarity to distance, we tried to preserve the ratios between mismatches values, and of course lower similarity should imply higher distance. Distance metric requirements: Symmetry, Δ inequality, non negativity, self distance=0

11. Actual Distance Matrices: Higher Mismatch Penalties at “Dots” AUCGGCGUUAUG AU0110.5 CG100.5111 GC10.50111 GU0.5110 UA0.511 0 UG0.511 0 ACGU A0222 C2022 G2202 U2220 - Gap cost : 3 per nucleotide involved. - Δ inequality : mismatch < 2* gap cost

12. DBs of Reliable Secondary Struc. RNaseP DB: http://www.mbio.ncsu.edu/RNaseP/ Sequences length: ~300 - 400 (+/-) nucleotides DBs constructed with manual intervention RNaseP function: Cleaves off an extra, or precursor, sequence of RNA on tRNA molecules. 16S rRNA: Comparative RNA Web Site: http://www.rna.icmb.utexas.edu/ Sequences length: ~1,500 (+/-) nucleotides 16S function: In charge of tRNA binding and formation of peptide bonds during translation.

13. Our results …ahhm… trees

14. RNaseP Tree, 51 Species Secondary structure based tree Good partition to 3 kingdoms. Bacteria (characterized by Bxy) also look good.

15. RNaseP 51 Species Sequence based tree Eukarya Bacteria Archaea Eukaryotes are not monophyletic (yeast external).

16. 16s rRNA – 20 Species Secondary structure based tree Fungi Bacillariophyta Viridaeplanatae Mammalia Amphibia

17. 16s rRNA – 91 Species Secondary structure based tree Eukarya Bacteria Archaea

18. After completing this project, we discovered a related, earlier work from David Penny’s group. When determining evolutionary relationships between some catalytic RNA molecules, they constructed a 16S rRNA tree based on a similar “distance approach”. We compared our results to the trees published in their article (using a different distance algorithm, RNAdistance, by Shapiro & Zhang). Collins et al., 2000

19. Collins et al., 2000. Collins’ 16s rRNA sequence based tree Collins’ 16s r RNA secondary struct based tree 16 Species Bacteria Archaea Bacteria Archaea

20. Our Tree, 13 Out of 16 Collins’ Species Secondary structure based tree Archaea Bacteria

21. A Close Look at the Trees Collins’ 16s rRNA seq based tree Our 16s second. struct. tree Collins’ 16s second. struct. based tree outgroups

22. A Close Look at Sec. Strucs. Supports a “Thermoplasma Outgroup” Theory MethanobacteruimMethanococcusThermoplasma

23. Conclusions 1.Encouraging results 2.Accuracy of structure based trees is comparable to sequence based trees. 3.Warning: Reliable secondary structures are crucial for accurate tree reconstruction.