1. Discovery of RNA Structural Elements Using Evolutionary Computation Authors: G. Fogel, V. Porto, D. Weekes, D. Fogel, R. Griffey, J. McNeil, E. Lesnik, D. Ecker, R. Sampath, Natural Selection Inc. and Ibis Therapeutics Presenter: Elena Zheleva April 2, 2004

2. Introduction  Problem Statement  Background  Evolutionary Computation Population initialization Variation Fitness Selection  Results  Conclusion

3. Problem Statement  Computational Biology problem: given a RNA secondary structure description, search for similar secondary structures  Currently, exhaustive search techniques are used to narrow down search space  Authors focus on presentation and set of operators to search via evolution

4. Outline  Problem Statement  Background  Evolutionary Computation Population initialization Variation Fitness Selection  Results  Conclusion

5. Background  RNA (ribonucleic acid) directs middle steps of protein production single-stranded, certain parts are folded  RNA Secondary Structure - accounts for diverse functional activities

6. Background  RNA Secondary Structure: Recurs in multiple genes within a single organism Recurs in across the same gene in several organism  Why a computational tool for RNA secondary structure search? Discover new structures Improve understanding of functional and regulatory relationships amongst related RNAs

7. Background – RNAMotif  RNAMotif: mines nucleotide sequence databases for repeating structure motifs  RNAMotif Input: descriptor contains details about pairing information, length, sequence

8. Background - RNAMotif  RNAMotif Output: list of real structures   RNAMotif may return a very high number of motifs when descriptor is more flexible  Input to the EA: RNAMotif Output

9. Outline  Problem Statement  Background  Evolutionary Computation Population initialization Variation Fitness Selection  Results  Conclusion

10. Evolutionary Computation Population Initialization  P parent bins  B – bin size  Bin = a contending solution  Each bin contains structures from different organisms  Structures chosen at random from RNAMotif Output file Figure 1

11. Outline  Problem Statement  Background  Evolutionary Computation Population initialization Variation Fitness Selection  Results  Conclusion

12. Evolutionary Computation Variation  P parent bins are copied to O offspring bins  Variables: operator, number of times to apply it  Variation Operator 1: structure replacement within a specified organism Replacement taken from RNAMotif Output File Local – neighboring replacement structure Global – random replacement structure Example: P organisms = {H. Sapiens, S. Scrofa, E. Coli, G. Gallus}

13. Evolutionary Computation Variation  Variation Operator 2: Structure replacement from different organisms Variable: # of structures to be replaced Example: # = 2 P organisms = {H. Sapiens, S. Scrofa, E. Coli, G. Gallus} O organisms = {H. Sapiens, C. Griseus, E. Coli, S. Scrofa}

14. Evolutionary Computation Variation  Variation Operator 3: random single-point bin recombination Generates a second parent from RNAMotif output and applies single-point bin recombination Chooses randomly one of the two offsprings Example: P = {H, S, E, G} P = {D, E, O, B} O = {H, S, E, B} O = {D, E, O, G}  Variation Operator 4: random multi-point bin recombination

15. Outline  Problem Statement  Background  Evolutionary Computation Population initialization Variation Fitness Selection  Results  Conclusion

16. Evolutionary Computation Fitness  Fitness Function Scoring Components: Structure nucleotide sequence similarity Structure length similarity Structure thermodynamic stability similarity  These measures are applied pairwise by each structural component and summed into a final bin score

17. Outline  Problem Statement  Background  Evolutionary Computation Population initialization Variation Fitness Selection  Results  Conclusion

18. Evolutionary Computation Selection  Selection: For every bin in population, A set of R rival bins is randomly selected Calculate score = # rivals with lower fitness  Lower bins are removed  Iterations continue until number of generations (G) or CPU time is satisfied, or until expected change of fitness/gen  0

19. Outline  Problem Statement  Background  Evolutionary Computation Population initialization Variation Fitness Selection  Results  Conclusion

20. Results  Experiment 1: 7.6x10 possible bins Exhaustive search: 125 days  EA examined ~10 bins before converging < 3 minutes 8 4

21. Results

22.  To test the utility of this method Run on newly discovered genomes (S. Pyogenes) Compare to database which has an alignment for this RNA secondary structure for previously discovered genomes (S. Mutans) Found similar sequence and structure to close organisms

23. Outline  Problem Statement  Background  Evolutionary Computation Population initialization Variation Fitness Selection  Results  Conclusion

24. Conclusion  Evolutionary Algorithm can be applied to find RNA secondary structures over a wide range of organisms  Converges quickly and reliably  Algorithm comes up with a solution which contains information about structural elements for different organisms/genomes