Discovery of RNA Structural Elements Using Evolutionary Computation

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
• lt 3 minutes

8
4
21
Results
22
Results

• 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