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Phylogeny Reconstruction from Experimental Data

Phylogeny Reconstruction from Experimental Data. Dean L. Zeller Dr. F. F. Dragan, advisor Kent State University April 7 th , 2006.

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Phylogeny Reconstruction from Experimental Data

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  1. Phylogeny Reconstruction from Experimental Data Dean L. Zeller Dr. F. F. Dragan, advisor Kent State University April 7th, 2006

  2. “…the great Tree of Life fills with its dead and broken branches the crust of the earth, and covers the surface with its ever-branching and beautiful ramifications.” Charles Darwin (1809-1882) Father of Evolution Phylogeny Reconstruction

  3. Outline • Goals of research • Evolution trees, phylogenies • Assumptions • Atlas of Evolution Trees • Genetic tests (hypothetical) • Phylogeny reconstruction methods • Future Work Phylogeny Reconstruction

  4. Goals of Research Specific Goals • Create methods of phylogeny reconstruction from various hypothetical tests. • Make use of and create more adequate evolution models. • Create “teachable” lessons on bioinformatics suitable for a mid-level computer science, mathematics, or biology class. Long Term Goals • Discover methods of phylogeny reconstruction from a new perspective. • Educate the next generation of computational biologists. Phylogeny Reconstruction

  5. Evolution Tree example Phylogeny Reconstruction

  6. Evolution Tree example Phylogeny Reconstruction

  7. Evolution Tree (theoretical) Phylogeny Reconstruction

  8. Assumptions • By making simple assumptions, the problem complexity is greatly reduced. • Redundant nodes removed • Multiple splits nodes replaced with isomorphic approximations • Only consider isomorphically unique trees Phylogeny Reconstruction

  9. Assumption #1 • Redundant nodes are removed without loss of data. • It is already assumed the species is slowly changing over time. It does not add to the problem to consider a single point along the way. Phylogeny Reconstruction

  10. Assumption #2 • Multiple split nodes replaced with isomorphic approximations • Some loss of data, but greatly reduces the problem complexity Phylogeny Reconstruction

  11. Assumption #3 • Isomorphically unique trees Phylogeny Reconstruction

  12. Atlas of Evolution Trees (5 leaves) Phylogeny Reconstruction

  13. Atlas of Evolution Trees (6 leaves) Phylogeny Reconstruction

  14. Genetic Tests • At this point, all tests are purely hypothetical. • Plausible results can be converted from existing tests. • Binary Two-Species Test (BTST) • Discrete Two-Species Test (DTST) • Continuous Two-Species Test (CTST) • Closer Relative Three-Species Test (CRTST) Phylogeny Reconstruction

  15. Binary Two-Species Test (BTST) • Returns 1 if species x and y are genetically close to a certain degree, and 0 otherwise. • Data collected to form a similarity grid and distance graph (k-leaf root). Phylogeny Reconstruction

  16. Step 1 – Difference Summary Table a b c d e f a 1 1 0 0 0 b 1 0 0 0 c 1 0 0 d 1 1 e 1 f Reconstruction from BTST Step 2 – k-leaf root Step 3 – phylogeny Phylogeny Reconstruction

  17. Reconstruction from BTST • Linear time solution exists for k = 3 [Br05a] • … and k = 4 [Br05b] • An open problem for k 5 • Severely limits analysis capability. Phylogeny Reconstruction

  18. Discrete Two-Species Test (DTST) • Returns a discrete value (k=2,3,4,…) denoting distance between x and y in tree. • Test can be converted from existing tests. • Data collected to form a distance grid. • Create distance graphs incrementally. Phylogeny Reconstruction

  19. Difference Summary Table a b c d e f a 2 3 5 6 6 b 3 5 6 6 c 4 5 5 d 3 3 e 2 f Reconstruction from DTST k 3 k 2 k 4 k 5 k 6 Phylogeny Reconstruction

  20. Reconstruction from DTST Distance  2 Direct Neighbors Distance  3 Close relatives Distance  4 Tree complete Phylogeny Reconstruction

  21. Continuous Two Species Test (CTST) • Returns a continuous value d denoting distance between x and y in tree. • Data collected to form a distance grid. • Tree reconstructed in ascending order of closeness. • Highest degree of accuracy required Phylogeny Reconstruction

  22. Distance Summary Table a b c d e f a 1.96 3.64 7.31 9.07 11.65 b 3.51 7.64 12.34 10.71 c 5.90 8.21 7.99 d 4.73 4.63 e 2.31 f Reconstruction from CTST Phylogeny Reconstruction

  23. Reconstruction from CTST Diff(a,b) 1.96 Make connection  Diff(e,f) 2.31 Make connection  Diff(b,c) 3.51 Make connection  Diff(a,c) 3.64Connection previously established Diff(d,f) 4.63 Make connection  Diff(d,e) 4.73Connection previously established Diff(c,d) 5.90 Make connection , STOP -- All species included in tree Phylogeny Reconstruction

  24. Actual CTST data Source: [Fe04] Phylogeny Reconstruction

  25. Phylogeny Reconstruction Reconstruction from CTST results in the following tree: Phylogeny Reconstruction

  26. CTST Results, part 1 • Use the correlation statistical measurement to determine relationship between data used to create tree and distance data created by tree. (>0.8 is “strong”.) data distance chimp  human 0.27 2 gorilla  human 0.31 3 gorilla  chimp 0.35 3 orang  gorilla 0.46 3 orang  human 0.47 4 orang  chimp 0.51 4 gorilla  human 0.56 5 gibbon  gorilla 0.60 5 gibbon  chimp 0.62 5 gibbon  orang 0.71 3 Correlation: 0.64 (positive relationship exists) Note: if gibbon  orang was 5 instead of 3, the correlation would be 0.93. Phylogeny Reconstruction

  27. CTST Results, part 2 • Use the correlation statistical measurement to determine relationship between remaining data and distance data created by tree. data distance mouse  chimp 1.44 6 mouse  gorilla 1.45 5 mouse  human 1.46 6 mouse  orang 1.48 4 mouse  gibbon 1.52 3 bovine  gorilla 1.52 6 bovine  human 1.59 7 bovine  chimp 1.60 7 bovine  orang 1.66 5 bovine  mouse 1.67 3 bovine  gibbon 1.72 4 Correlation: -.24 (weak negative relationship) Phylogeny Reconstruction

  28. CTST Conclusions • Relationship is statistically significant for the lower data values resulting in species close on resulting phylogeny, but is weak for data values further away. • There are stronger methods of phylogeny reconstruction, but this serves as a good starting point. Phylogeny Reconstruction

  29. Closer Relative Three-Species Test (CRTST) • Returns one of two possible trees on three species. • Use the Merge Partial Evolution Trees [Li99] algorithm to reconstruct phylogeny. • Allows for multiple species evolution. Phylogeny Reconstruction

  30. Results from CRTST data Phylogeny Reconstruction

  31. Reconstruction from CRTST Phylogeny Reconstruction

  32. Reconstruction from CRTST Phylogeny Reconstruction

  33. Reconstruction from CRTST Phylogeny Reconstruction

  34. Reconstruction from CRTST Phylogeny Reconstruction

  35. Literature Review of Related Methods • Additive and Ultrametric Trees [Wu04] • Minimum Increment Evolution Tree (MEIT) [Wu04] • Evolutionary Tree Insertion with Minimum Increment (ETIMI) [Wu04] • Maximum Homeomorphic Agreement Subtree (MHT) [Ga97] • Maximum Agreement Subtree (MAST) [Ga97] • Maximum Inferred Consensus Tree (MICT) [Li99] • Maximum Inferred Local Consensus Tree (MILCT) [Li99] • Balanced Randomized Tree Splitting (BRTS) [Ka99] • Merging Partial Evolution Trees (MPET) [Li99] Phylogeny Reconstruction

  36. Atlas of Distance Graphs • Inspired by An Atlas of Graphs[Re99] • Elegant yet simple way to analyze graphs and trees • Apply same style to phylogenies and distance graphs. Phylogeny Reconstruction

  37. Atlas of Distance Graphs k=2 k=2 k=3 k=2 k=3 k=4 Phylogeny Reconstruction

  38. Atlas of Distance Graphs k=2 k=3 k=4 k=2 k=3 k=4 k=5 Phylogeny Reconstruction

  39. k = 2 k = 3 k = 4 k = 5 k = 6 k = 7 k = 8 Distance Graph Simulator a b i h c d g e f Graph complete Phylogeny Reconstruction

  40. Class Assignments • Assignment 1 – Drawing Trees • Assignment 2 – Phylogenetic Distance Graphs • Assignment 3 – Phylogeny Reconstruction (Tested on CS10051 students, Spring 2006) Phylogeny Reconstruction

  41. Future Work • Additional bioinformatics class assignments • Atlas of Phylogenetic Distance Graphs • Implement the Phylogeny Reconstruction Simulator using NetworkX • Remove redundant node and isomorphic approximation assumptions • Apply to all nodes in tree instead of just the leaves Phylogeny Reconstruction

  42. References [Br05a] Brandstädt, A., V.B. Le, and R. Sritharan (2005). “Structure and Linear Time Recognition of 4-Leaf Powers”, Unpublished manuscript. [Br05b] Brandstädt, A. and V. B. Le (2005). “Structure and Linear Time Recognition of 3-Leaf Powers”, Unpublished manuscript. [Fe04] J. Felsenstein (2004). Inferring Phylogenies, Sinauer Associates, Inc. [Ga97] L. Gąsieniec, J. Jansson, A. Lingas, and A. Östlin (1997), “On the complexity of computing evolutionary trees,” Proceedings of Computing and Combinatonics Third Annual International Conference COCOON ’97, Shanghai, China, pp. 134 to 145, Aug 97. [Ka99] Y. Kao, A. Lingas, and A. Östlin (1999), “Balanced Randomized Tree Splitting with Applications to Evolutionary Tree Constructions,” Proceedings of the 16th Annual Symposium on Theoretical Aspects of Computer Science, Trier, Germany, pp. 184 to 196, March 1999. [Li99] A. Lingas, H. Olsson, and A. Östlin (1999), “Efficient Merging, Construction, and Maintenance of Evolutionary Trees,” Proceedings of the 26th International Colloquium on Automata, Languages, and Programming (ICALP) ’99, Prague, Chech Republic, pp. 544 to 553, July 1999. [Re99] Read, R.C. and R.J. Wilson (1999). An Atlas of Graphs, Oxford Science Publications. [Wu04] Wu, B.Y. and K.M. Chao (2004). Spanning Trees and Optimization Problems. Chapman & Hall/CRC. Phylogeny Reconstruction

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