Se ha denunciado esta presentación.
Se está descargando tu SlideShare. ×

Accommodating clustered divergences in phylogenetic inference

Ad

Accommodating
clustered divergences in
phylogenetic inference
Jamie R. Oaks1,2
1Department of Biology, University of
Washi...

Ad

Phylogenetics is rapidly
progressing as an endeavor
of statistical inference
c 2007 Boris Kulikov boris-kulikov.blogspot.c...

Ad

Phylogenetics is rapidly
progressing as an endeavor
of statistical inference
“Big data” present exciting
possibilities and...

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Eche un vistazo a continuación

1 de 92 Anuncio
1 de 92 Anuncio

Más Contenido Relacionado

Similares a Accommodating clustered divergences in phylogenetic inference (20)

Accommodating clustered divergences in phylogenetic inference

  1. 1. Accommodating clustered divergences in phylogenetic inference Jamie R. Oaks1,2 1Department of Biology, University of Washington 2Department of Biological Sciences, Auburn University October 21, 2015 c 2007 Boris Kulikov boris-kulikov.blogspot.com Clustered diversification Jamie Oaks – phyletica.org 1/27
  2. 2. Phylogenetics is rapidly progressing as an endeavor of statistical inference c 2007 Boris Kulikov boris-kulikov.blogspot.com Clustered diversification Jamie Oaks – phyletica.org 2/27
  3. 3. Phylogenetics is rapidly progressing as an endeavor of statistical inference “Big data” present exciting possibilities and computational challenges c 2007 Boris Kulikov boris-kulikov.blogspot.com Clustered diversification Jamie Oaks – phyletica.org 2/27
  4. 4. Phylogenetics is rapidly progressing as an endeavor of statistical inference “Big data” present exciting possibilities and computational challenges Exciting opportunities to develop new ways to study biology in the light of phylogeny c 2007 Boris Kulikov boris-kulikov.blogspot.com Clustered diversification Jamie Oaks – phyletica.org 2/27
  5. 5. Current state of phylogenetics Clustered diversification Jamie Oaks – phyletica.org 3/27
  6. 6. Current state of phylogenetics Assumption: Divergences are independent across the tree Clustered diversification Jamie Oaks – phyletica.org 3/27
  7. 7. Current state of phylogenetics Assumption: Divergences are independent across the tree We know this assumption is frequently violated Clustered diversification Jamie Oaks – phyletica.org 3/27
  8. 8. Current state of phylogenetics Assumption: Divergences are independent across the tree We know this assumption is frequently violated Clustered diversification Jamie Oaks – phyletica.org 3/27
  9. 9. Current state of phylogenetics Assumption: Divergences are independent across the tree We know this assumption is frequently violated Why account for this non-independence? Clustered diversification Jamie Oaks – phyletica.org 3/27
  10. 10. Current state of phylogenetics Assumption: Divergences are independent across the tree We know this assumption is frequently violated Why account for this non-independence? 1. Improve inference Clustered diversification Jamie Oaks – phyletica.org 3/27
  11. 11. Current state of phylogenetics Assumption: Divergences are independent across the tree We know this assumption is frequently violated Why account for this non-independence? 1. Improve inference 2. Provide a framework for studying processes of co-diversification Clustered diversification Jamie Oaks – phyletica.org 3/27
  12. 12. Current state of phylogenetics Assumption: Divergences are independent across the tree We know this assumption is frequently violated Why account for this non-independence? 1. Improve inference 2. Provide a framework for studying processes of co-diversification This is a model-choice problem Clustered diversification Jamie Oaks – phyletica.org 3/27
  13. 13. Divergence model choice τ1 T1 T2 T3 Clustered diversification Jamie Oaks – phyletica.org 4/27
  14. 14. Divergence model choice τ1 T1 T2 T3 Clustered diversification Jamie Oaks – phyletica.org 4/27
  15. 15. Divergence model choice τ2 τ1 T1 T2 T3 Clustered diversification Jamie Oaks – phyletica.org 4/27
  16. 16. Divergence model choice τ1τ2 T1 T2 T3 Clustered diversification Jamie Oaks – phyletica.org 4/27
  17. 17. Divergence model choice τ1τ2 T1 T2 T3 Clustered diversification Jamie Oaks – phyletica.org 4/27
  18. 18. Divergence model choice τ3 τ1τ2 T1 T2 T3 Clustered diversification Jamie Oaks – phyletica.org 4/27
  19. 19. Inferring co-diversification m1 m2 m3 m4 m5 τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  20. 20. Inferring co-diversification m1 m2 m3 m4 m5 τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 We want to infer m and T given DNA sequence alignments X J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  21. 21. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 We want to infer m and T given DNA sequence alignments X J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  22. 22. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 We want to infer m and T given DNA sequence alignments X p(mi | X) ∝ p(X | mi )p(mi ) J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  23. 23. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 We want to infer m and T given DNA sequence alignments X p(mi | X) ∝ p(X | mi )p(mi ) p(X | mi ) = θ p(X | θ, mi )p(θ | mi )dθ J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  24. 24. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 We want to infer m and T given DNA sequence alignments X p(mi | X) ∝ p(X | mi )p(mi ) p(X | mi ) = θ p(X | θ, mi )p(θ | mi )dθ Divergence times Gene trees Substitution parameters Demographic parameters J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  25. 25. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 Challenges: J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  26. 26. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 Challenges: 1. Cannot solve all the integrals analytically J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  27. 27. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 Challenges: 1. Cannot solve all the integrals analytically Numerical approximation via approximate-likelihood Bayesian computation (ABC) J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  28. 28. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 Challenges: 1. Cannot solve all the integrals analytically Numerical approximation via approximate-likelihood Bayesian computation (ABC) 2. Sampling over all possible models J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  29. 29. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 Challenges: 1. Cannot solve all the integrals analytically Numerical approximation via approximate-likelihood Bayesian computation (ABC) 2. Sampling over all possible models 5 taxa = 52 models J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  30. 30. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 Challenges: 1. Cannot solve all the integrals analytically Numerical approximation via approximate-likelihood Bayesian computation (ABC) 2. Sampling over all possible models 5 taxa = 52 models 10 taxa = 115,975 models J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  31. 31. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 Challenges: 1. Cannot solve all the integrals analytically Numerical approximation via approximate-likelihood Bayesian computation (ABC) 2. Sampling over all possible models 5 taxa = 52 models 10 taxa = 115,975 models 20 taxa = 51,724,158,235,372 models!! J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  32. 32. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 Challenges: 1. Cannot solve all the integrals analytically Numerical approximation via approximate-likelihood Bayesian computation (ABC) 2. Sampling over all possible models 5 taxa = 52 models 10 taxa = 115,975 models 20 taxa = 51,724,158,235,372 models!! A “diffuse” Dirichlet process prior (DPP) J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 5/27
  33. 33. “Easy” as ABC A A A G G G C C C C C C G G G G G G A A A A A T A A A A A A T T C C C C G G G G G G T T T T T T G G G G G G C C C T T T T T T C C C C C C C C C G G G G G G C C T T T T A A A A A A C C C C C C G G G G G G T T T T T T A A A G G G C C C C C C C C C C C C A A A T T T G G G G G G T T T T C C A A A A A A C C C C C C C C C T T T G G G G G G G G G G G G T T T T T T S1 S2 S3 Clustered diversification Jamie Oaks – phyletica.org 6/27
  34. 34. “Easy” as ABC A A A G G G C C C C C C G G G G G G A A A A A T A A A A A A T T C C C C G G G G G G T T T T T T G G G G G G C C C T T T T T T C C C C C C C C C G G G G G G C C T T T T A A A A A A C C C C C C G G G G G G T T T T T T A A A G G G C C C C C C C C C C C C A A A T T T G G G G G G T T T T C C A A A A A A C C C C C C C C C T T T G G G G G G G G G G G G T T T T T T S1 S2 S3 Clustered diversification Jamie Oaks – phyletica.org 6/27
  35. 35. “Easy” as ABC A A A G G G C C C C C C G G G G G G A A A A A T A A A A A A T T C C C C G G G G G G T T T T T T G G G G G G C C C T T T T T T C C C C C C C C C G G G G G G C C T T T T A A A A A A C C C C C C G G G G G G T T T T T T A A A G G G C C C C C C C C C C C C A A A T T T G G G G G G T T T T C C A A A A A A C C C C C C C C C T T T G G G G G G G G G G G G T T T T T T S1 S2 S3 Clustered diversification Jamie Oaks – phyletica.org 6/27
  36. 36. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Clustered diversification Jamie Oaks – phyletica.org 7/27
  37. 37. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Clustered diversification Jamie Oaks – phyletica.org 7/27
  38. 38. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Clustered diversification Jamie Oaks – phyletica.org 7/27
  39. 39. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Clustered diversification Jamie Oaks – phyletica.org 7/27
  40. 40. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Clustered diversification Jamie Oaks – phyletica.org 7/27
  41. 41. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Clustered diversification Jamie Oaks – phyletica.org 7/27
  42. 42. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Clustered diversification Jamie Oaks – phyletica.org 7/27
  43. 43. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Clustered diversification Jamie Oaks – phyletica.org 7/27
  44. 44. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Clustered diversification Jamie Oaks – phyletica.org 7/27
  45. 45. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 T1 T2 T3 τ2 τ1 T1 T2 T3 τ1τ2 T1 T2 T3 τ1τ2 T1 T2 T3 τ3 τ1τ2 T1 T2 T3 Challenges: 1. Cannot solve all the integrals analytically Numerical approximation via approximate-likelihood Bayesian computation (ABC) 2. Sampling over all possible models 5 taxa = 52 models 10 taxa = 115,975 models 20 taxa = 51,724,158,235,372 models!! A “diffuse” Dirichlet process prior (DPP) J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 9/27
  46. 46. Sampling divergence models—a novel approach The divergence models are ways of assigning our taxa to events Clustered diversification Jamie Oaks – phyletica.org 10/27
  47. 47. Sampling divergence models—a novel approach The divergence models are ways of assigning our taxa to events A Dirichlet process prior (DPP) model is a convenient and flexible solution Peter Dirichlet Clustered diversification Jamie Oaks – phyletica.org 10/27
  48. 48. Sampling divergence models—a novel approach The divergence models are ways of assigning our taxa to events A Dirichlet process prior (DPP) model is a convenient and flexible solution Common Bayesian approach to assigning variables to an unknown number of categories Peter Dirichlet Clustered diversification Jamie Oaks – phyletica.org 10/27
  49. 49. Sampling divergence models—a novel approach The divergence models are ways of assigning our taxa to events A Dirichlet process prior (DPP) model is a convenient and flexible solution Common Bayesian approach to assigning variables to an unknown number of categories Controlled by “concentration” parameter: α Peter Dirichlet Clustered diversification Jamie Oaks – phyletica.org 10/27
  50. 50. α α+2 1 α+2 1 α+2 α α+1 α α+2 2 α+2 1 α+1 Clustered diversification Jamie Oaks – phyletica.org 11/27
  51. 51. α α+2 1 α+2 1 α+2 α α+1 α α+2 2 α+2 1 α+1 Clustered diversification Jamie Oaks – phyletica.org 11/27
  52. 52. α α+2 1 α+2 1 α+2 α α+1 α α+2 2 α+2 1 α+1 Clustered diversification Jamie Oaks – phyletica.org 11/27
  53. 53. α α+2 1 α+2 1 α+2 α α+1 α α+2 2 α+2 1 α+1 Clustered diversification Jamie Oaks – phyletica.org 11/27
  54. 54. α α+1 α α+2 α α+2 α α+1 1 α+2 1 α+2 α α+1 1 α+21 α+2 α α+1 1 α+1 α α+2 α α+2 1 α+1 2 α+2 2 α+2 1 α+1 Clustered diversification Jamie Oaks – phyletica.org 11/27
  55. 55. α = 0.5 α α+1 α α+2 = 0.067 α α+2 α α+1 1 α+2 = 0.133 1 α+2 α α+1 1 α+2 = 0.1331 α+2 α α+1 1 α+1 α α+2 = 0.133 α α+2 1 α+1 2 α+2 = 0.5332 α+2 1 α+1 Clustered diversification Jamie Oaks – phyletica.org 11/27
  56. 56. α = 10.0 α α+1 α α+2 = 0.758 α α+2 α α+1 1 α+2 = 0.076 1 α+2 α α+1 1 α+2 = 0.0761 α+2 α α+1 1 α+1 α α+2 = 0.076 α α+2 1 α+1 2 α+2 = 0.0152 α+2 1 α+1 Clustered diversification Jamie Oaks – phyletica.org 11/27
  57. 57. New method: dpp-msbayes Flexible Dirichlet-process prior (DPP) over all possible divergence models J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 12/27
  58. 58. New method: dpp-msbayes Flexible Dirichlet-process prior (DPP) over all possible divergence models Flexible priors on parameters to avoid strongly weighted posteriors J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 12/27
  59. 59. New method: dpp-msbayes Flexible Dirichlet-process prior (DPP) over all possible divergence models Flexible priors on parameters to avoid strongly weighted posteriors Multi-processing to accommodate genomic datasets J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 12/27
  60. 60. dpp-msbayes: Simulation-based assessment Validation: Simulate 50,000 datasets and analyze each under the same model Clustered diversification Jamie Oaks – phyletica.org 13/27
  61. 61. dpp-msbayes: Simulation-based assessment Validation: Simulate 50,000 datasets and analyze each under the same model Robustness: Simulate datasets that violate model assumptions and analyze each of them Clustered diversification Jamie Oaks – phyletica.org 13/27
  62. 62. dpp-msbayes: Validation results 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Posterior probability of one divergence Trueprobabilityofonedivergence J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 14/27
  63. 63. dpp-msbayes: Robustness results 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Posterior probability of one divergence Trueprobabilityofonedivergence J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 15/27
  64. 64. dpp-msbayes: Performance New method for estimating shared evolutionary history shows: 1. Model-choice accuracy 2. Robustness to model violations 3. Power to detect variation in divergence times 4. It’s fast! J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 16/27
  65. 65. dpp-msbayes: Performance New method for estimating shared evolutionary history shows: 1. Model-choice accuracy 2. Robustness to model violations 3. Power to detect variation in divergence times 4. It’s fast! A new tool for biologists to leverage comparative genomic data to explore processes of co-diversification J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 16/27
  66. 66. Empirical applications Clustered diversification Jamie Oaks – phyletica.org 17/27
  67. 67. Empirical applications Did repeated fragmentation of islands during inter-glacial rises in sea level promote diversification? Clustered diversification Jamie Oaks – phyletica.org 17/27
  68. 68. Climate-driven diversification Clustered diversification Jamie Oaks – phyletica.org 18/27
  69. 69. Climate-driven diversification Clustered diversification Jamie Oaks – phyletica.org 18/27
  70. 70. Climate-driven diversification Clustered diversification Jamie Oaks – phyletica.org 18/27
  71. 71. Results 1 3 5 7 9 11 13 15 17 19 21 Number of divergence events 0.00 0.02 0.04 0.06 0.08 0.10 Posteriorprobability J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 19/27
  72. 72. Results 1 3 5 7 9 11 13 15 17 19 21 Number of divergence events 0.00 0.02 0.04 0.06 0.08 0.10 Posteriorprobability 0100200300400500 Time (kya) 0 -50 -100 Sealevel(m) J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Clustered diversification Jamie Oaks – phyletica.org 19/27
  73. 73. More data! Collecting genomic data from taxa co-distributed across Southeast Asian Islands and Mainland Clustered diversification Jamie Oaks – phyletica.org 20/27
  74. 74. More data! Collecting genomic data from taxa co-distributed across Southeast Asian Islands and Mainland Preliminary results for 1000 loci from 5 pairs of Gekko mindorensis populations 1 2 3 4 5 Number of divergence events, j¿j -5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.02ln(Bayesfactor) Clustered diversification Jamie Oaks – phyletica.org 20/27
  75. 75. Diversification across African rainforests Did climate cycles drive diversification and community assembly across rainforest taxa? Clustered diversification Jamie Oaks – phyletica.org 21/27
  76. 76. Diversification across African rainforests Did climate cycles drive diversification and community assembly across rainforest taxa? Preliminary results with 300 loci from 3 taxa 1 2 3 Number of divergence events, j¿j -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2ln(Bayesfactor) Clustered diversification Jamie Oaks – phyletica.org 21/27
  77. 77. Conclusions New method for estimating shared evolutionary history Shows good “frequentist” behavior Relatively robust to model violations Clustered diversification Jamie Oaks – phyletica.org 22/27
  78. 78. Conclusions New method for estimating shared evolutionary history Shows good “frequentist” behavior Relatively robust to model violations Finding support for temporally clustered divergences in multiple systems Clustered diversification Jamie Oaks – phyletica.org 22/27
  79. 79. Conclusions New method for estimating shared evolutionary history Shows good “frequentist” behavior Relatively robust to model violations Finding support for temporally clustered divergences in multiple systems However, there is a lot of uncertainty! Clustered diversification Jamie Oaks – phyletica.org 22/27
  80. 80. Current work: More power Full-likelihood Bayesian implementation 1 D. Bryant et al. (2012). Molecular Biology And Evolution 29: 1917–1932 Clustered diversification Jamie Oaks – phyletica.org 23/27
  81. 81. Current work: More power Full-likelihood Bayesian implementation Uses all the information in the data Applicable to deeper timescales 1 D. Bryant et al. (2012). Molecular Biology And Evolution 29: 1917–1932 Clustered diversification Jamie Oaks – phyletica.org 23/27
  82. 82. Current work: More power Full-likelihood Bayesian implementation Uses all the information in the data Applicable to deeper timescales Analytically integrate over gene trees 1 1 D. Bryant et al. (2012). Molecular Biology And Evolution 29: 1917–1932 Clustered diversification Jamie Oaks – phyletica.org 23/27
  83. 83. Current work: More power Full-likelihood Bayesian implementation Uses all the information in the data Applicable to deeper timescales Analytically integrate over gene trees 1 Very efficient numerical approximation of posterior Applicable to NGS datasets 1 D. Bryant et al. (2012). Molecular Biology And Evolution 29: 1917–1932 Clustered diversification Jamie Oaks – phyletica.org 23/27
  84. 84. Next step: A general framework Develop a framework for inferring shared divergences across phylogenies τ1τ2 T1 T2 T3 Clustered diversification Jamie Oaks – phyletica.org 24/27
  85. 85. Next step: A general framework Develop a framework for inferring shared divergences across phylogenies τ1τ2 T1 T2 T3 Clustered diversification Jamie Oaks – phyletica.org 24/27
  86. 86. Next step: A general framework Develop a framework for inferring shared divergences across phylogenies Generalize Bayesian phylogenetics to incorporate shared divergences τ1τ2 T1 T2 T3 Clustered diversification Jamie Oaks – phyletica.org 24/27
  87. 87. Next step: A general framework Develop a framework for inferring shared divergences across phylogenies Generalize Bayesian phylogenetics to incorporate shared divergences Sample models numerically via reversible-jump Markov chain Monte Carlo τ1τ2 T1 T2 T3 Clustered diversification Jamie Oaks – phyletica.org 24/27
  88. 88. Next step: A general framework Develop a framework for inferring shared divergences across phylogenies Generalize Bayesian phylogenetics to incorporate shared divergences Sample models numerically via reversible-jump Markov chain Monte Carlo Benefits: Improve phylogenetic inference Framework for studying processes of co-diversification τ1τ2 T1 T2 T3 Clustered diversification Jamie Oaks – phyletica.org 24/27
  89. 89. Everything is on GitHub. . . Software: dpp-msbayes: https://github.com/joaks1/dpp-msbayes PyMsBayes: https://joaks1.github.io/PyMsBayes ABACUS: Approximate BAyesian C UtilitieS. https://github.com/joaks1/abacus Open-Science Notebook: msbayes-experiments: https://github.com/joaks1/msbayes-experiments Clustered diversification Jamie Oaks – phyletica.org 25/27
  90. 90. Acknowledgments Ideas and feedback: Leach´e Lab Minin Lab Holder Lab Brown Lab/KU Herpetology Computation: Funding: Photo credits: Rafe Brown, Cam Siler, Jesse Grismer, & Jake Esselstyn FMNH Philippine Mammal Website: D.S. Balete, M.R.M. Duya, & J. Holden PhyloPic! Clustered diversification Jamie Oaks – phyletica.org 26/27
  91. 91. Questions? joaks@auburn.edu c 2007 Boris Kulikov boris-kulikov.blogspot.com Clustered diversification Jamie Oaks – phyletica.org 27/27

×