1 / 14

Integrating Uncertainty in Distance-Matrix Phylogenetics

This study discusses incorporating uncertainty in distance-based methods for phylogenetic analysis, presenting variance models and two types of trees, ultrametric time tree and non-ultrametric divergence tree. It introduces a method that integrates both tree types coherently, leading to a rooted tree without requiring an outgroup. The approach involves agglomerative stage time tree and divergence tree, considering divergence additivity and regression models. Estimation is carried out simultaneously using Generalized Least Squares (GLS) method, focusing on accurate variance assumptions. The proposed method, named StatTree, is computationally efficient and suitable for unbalanced topologies.

jerome-grimes
Download Presentation

Integrating Uncertainty in Distance-Matrix Phylogenetics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Incorporating uncertainty in distance-matrix phylogenetics Wally Gilks Leeds University Tom Nye Newcastle University Pietro Liò Cambridge University Isaac Newton Institute December 17, 2007

  2. Distance-based methods • Larger trees • Faster algorithms • Less model-dependent • Genome-scale evolutionary rearrangements

  3. Agglomerative distance methods • NJ(Saitou and Nei, 1987) • BioNJ(Gascuel, 1997) • Weighbor(Bruno et al, 2000) • MVR(Gascuel, 2000) • FastME(Desper and Gascuel, 2004)

  4. A B C Variance models • Independent distances • Ordinary Least Squares (OLS) • Weighted Least Squares (WLS) • NJ, Weighbor, FastME • Correlated distances • shared evolutionary paths (Chakraborty, 1977) • computed from shared sequences: BioNJ • induced by estimation process (we show) • Generalised Least Squares (GLS) • Hasegawa (1985), Bulmer (1991),MVR A

  5. Two types of tree Ultrametric time tree Non-ultrametric divergence tree Time (mya) Divergence = “true distance” = integrated rate of evolution = path length Divergence 0 more evolution

  6. Which tree type to assume? • Ultrametric tree makes stronger assumptions • Different methods for estimating each type • But both types are in principle correct! • Our method coherently integrates both types • Produces rooted tree, no need for outgroup

  7. An agglomerative stage time tree divergence tree Time (mya) Divergence E C E A C A 0 D B D B

  8. Divergence additivity divergence tree and for X = C,D,… E C A D B

  9. parameters mean zero Distances are estimated divergences Regression model divergence tree and for X = C,D,… E C A D B

  10. time tree Time (mya) E C A parameter 0 D B mean zero uncorrelated Divergences are distorted times Random effects model

  11. controls noise function of clade A structure clade A size shared node A elapsed time Chakraborty (1977) Nei et al (1985) Bulmer (1991) controls distortion variance parameters Variance assumptions

  12. Estimation • Time tree and divergence tree are estimated simultaneously • by GLS (Hasegawa, 1985; Bulmer, 1991) • Choose most recent agglomeration always • Estimated divergences become the distances for the next stage • Variance formula accommodates estimation-induced correlations

  13. Notes • Can estimate variance parameters s2 and n • Computationally efficient algorithm • same time-complexity as BioNJ • we call it StatTree

  14. Simulations 16 taxa, unbalanced topology, 100 simulations

More Related