Guenomu – a Bayesian supertree program for species tree reconstruction

Leonardo de Oliveira Martins

Diego Mallo

David Posada

● leomrtns@uvigo.es

● dmallo@uvigo.es

● dposada@uvigo.es

Guenomu – a Bayesian supertree program for species tree reconstruction

Leonardo de Oliveira Martins

Diego Mallo

David Posada

● leomrtns@uvigo.es

● dmallo@uvigo.es

● dposada@uvigo.es

With help from Klaus Schliep, Mateus Patricio and Nicolas Lartillot

D1

D2

G1 S

G2

Model for the evolution of gene families

.

.

.

Dn

Gn

Model for the evolution of gene families

Our assumption:

We just need to consider the simplest

explanation for the difference between

the gene and species trees

D1

G1 S

Model for the evolution of gene families

distance between G and S

P(G

/S)

Our assumption:

We just need to consider the simplest

explanation for the difference between

the gene and species trees

D1

G1 S

Steel and Rodrigo 2008(doi:10.1080/10635150802033014)

Model for the evolution of gene families

Our assumption:

We just need to consider the simplest

explanation for the difference between

the gene and species trees

● we may use several such simple explanations

D1

G1 S

distance between G and S

P(G

/S)

Steel and Rodrigo 2008(doi:10.1080/10635150802033014)

Model for the evolution of gene families

Our assumption:

We just need to consider the simplest

explanation for the difference between

the gene and species trees

● we may use several such simple explanations

● work with unrooted gene trees

D1

G1 S

distance between G and S

P(G

/S)

Steel and Rodrigo 2008(doi:10.1080/10635150802033014)

Quantifying the disagreement

gene tree species tree

reconciliation

assuming deepcoal:

assuming duplosses:

1 deepcoal

1 dup3 losses

Stochastic error/nonparametric (RF distance etc.)

assuming HGT:

1 event

Quantifying the disagreement

gene tree species tree

reconciliation

assuming deepcoal:

assuming duplosses:

1 deepcoal

1 dup3 losses

Stochastic error/nonparametric (RF distance etc.)

assuming HGT:

1 event

Must work with mul-trees

.

.

.

Distribution of gene trees: probabilistic model

S

D1

G1

Dn

Gn

.

.

.

Distribution of gene trees: probabilistic model

θ1

θn

S

D1

G1

Dn

Gn

λdup1

λdupn

λdupprior

.

.

.

.

.

.

Distribution of gene trees: probabilistic model

S

D1

G1

.

.

.Dn

Gn

θ1

θn

λdup1

λdupn

λdupprior

.

.

.

.

.

.

λloss1

λlossn

λlossprior

λRF1

λRFn

λRFprior...

Distribution of gene trees: probabilistic model

S

D1

G1

.

.

.Dn

Gn

θ1

θn

λdup1

λdupn

λdupprior

.

.

.

.

.

.

λloss1

λlossn

λlossprior

λRF1

λRFn

λRFprior...

Distribution of gene trees: probabilistic model

S

D1

G1

.

.

.Dn

Gn

θ1

θn

λdup1

λdupn

λdupprior

.

.

.

.

.

.

λloss1

λlossn

λlossprior

λRF1

λRFn

λRFprior...

guenomu algorithm

SG1

.

.

.Gn

.

.

.

ImportanceSampling

So we can use complex, state-of-the-art software for phylogenetic inference

λdup1

λdupn

λdupprior

.

.

.

.

.

.

λloss1

λlossn

λlossprior

λRF1

λRFn

λRFprior...

SG1

.

.

.Gn

.

.

.

ImportanceSampling

So we can use complex, state-of-the-art software for phylogenetic inference

Input

guenomu algorithm

λdup1

λdupn

λdupprior

.

.

.

.

.

.

λloss1

λlossn

λlossprior

λRF1

λRFn

λRFprior...

SG1

.

.

.Gn

.

.

.

ImportanceSampling

So we can use complex, state-of-the-art software for phylogenetic inference

Output

Do not rely on single estimates of gene phylogenies

guenomu algorithm

Simulation scenarios

1 – SimPhy (Mallo et al. in prep)

Simulation scenarios

Rasmussen, Kellis 2012 (10.1101/gr.123901.111)

1 – SimPhy (Mallo et al. in prep)

Simulation scenarios

Rasmussen, Kellis 2012 (10.1101/gr.123901.111)

2 – generation of tree uncertainty

1 – SimPhy (Mallo et al. in prep)

Analysis of simulated data sets

Analysis of simulated data sets● Distance matrix methods

Analysis of simulated data sets● Distance matrix methods

Helmkamp et al. 2012 (doi:10.1089/cmb.2012.0042)

Analysis of simulated data sets● Distance matrix methods

● Gene Tree Parsimony

Chaudhary et al. 2010 (doi:10.1186/1471-2105-11-574)

Analysis of simulated data sets● Distance matrix methods

● Gene Tree Parsimony

● guenomu

Species tree accuracy distributions per method

Species tree accuracyby simulation model

~7k samples under the coalescent

~15k under pure-birth

Species tree accuracyby simulation model

~7k samples under the coalescent

~15k under pure-birth

0.8 ~ 1

0.6 ~ 0.8

0.4 ~ 0.6

0.2 ~ 0.4

0 ~ 0.2

Species tree accuracyby simulation model

~7k samples under the coalescent

~15k under pure-birth

0.8 ~ 1

0.6 ~ 0.8

0.4 ~ 0.6

0.2 ~ 0.4

0 ~ 0.2

Gene family tree reconstruction accuracy

input:

Gene family tree reconstruction accuracy

input: output:

Gene family tree reconstruction accuracy

input: output:

Thank you!

http://bitbucket.org/leomrtns/guenomu

Species tree accuracy distributions, per simulation

Species tree accuracy distribution – mulRF distance only