Talk presented at the Evolution Meeting 2013 (http://www.evolutionmeeting.org/engine/search/index.php?func=detail&aid=478)

1. A probabilistic parsimonious model
for species tree reconstruction
Leonardo de Oliveira Martins
David Posada
●
leomrtns@uvigo.es
●
dposada@uvigo.es
with invaluable help from Klaus Schliep and Diego Mallo

2. What do we want
●
To estimate species trees given arbitrary gene families ←
can contain paralogous, missing data, etc.
To account for uncertainty in gene tree and species tree
estimation ← some gene families may be more informative, or
●
maybe we don't have signal at all
●
To allow for several sources of disagreement ← real data
seldomly can be explained by just one biological phenomenon
●
Fast computation ← improvement provided by slower, fully
probabilistic methods may be elusive, and they can benefit from
our output nonetheless

3. Outline
Model of gene family evolution
Parsimonious estimation of disagreement
* reconciliation
* distance between trees
Hierarchical Bayesian model
Examples
* comparing many trees
* simulation
* TreeFam data set

4. Model for the evolution of gene families
S
G1
D1
G2
D2
Gn
Dn
.
.
.

5. Model for the evolution of gene families
S
G1
D1
We just need to consider the
simplest explanation for the
P(G/S)
Our assumption:
difference between the gene
and species trees
we may use several such
simple explanations
●
distance between G and S

6. Model for the evolution of gene families
S
G1
D1
We just need to consider the
simplest explanation for the
difference between the gene
and species trees
P(G/S)
Our assumption:
Rodrigo and Steel.
2008. SystBiol 57: 243
ML supertrees
we may use several such
simple explanations
●
work with unrooted gene
trees
●
penalize gene trees very
different from species tree
●
distance between G and S

7. Outline
Model of gene family evolution
Parsimonious estimation of disagreement
* reconciliation
* distance between trees
Hierarchical Bayesian model
Examples
* comparing many trees
* simulation
* TreeFam data set

8. Quantifying the disagreement
assuming deepcoal:
gene tree
species tree
reconciliation
1 deepcoal
assuming duplosses:
1 dup
3 losses
assuming HGT:
1 event

9. Quantifying the disagreement
assuming deepcoal:
gene tree
species tree
reconciliation
1 deepcoal
assuming duplosses:
1 dup
3 losses
assuming HGT:
1 event
Stochastic error/nonparametric

10. Outline
Model of gene family evolution
Parsimonious estimation of disagreement
* reconciliation
* distance between trees
Hierarchical Bayesian model
Examples
* comparing many trees
* simulation
* TreeFam data set

11. Quantifying the disagreement – other measures
mul-tree version: Chaudhary R, Burleigh JG, Fernández-Baca D (2013) Inferring Species Trees from
Incongruent Multi-Copy Gene Trees Using the Robinson-Foulds Distance. arXiv:1210.2665

12. Quantifying the disagreement – other measures
de Oliveira Martins et al. (2008) Phylogenetic Detection of Recombination with a Bayesian Prior on the
Distance between Trees. PLoS ONE 3(7): e2651.

13. Quantifying the disagreement – other measures
see also: Whidden et al. (2013) Supertrees based on the subtree prune-and-regraft distance. PeerJ PrePrints
1:e18v1

14. Quantifying the disagreement – other measures
Hdist similar to: Nye TMW, Liò P, Gilks WR (2006) A novel algorithm and web-based tool for comparing two
alternative phylogenetic trees. Bioinformatics 22: 117-119

15. Now we have estimates for these
assuming deepcoal:
1 deepcoal
assuming duplosses:
1 dup
3 losses
assuming HGT:
1 event
Stochastic error/nonparametric

16. Now we have estimates for these
assuming deepcoal:
Gene tree parsimony
1 deepcoal
assuming duplosses:
1 dup
3 losses
assuming HGT:
1 event
Stochastic error/nonparametric

17. Now we have estimates for these
assuming deepcoal:
Gene tree parsimony
1 deepcoal
assuming duplosses:
Gene tree parsimony
1 dup
3 losses
assuming HGT:
1 event
Stochastic error/nonparametric

18. Now we have estimates for these
assuming deepcoal:
Gene tree parsimony
1 deepcoal
assuming duplosses:
Gene tree parsimony
1 dup
3 losses
assuming HGT:
(approximate) dSPR
1 event
Stochastic error/nonparametric

19. Now we have estimates for these
assuming deepcoal:
Gene tree parsimony
1 deepcoal
assuming duplosses:
Gene tree parsimony
1 dup
3 losses
assuming HGT:
(approximate) dSPR
1 event
RF, Hdist
Stochastic error/nonparametric

20. Considering several measures of disagreement:
Thus we can incorporate e.g. duplications
and losses while accounting for HGT and
random errors
Easy to include other
distances in the future

21. Considering several measures of disagreement:
Thus we can incorporate e.g. duplications
and losses while accounting for HGT and
random errors
Easy to include other
distances in the future
Problem: the normalization constant
Ref.: Bryant D, Steel M (2009) Computing the Distribution of a Tree Metric. TCBB: 420 – 426
Solution: importance sampling estimate of Z(.)
E.g.: Rodrigue N, Kleinman CL, Philippe H, Lartillot N (2009) Computational Methods for Evaluating
Phylogenetic Models of Coding Sequence Evolution with Dependence between Codons. Mol Biol Evol 26:
1663-1676.

22. Outline
Model of gene family evolution
Parsimonious estimation of disagreement
* reconciliation
* distance between trees
Hierarchical Bayesian model
Examples
* comparing many trees
* simulation
* TreeFam data set

23. Distribution of gene trees: probabilistic model
G1
D1
Q1
.
.
.
Gn
Dn
Qn
S

24. Distribution of gene trees: probabilistic model
G1
S
λdup1
D1
Q1
.
.
.
λdupprior
Gn
Dn
Qn
λdupn

25. Distribution of gene trees: probabilistic model
G1
S
λdup1
D1
Q1
λloss1
.
.
.
λspr1
λdupprior
Gn
Dn
Qn
.
.
.
λdupn
λlossn .
.
λsprn .
λlossprior
λsprprior

26. Distribution of gene trees: probabilistic model
G1
S
λdup1
Importance
Sampling
So we can use complex,
state-of-the-art software
for phylogenetic
inference
λloss1
.
.
.
λspr1
.
.
.
λdupprior
Gn
λdupn
λlossn .
.
λsprn .
λlossprior
λsprprior

27. Distribution of gene trees: probabilistic model
G1
S
λdup1
Importance
Sampling
So we can use complex,
state-of-the-art software
for phylogenetic
inference
λloss1
.
.
.
λspr1
.
.
.
λdupprior
Gn
λdupn
λlossn .
.
λsprn .
Input
λlossprior
λsprprior

28. Distribution of gene trees: probabilistic model
G1
S
λdup1
Importance
Sampling
So we can use complex,
state-of-the-art software
for phylogenetic
inference
λloss1
.
.
.
λspr1
.
.
.
λdupprior
Gn
λdupn
λlossn .
.
λsprn .
Output
λlossprior
λsprprior

29. Distribution of gene trees: probabilistic model
G1
S
λdup1
Importance
Sampling
So we can use complex,
state-of-the-art software
for phylogenetic
inference
We should not rely on
single estimates of gene
phylogenies
λloss1
.
.
.
λspr1
.
.
.
λdupprior
Gn
λdupn
λlossn .
.
λsprn .
λlossprior
λsprprior
Output
E.g.: Boussau B, Szollosi GJ, Duret L, Gouy M, Tannier E, Daubin V. (2012) Genome-scale coestimation of
species and gene trees. Genome research 23: 323-330.

30. Outline
Model of gene family evolution
Parsimonious estimation of disagreement
* reconciliation
* distance between trees
Hierarchical Bayesian model
Examples
* comparing many trees
* simulation
* TreeFam data set

31. Example: distances between gene families
●
567 single-copy gene trees for 23 species
Data from.: Salichos L, Rokas A (2013) Inferring ancient divergences requires genes with strong
phylogenetic signals. Nature 497: 327–331
●
Analysis under a model where only RF, Hdist and dSPR are considered
●
Not interested in data set per se (unreliable)
●
Use it just as a didactical tool about how the model works

32. Example: distances between gene families
●
567 single-copy gene trees for 23 species
Data from.: Salichos L, Rokas A (2013) Inferring ancient divergences requires genes with strong
phylogenetic signals. Nature 497: 327–331
●
Analysis under a model where only RF, Hdist and dSPR are considered
●
Not interested in data set per se (unreliable)
●
Use it just as a didactical tool about how the model works
RF
Hdist
SPR

33. Example: distances between gene families
RF
Hdist
SPR

34. Example: distances between gene families
Posterior samples
RF
Hdist
SPR

35. Example: distances between gene families
Posterior samples
best estimate
RF
Hdist
SPR

36. Outline
Model of gene family evolution
Parsimonious estimation of disagreement
* reconciliation
* distance between trees
Hierarchical Bayesian model
Examples
* comparing many trees
* simulation
* TreeFam data set

37. Analysis of simulated data sets
●
Fully probabilistic simulation of gene trees by Diego Mallo and
David Posada
●
Birth and death of new loci, conditioned on a multispecies
coalescent, followed by sequence evolution
We use gene trees only, and simulate
tree inference error
Idea from: Rasmussen MD, Kellis M (2012) Unified modeling of gene duplication, loss, and coalescence using
a locus tree. Genome Res. 22: 755-765

38. Analysis of simulated data sets – results

39. Analysis of simulated data sets – results

40. Analysis of simulated data sets – results

41. Outline
Model of gene family evolution
Parsimonious estimation of disagreement
* reconciliation
* distance between trees
Hierarchical Bayesian model
Examples
* comparing many trees
* simulation
* TreeFam data set

42. Single copy genes from Drosophila (TreeFam)
●
4591 informative, single-copy gene families
●
(TreeFam database has 14250 informative gene families)

43. Single copy genes from Drosophila (TreeFam)
●
4591 informative, single-copy gene families
●
(TreeFam database has 14250 informative gene families)

44. Single copy genes from Drosophila (TreeFam)
●
4591 informative, single-copy gene families
Estimated species tree:
●
Root location uncertain

45. Single copy genes from Drosophila (TreeFam)
●
4591 informative, single-copy gene families
Estimated species tree:
●
Root location uncertain
●
Only one unrooted topology

46. Large gene families from Drosophila (TreeFam)
●
43 gene families with 102~295 tips

47. Large gene families from Drosophila (TreeFam)
●
43 gene families with 102~295 tips
best species tree:
~100%

48. To recap, our model can
●
Estimate species trees given arbitrary gene families ← can
contain paralogous, missing data, etc.
The larger, the better – specially for rooting the species tree
Account for uncertainty in gene tree and species tree
estimation ← some gene families may be more informative, or
●
maybe we don't have signal at all
Do not assume gene trees are known – embrace ignorance!
●
Allow for several sources of disagreement ← real data
seldomly can be explained by just one biological phenomenon
Different gene families may be product of distinct processes
●
Be fast ← improvement provided by slower, fully probabilistic
methods may be elusive, and they can benefit from our output
nonetheless
It's parallelized, and all distances can be calculated very fast.

49. Check out http://darwin.uvigo.es for announcements, code, slides...
Thank you!