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Advances and Limitations of Maximum Likelihood Phylogenetics. Olivier Gascuel LIRMM-CNRS, Montpellier, France. Stéphane Guindon. Wim Hordijk. Quang Le Si. Maria Anisimova. Nicolas Lartillot. Jean-François Dufayard. Most of the talk will be about proteins. Man. - PowerPoint PPT Presentation
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Advances and Limitationsof Maximum Likelihood Phylogenetics
Olivier Gascuel
LIRMM-CNRS, Montpellier, France
StéphaneGuindon
WimHordijk
Quang Le Si
NicolasLartillot
MariaAnisimova
Jean-FrançoisDufayard
Most of the talk will be about proteins
The data is a set of aligned sequences
Man
Zebrafish
Frog
Fly
Yeast
Amoeba
ParameciumBlue algae
M A E I G R L I E F S A M V D F W Q N R CM A E I G R L V E Y S A M V D F W Q N R CM A D L G K L I D Y S A L V D F W Q N R CM S D I G K L V E F S P M V E F W Q Q K CM S E I G R L V E F - - - - - F W Q N R CL S E L G R L V D F - - - - D F W N N R CL A E L G K L V E - - - - - - - - - - R CL S D L G K L I D - - - - - - - - - - K C
the data
the data at sitei
D
D i
We aim to reconstruct the phylogeny of the sequences in the alignment
a phylogeny with branch lengthsT
We assume a substitution model, denoted as M
The likelihood of data D, given M and T, is
We search for the tree T* that maximizes data likelihood
, ;L T M D
* , ;TT ArgMax L T M D
Algorithmics Simultaneous NNIs Fast SPRs Results
Statistical modeling An improved replacement matrix Accounting for the structure Results
N = NJ
M = FastME (distance)
D = DNAPARS
P = PHYML (ML)
Maximum pairwise divergence
Top
olog
ical
acc
urac
y (R
F)
Simulation data (40 taxa, random model trees)
Algorithmics
Algorithmics
NNI
Algorithmics
Algorithmics
Algorithmics
SPR
PHYML-NNI
a) Start with a reasonnable tree with branch lengths (BIONJ)
b) Compute all subtree partial likelihoods
c) Independently compute all optimal branch-lengths and optimal NNI configurations (i.e. local changes)
d) When no local change significantly increases the likelihood, return the current tree
e) Else, apply to the current tree all local changes; if the tree likelihood increases go to (b), else (~5% of the cases) apply as many as possible of these changes and go to (b)
Comments
Simultaneous NNIs can change the tree dramatically, and are not included in (single) SPR or TBR
The algorithm is very fast and able to deal with large datasets (up to 500-1000 taxa with DNA sequences)
High topological accuracy with simulated data
But real data tend to be harder than simulated data, specially the multiple-gene, concatenated datasets
Fast SPRs
SPRs are non-local moves
We start from a phylogeny with ML branch length estimates
The SPR procedure involves testing all (subtree, edge) pairs
This cannot be achieved in an exact way (i.e. with optimal branch lengths), thus the game is to focus on the most promising pairs (PHYML 3.0 uses a parsimony approach) and to minimize the number of length optimizations and partial likelihod calculations.
As soon as an improving SPR is found, we fully optimize all branch lengths, compute all partial likelihoods and iterate the procedure.
Results
60 Treebase protein alignments (i.e. all available datasets, only removing redundancies and incomplete data).
average of ~25 sequences and ~1000 sites
2 genomic datasets (e.g. 12.000 sites and 64 sequences)
WAG+4+I, with PHYML 3.0
SPR is about twice slower than NNI, ranging from a few seconds to a few hours
A1 A2 LLK/site A1>A2 A1<A2 A1=A2
SPR NNI 0.004 28 (6) 8 (2) 24 (52)
p-value<0.01
Results
60 Treebase protein alignments (i.e. all available datasets, only removing redundancies and incomplete data).
average of ~25 sequences and ~1000 sites
2 genomic datasets (e.g. 12.000 sites and 64 sequences)
WAG+4+I, with PHYML 3.0
RAXML is in between in LLK values, and 2-3 times slower than PHYML SPR
A1 A2 LLK/site A1>A2 A1<A2 A1=A2
SPR NNI 0.004 28 (6) 8 (2) 24 (52)
Comments
Fast with this representative, relatively small alignments
Output trees are not statistically different (in most cases, 52/60)
SPR trees do not depend (much) on the starting trees
Some more intensive search strategy could be envisaged, e.g. based on tabu
Genetic algorithms (e.g. MetaPIGA, GARLI) also perform well.
I do not expect high gains from further algorithmic developments (with such datasets)
Statistical modeling
An improved, general AA replacement matrix
Accounting for structure and exposition to solvent
Results
AA time-reversible replacement matrices
is the instaneous rate of changes from x to y
Key role in protein phylogenetics (and alignment)
M is defined by:
lx yl P l e MP
x yM
x y y x yM R
x yM M
Global rate 1 in estimation and
when using several models
Exchangeability x yR R
Equilibrium frequency
Estimating replacement matrices
Counting approach of Dayhoff et al. (1972), using pairwise alignments of closely related proteins (PAM, JTT, …).
Logarithmic (Gonnet et al 1992) and resolvent (Muller et al 2000) counting approaches to deal with pairs of remote proteins
A strong tendency is to estimate different matrices for different protein groups (mitochondrial, prokaryotic, viral, arthropoda …).
But general matrices (e.g., JTT, WAG) are widely used, e.g. to build deep phylogenies or to analyze concatenated datasets.
ML estimation of replacement matrices
Counting methods are not able to deal with multiple alignments, which contain much more information than protein pairs
ML methods exploit multiple alignments and phylogenies
a set of multiple alignments, we aim to maximize
But we cannot simultaneously estimate a number of trees and M. This full maximization was only used with unique concatenated alignments (e.g. Adachi&Hasegawa 1996, with mitochondrial genes, ~3350 sites and 20 taxa).
, ;a a
a
L A L T D M
aA D
ML estimation of replacement matrices, Whelan&Goldman 2001
First step: approximate trees are inferred using NJ and ML branch length estimation
Second step: M is estimated using an EM algorithm maximizing
WAG was estimated using BRKALN (186 aligments, ~51.000 sites, ~900.000 AAs)
WAG is much better than JTT (also estimated from BRKALN)
; ,a a
a
L A L D T M
ML estimation of replacement matrices, Whelan&Goldman 2001
Variability of rates across sites (RAS) was not incorporated in likelihood calculations.
It is now recognized that RAS is essential. Some sites are slow (invariant) due to strong evolutionary constraints, while others are very fast.
RAS is usually implemented with a discrete gamma distribution of rates and invariant sites (4+I), and used to infer most of trees.
Moreover, BRKALN is limited regarding current databases, and likely biased toward proteins being easy to cristallize, with well defined 3D structure.
Lee & G., 2007 (submission next week !)
We used the seed alignments of Pfam, which are manually verified multiple alignments of representative sets of sequences, and selected 3,913 large enough alignments (~600.000 sites, ~6.5 millions AAs).
The trees were inferred by PHYML with WAG+4+I
Each site i was categorized in the rate category with maximum a posteriori probability, and rate
The LG replacement matrix was estimated using XRATE (Holmes et al 06) EM-based software, with site likelihood
; ,a aic iL D T M
c i c i
aT
Lee & G., 2007 (submission next week !)
We used the seed alignments of Pfam, which are manually verified multiple alignments of representative sets of sequences, and selected 3,913 large enough alignments (~600.000 sites, ~6.5 millions AAs).
The trees were inferred by PHYML with WAG+4+I
Each site i was categorized in the rate category with maximum a posteriori probability, and rate
The replacement matrix was estimated using XRATE (Holmes 06) EM-based software, with site likelihood
; ,a ac c i
c
L D T M
c i c i
aT
Convergence problems
LG/WAG matrices
AA frequencies: relatively close, very low influence on likelihood values when inferring trees
Exchangeabilities: strongly correlated
~20 times slower with LG
require 3 DNA substitutions
LG/WAG matrices
Our estimation procedure has better ability to distinguish among the substitution events that are very rare (likely occuring in fast sites only) and those being not so rare (possibly occuring in slow sites).
LG exchangeabilities are much more contrasted than WAG’s
But LG cannot be viewed as a constrasted version of WAG:
ratio 0.6
AsparagineTyrosine
LG
WAG
0.69
1.14
LG/WAG matrices
Our estimation procedure has better ability to distinguish among the substitution events that are very rare (likely occuring in fast sites only) and those being not so rare (possibly occuring in slow sites).
LG exchangeabilities are much more contrasted than WAG’s
But LG cannot be viewed as a constrasted version of WAG:
ratio 2.0
CysteinTyrosine
LG
WAG
1.15
0.57
LG/WAG in tree inference
We analyzed the 60 Treebase alignments using PHYML_SPR with WAG+4+I, LG+4+I, and JTT+4+I.
We measured the tree length, the gama parameter value () and the loglikelihood. We also compared the tree topologies.
M1 M2 Topology
M1M2
AIC/site
M1-M2
M1>M2 M1<M2
JTT WAG 41/60 -0.17 15 (7) 45 (21)
p-value<0.01
LG/WAG in tree inference
LG trees are longer than WAG trees
Topologies of the inferred trees differ with half of the data sets.
Clear improvement in likelihood values
Similar results with Pfam test aligments
M1 M2 Length
M1/M2
M1/M2
Topology
M1M2
AIC/site
M1-M2
M1>M2 M1<M2
LG WAG 1.07
(58/60)
0.85
(46/60)
30/60 0.23 48 (39) 12 (2)
Accounting for exposition and secondary structure
Substitutions clearly depend on secondary structure and exposition; e.g., buried sites are and remain hydrophobic.
Overington et al.1990; Lüthy et al. 1991; Topham et al. 1993; Wako and Blundell 1994; Goldman et al. 1996 (to infer both the structure and the phylogeny).
Not (or rarely) used today in phylogenetics, though the structure of dozens of thousands of proteins is now available.
We revisited the question thanks to (1) our improved ML-based estimation procedure, (2) the huge, current databases.
Learning and testing data
We extracted from HSSP ((homology-derived structures of proteins) 4,889 non-redundant (sub)alignments.
290,000 sequences, 1,250,000 sites and 71 billions AAs.
Secondary structure (Helix, Sheet, Turn, Coil) and exposition (Exposed, Buried) are available for all the sites, but not fully reliable (80-90% of conservation).
We randomly selected 500 alignments as a test set, leaving 4,389 alignments to learn substitution matrices for various site categories ( E, B; H, S, T, C; E&H, E&S, E&T …).
Computing the tree likelihood using site partition
Each category is associated to a replacement matrix; the category and corresponding matrix are known for every site i
, , , ,i i ii
L T D L T D M M
Extra parameters: gamma, proportion of invariant sites, etc.
No extra parameter,
regarding single-matrix models
iM
Mixture model
Site category is unknown. We have a set of replacement matrices corresponding to various categories with probabilities
, , , , ii
L T D L T D
MM
M M
MM
extra parameters,
regarding single-matrix models, or none when the
are known (e.g. buried/exposed)
1M
M
Confidence-based combination
Site category is “known”, but not fully reliable
, ,
, , (1 ) , ,
i i
iii
c L T DL T D c L T D
MM
MM M
One more parameter
than mixture
Confidence coefficient, estimated separately for each alignment;
c 1 useful site assignments,
c 0: useless site assignments
Results of buried/exposed model (LG_EX)
We analyzed the 60 Treebase and 300 HSSP test alignments with various models, all using 4+I option.
M1 M2 AIC/site
M1-M2
M1>M2 Topology
M1M2
LG WAG 0.36 248/300 165/300
LG_EX
Partitioning
WAG 1.03 294/300 199/300
LG_EX
Confidence
WAG 1.15 297/300 201/300
LG_EX
Mixture
WAG 0.33
LG=0.23
49/60
LG=48
33/60
LG=30
HSSP
Treebase
Results
Likelihood gain is lower when using the secondary structure (LG_SS, ~0.85) and higher when combining both secondary structure and exposition (LG_EX_SS, ~1.6).
The difference between LG_EX_SS+4+I and WAG+4+I, is of the same range as the difference between WAG+4+I and WAG (~2.0).
Discussion
We revisited questions and models which were proposed and explored by N. Goldman, Z. Yang, their collaborators, … others, using today
concepts, e.g. RAS MUST be accounted for in tree inference AND replacement matrix estimation,
tools (XRATE, PHYML),
and databases (Pfam, HSSP).
Discussion
We revisited questions and models which were proposed and explored by N. Goldman, Z. Yang, their collaborators, … others, using today
concepts, e.g. RAS MUST be accounted for in tree inference AND replacement matrix estimation,
tools (XRATE, PHYML),
and databases (Pfam, HSSP),
and computers !
Discussion
M1 M2 AIC/site M1>M2 database
PASSML(--I)
WAG(++I)
-0.6 HSSP
Elegant HMM model to account for secondary structure and exposition, but not incoporating any RAS (Lio et al, 98)
Discussion
M1 M2 AIC/site M1>M2 database
PASSML(--I)
WAG -0.6 HSSP
JTT WAG -0.23 HSSP
Counting estimate ML estimate
Discussion
M1 M2 AIC/site M1>M2 database
PASSML(--I)
WAG -0.6 HSSP
JTT WAG -0.23 HSSP
LG WAG 0.33 248/300 HSSP
ML estimation with RAS and larger database
Discussion
M1 M2 AIC/site M1>M2 database
PASSML(--I)
WAG -0.6 HSSP
JTT WAG -0.23 HSSP
LG WAG 0.33 248/300 HSSP
LG_EX WAG 1.15 297/300 HSSP
Accounting for solvent exposition of residues
Discussion
M1 M2 AIC/site M1>M2 database
PASSML(--I)
WAG -0.6 HSSP
JTT WAG -0.23 HSSP
LG WAG 0.33 248/300 HSSP
LG_EX WAG 1.15 297/300 HSSP
SPR NNI 0.009 28(6)/60 Treebase
Warm up conclusions
Statistical modelling provides much higher gains than algorithmics !
Warm up conclusions
Statistical modelling provides much higher gains than algorithmics !
This should continue in the next years, as current models are still rejected for a number of alignments …….
Number of AA per site (Lartillot et al 2004, 2007)
WAG LG M1500
Mean 3.33 3.25 2.69
Variance 8.13 7.53 4.59
Warm up conclusions
Statistical modelling provides much higher gains than algorithmics !
This should continue in the next years, as current models are still rejected for a number of alignments …..
Thank you all, the organizers and the Isaac Newton Institute
Independence assumption:
Stationary distribution of AA:
, ; , ; ii
L T M D L T M D
x
The tree likelihood is recursively computed from the root:
, ; , , ;
, , , ;
... ...
i x i ix AA
x x y U i ix AA y AA
y AA
L T M D L T M a x D
P l M L U M u y U D
V
lU
U V
u v
lV
a
Probability of change from
x to y in time lU
Partial likelihood of rooted tree U
(L(U) for short)
With time reversible models, the tree likelihood can be obtained from any branch, using partial likelihoods L(U) and L(V), and branch length l(u,v).
U Vu vl(u,v)
(Relatively) time consuming
Computing the partial likelihood of all subtrees
Optimizing the branch lengths and computing the likelihood of a given topology
Very time consuming
Searching the topology space in an hill-climbing, exact way.
Efficient algorithms simultaneously modify the branch lengths and the tree topology, thus searching the space of phylogenies with branch-lengths.
Silmutaneous NNIs : two (relatively) fast and easy operations (when all partial likelihoods are known)
Independently computing all optimal branch lengths
Independently computing all optimal NNI configurations
U Vu vl(u,v)
e
C
A
B
D Evaluate AC|BD and AD|BC, optimizing l(e)or all five branches
Orchestrating calculations (RAXML, PHYML ….)
Step0 - All partial likelihoods are available
Orchestrating calculations
Step1 – Pruning the subtree and estimating the branch being left
Orchestrating calculations
Step2 – Computing 1 partial likelihood, estimating the 3 new branch lengths and computing the tree likelihood
Orchestrating calculations
Step3 – Computing 1 partial likelihood, estimating the 3 new branch lengths and computing the tree likelihood … etc.
Progressive filtering strategy (PHYML)
All possible SPRs are first filtered by a fast distance-based (or parsimony) algorithm; typically, we retain for every subtree the 20% most promising edges for regraphting.
Previous scheme is run several times with increasingly sophisticated branch-length estimations; when an improving SPR is found, it is returned and the procedure restart from the beginning; else, results are used to rank and filter remaining SPRs.
This strategy allows considerable gain in computing time, without loss on the resulting tree.
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