37 research outputs found
Developing and applying supertree methods in Phylogenomics and Macroevolution
Supertrees
can
be
used
to
combine
partially
overalapping
trees
and
generate
more
inclusive
phylogenies.
It
has
been
proposed
that
Maximum
Likelihood
(ML)
supertrees
method
(SM)
could
be
developed
using
an
exponential
probability
distribution
to
model
errors
in
the
input
trees
(given
a
proposed
supertree).
When
the
tree-‐to-‐tree
distances
used
in
the
ML
computation
are
symmetric
differences,
the
ML
SM
has
been
shown
to
be
equivalent
to
a
Majority-‐Rule
consensus
SM,
and
hence,
exactly
as
the
latter,
it
has
the
desirable
property
of
being
a
median
tree
(with
reference
to
the
set
of
input
trees).
The
ability
to
estimate
the
likelihood
of
supertrees,
allows
implementing
Bayesian
(MCMC)
approaches,
which
have
the
advantage
to
allow
the
support
for
the
clades
in
a
supertree
to
be
properly
estimated.
I
present
here
the
L.U.St
software
package;
it
contains
the
first
implementation
of
a
ML
SM
and
allows
for
the
first
time
statistical
tests
on
supertrees.
I
also
characterized
the
first
implementation
of
the
Bayesian
(MCMC)
SM.
Both
the
ML
and
the
Bayesian
(MCMC)
SMs
have
been
tested
for
and
found
to
be
immune
to
biases.
The
Bayesian
(MCMC)
SM
is
applied
to
the
reanalyses
of
a
variety
of
datasets
(i.e.
the
datasets
for
the
Metazoa
and
the
Carnivora),
and
I
have
also
recovered
the
first
Bayesian
supertree-‐based
phylogeny
of
the
Eubacteria
and
the
Archaebacteria.
These
new
SMs
are
discussed,
with
reference
to
other,
well-‐
known
SMs
like
Matrix
Representation
with
Parsimony.
Both
the
ML
and
Bayesian
SM
offer
multiple
attractive
advantages
over
current
alternatives
Developing and applying supertree methods in Phylogenomics and Macroevolution
Supertrees
can
be
used
to
combine
partially
overalapping
trees
and
generate
more
inclusive
phylogenies.
It
has
been
proposed
that
Maximum
Likelihood
(ML)
supertrees
method
(SM)
could
be
developed
using
an
exponential
probability
distribution
to
model
errors
in
the
input
trees
(given
a
proposed
supertree).
When
the
tree-‐to-‐tree
distances
used
in
the
ML
computation
are
symmetric
differences,
the
ML
SM
has
been
shown
to
be
equivalent
to
a
Majority-‐Rule
consensus
SM,
and
hence,
exactly
as
the
latter,
it
has
the
desirable
property
of
being
a
median
tree
(with
reference
to
the
set
of
input
trees).
The
ability
to
estimate
the
likelihood
of
supertrees,
allows
implementing
Bayesian
(MCMC)
approaches,
which
have
the
advantage
to
allow
the
support
for
the
clades
in
a
supertree
to
be
properly
estimated.
I
present
here
the
L.U.St
software
package;
it
contains
the
first
implementation
of
a
ML
SM
and
allows
for
the
first
time
statistical
tests
on
supertrees.
I
also
characterized
the
first
implementation
of
the
Bayesian
(MCMC)
SM.
Both
the
ML
and
the
Bayesian
(MCMC)
SMs
have
been
tested
for
and
found
to
be
immune
to
biases.
The
Bayesian
(MCMC)
SM
is
applied
to
the
reanalyses
of
a
variety
of
datasets
(i.e.
the
datasets
for
the
Metazoa
and
the
Carnivora),
and
I
have
also
recovered
the
first
Bayesian
supertree-‐based
phylogeny
of
the
Eubacteria
and
the
Archaebacteria.
These
new
SMs
are
discussed,
with
reference
to
other,
well-‐
known
SMs
like
Matrix
Representation
with
Parsimony.
Both
the
ML
and
Bayesian
SM
offer
multiple
attractive
advantages
over
current
alternatives
The matroid structure of representative triple sets and triple-closure computation
The closure cl (R) of a consistent set R of triples (rooted binary trees on three leaves) provides essential information about tree-like relations that are shown by any supertree that displays all triples in . In this contribution, we are concerned with representative triple sets, that is, subsets R' of R with cl (R') = cl . In this case, R' still contains all information on the tree structure implied by R, although R' might be significantly smaller. We show that representative triple sets that are minimal w.r.t. inclusion form the basis of a matroid. This in turn implies that minimal representative triple sets also have minimum cardinality. In particular, the matroid structure can be used to show that minimum representative triple sets can be computed in polynomial time with a simple greedy approach. For a given triple set R that “identifies” a tree, we provide an exact value for the cardinality of its minimum representative triple sets. In addition, we utilize the latter results to provide a novel and efficient method to compute the closure cl (R) of a consistent triple set R that improves the time complexity (R Lr 4) of the currently fastest known method proposed by Bryant and Steel (1995). In particular, if a minimum representative triple set for R is given, it can be shown that the time complexity to compute cl (R) can be improved by a factor up to R Lr . As it turns out, collections of quartets (unrooted binary trees on four leaves) do not provide a matroid structure, in general
Polynomial supertree methods in phylogenomics: algorithms, simulations and software
One of the objectives in modern biology, especially phylogenetics, is to build larger clades of the Tree of Life. Large-scale phylogenetic analysis involves several serious challenges. The aim of this thesis is to contribute to some of the open problems in this context. In computational phylogenetics, supertree methods provide a way to reconstruct larger clades of the Tree of Life. We present a novel polynomial time approach for the computation of supertrees called FlipCut supertree. Our method combines the computation of minimum cuts from graph-based methods with a matrix representation method, namely Minimum Flip Supertrees. Here, the input trees are encoded in a 0/1/?-matrix. We present a heuristic to search for a minimum set of 0/1-flips such that the resulting matrix admits a directed perfect phylogeny. In contrast to other polynomial time approaches, our results can be interpreted in the sense that we try to minimize a global objective function, namely the number of flips in the input matrix. We extend our approach by using edge weights to weight the columns of the 0/1/?-matrix. In order to compare our new FlipCut supertree method with other recent polynomial supertree methods and matrix representation methods, we present a large scale simulation study using two different data sets. Our findings illustrate the trade-off between accuracy and running time in supertree construction, as well as the pros and cons of different supertree approaches. Furthermore, we present EPoS, a modular software framework for phylogenetic analysis and visualization. It fills the gap between command line-based algorithmic packages and visual tools without sufficient support for computational methods. By combining a powerful graphical user interface with a plugin system that allows simple integration of new algorithms, visualizations and data structures, we created a framework that is easy to use, to extend and that covers all important steps of a phylogenetic analysis