Bio::PhyloNetwork(3pm)					User Contributed Perl Documentation				    Bio::PhyloNetwork(3pm)

NAME

       Bio::PhyloNetwork - Module to compute with Phylogenetic Networks

SYNOPSIS

	use Bio::PhyloNetwork;

	# Create a PhyloNetwork object from a eNewick string
	my $net1=Bio::PhyloNetwork->new(
	  -eNewick=>'t0:((H1,(H2,l2)),H2); H1:((H3,l1)); H2:((H3,(l3,H1))); H3:(l4);'
	);

	# Print all available data
	print $net1;

	# Rebuild $net1 from its mu_data
	my %mudata=$net1->mudata();
	my $net2=Bio::PhyloNetwork->new(-mudata=>\%mudata,-numleaves=>4);
	print $net2;
	print "d=".$net1->mu_distance($net2)."
";

	# Get another one and compute distance
	my $net3=Bio::PhyloNetwork->new(
	  -eNewick=>'(l2,((l1,(H1,l4)),H1))r; (l3)H1;'
	);
	print "d=".$net1->mu_distance($net3)."
";

	# ...and find an optimal alignment w.r.t. the Manhattan distance (default)
	my ($weight,%alignment)=$net1->optimal_alignment($net3);
	print "weight:$weight
";
	foreach my $node1 (keys %alignment) {
	  print "$node1 => ".$alignment{$node1}."
";
	}
	# ...or the Hamming distance

	my ($weightH,%alignmentH)=$net1->optimal_alignment($net3,-metric=>'Hamming');
	print "weight:$weightH
";
	foreach my $node1 (keys %alignmentH) {
	  print "$node1 => ".$alignmentH{$node1}."
";
	}

	# Test for time consistency of $net1
	if ($net1->is_time_consistent) {
	  print "net1 is time consistent
"
	}
	else {
	  print "net1 is not time consistent
"
	}

	# create a network from the list of edges
	my $net4=Bio::PhyloNetwork->new(-edges=>
	  [qw(r s r t s u s c t c t v u b u l3 u b v b v l4 b l2 c l1)]);

	# Test for time consistency of $net3
	if ($net4->is_time_consistent) {
	  print "net4 is time consistent
"
	}
	else {
	  print "net4 is not time consistent
"
	}

	# And print all information on net4
	print $net4;

	# Compute some tripartitions
	my %triparts=$net1->tripartitions();

	# Now these are stored
	print $net1;

	# And can compute the tripartition error
	print "dtr=".$net1->tripartition_error($net3)."
";

DESCRIPTION

   Phylogenetic Networks
       This is a module to work with phylogenetic networks. Phylogenetic networks have been studied over the last years as a richer model of the
       evolutionary history of sets of organisms than phylogenetic trees, because they take not only mutation events but also recombination and
       horizontal gene transfer events into account.

       The natural model for describing the evolutionary history of a set of sequences under recombination events is a DAG, hence this package
       relies on the package Graph::Directed to represent the underlying graph of a phylogenetic network. We refer the reader to [CRV1,CRV2] for
       formal definitions related to phylogenetic networks.

   eNewick description
       With this package, phylogenetic networks can be given by its eNewick string. This description appeared in other packages related to
       phylogenetic networks (see [PhyloNet] and [NetGen]); in fact, these two packages use different descriptions. The Bio::PhyloNetwork package
       allows both of them, but uses the second one in its output.

       The first approach [PhyloNet] goes as follows: For each hybrid node H, say with parents u_1,u_2,...,u_k and children v_1,v_2,...v_l: split
       H in k+1 different nodes; let each of the first k copies be a child of one of the u_1,...,u_k (one for each) and have no children (hence we
       will have k extra leaves); as for the last copy, let it have no parents and have v_1,...,v_l be its children. This way we get a forest;
       each of the trees will be rooted at either one root of the phylogenetic network or a hybrid node of it; the set of leaves (of the whole
       forest) will be the set of leaves of the original network together with the set of hybrid nodes (each of them repeated as many times as its
       in-degree). Then, the eNewick representation of the phylogenetic network will be the Newick representation of all the trees in the obtained
       forest, each of them with its root labeled.

       The second approach [NetGen] goes as follows: For each hybrid node H, say with parents u_1,u_2,...,u_k and children v_1,v_2,...v_l: split H
       in k different nodes; let the first copy be a child of u_1 and have all v_1,v_2,...v_l as its children; let the other copies be child of
       u_2,...,u_k (one for each) and have no children. This way, we get a tree whose set of leaves is the set of leaves of the original network
       together with the set of hybrid nodes (possibly repeated). Then the Newick string of the obtained tree (note that some internal nodes will
       be labeled and some leaves will be repeated) is the eNewick string of the phylogenetic network.

       For example, consider the network depicted below:

	      r
	     / 
	    /	
	   U	 V
	  / 	/ 
	 1    /   3
	      H
	      |
	      2

       If the first approach is taken, we get the forest:

	      r
	     / 
	    /	
	   U	 V
	  / 	/ 
	 1   H H   3
	      |
	      H
	      |
	      2

       Hence, the eNewick string is '((1,H),(H,3))r; (2)H;'.

       As for the second one, one gets the tree:

	      r
	     / 
	    /	
	   U	 V
	  / 	/ 
	 1   H |   3
	       H
	       |
	       2

       Hence, the eNewick string is '((1,H),((2)H,3))r;'.

       Note: when rooting a tree, this package allows the notations '(subtree,subtree,...)root' as well as 'root:(subtree,subtree,...)', but the
       first one is used when writing eNewick strings.

   Tree-child phylogenetic networks
       Tree-child (TC) phylogenetic networks are a special class of phylogenetic networks for which a distance, called mu-distance, is defined
       [CRV2] based on certain data (mu-data) associated to every node.  Moreover, this distance extends the Robinson-Foulds on phylogenetic
       trees. This package allows testing for a phylogenetic network if it is TC and computes mu-distances between networks over the same set of
       leaves.

       Moreover, the mu-data allows one to define the optimal (in some precise sense) alignment between networks over the same set of leaves. This
       package also computes this optimal alignment.

   Tripartitions
       Although tripartitions (see [CRV1] and the references therein) do not allow to define distances, this package outputs tripartitions and
       computes a weak form of the tripartition error.

   Time-consistency
       Another useful property of Phylogenetic Networks that appears in the literature is that of time-consistency or real-time hybrids [BSS].
       Roughly speaking, a network admits a temporal representation if it can be drawn in such a way that tree arcs (those whose end is a tree
       node) are inclined downwards, while hybridization arcs (those whose end is a hybrid node) are horizontal.  This package checks for time-
       consistency and, if so, a temporal representation is provided.

AUTHOR

	Gabriel Cardona, gabriel(dot)cardona(at)uib(dot)es
	Gabriel Valiente, valiente(at)lsi(dot)upc(dot)edu

SEE ALSO

       [CRV1]
	   G. Cardona, F. Rossello, G. Valiente. Tripartitions do not always discriminate phylogenetic networks. arXiv:0707.2376v1 [q-bio.PE]

       [CRV2]
	   G. Cardona, F. Rossello, G. Valiente. A Distance Measure for Tree-Child Phylogenetic Networks. Preprint.

       [NetGen]
	   M.M. Morin, and B.M.E. Moret. NetGen: generating phylogenetic networks with diploid hybrids. Bioinformatics 22(2006), 1921-1923

       [PhyloNet]
	   PhyloNet: "Phylogenetic Networks Toolkit".  http://bioinfo.cs.rice.edu/phylonet

       [BSS]
	   M. Baroni, C. Semple, and M. Steel. Hybrids in Real Time. Syst. Biol. 55(1):46-56, 2006

APPENDIX

       The rest of the documentation details each of the object methods.

   new
	Title	: new
	Usage	: my $obj = new Bio::PhyloNetwork();
	Function: Creates a new Bio::PhyloNetwork object
	Returns : Bio::PhyloNetwork
	Args	: none
		   OR
		  -eNewick => string
		   OR
		  -graph => Graph::Directed object
		   OR
		  -edges => reference to an array
		   OR
		  -tree => Bio::Tree::Tree object
		   OR
		  -mudata => reference to a hash,
		  -leaves => reference to an array
		   OR
		  -mudata => reference to a hash,
		  -numleaves => integer

       Returns a Bio::PhyloNetwork object, created according to the data given:

       new()
	  creates an empty network.

       new(-eNewick => $str)
	  creates the network whose Extended Newick representation (see description above) is the string $str.

       new(-graph => $graph)
	  creates the network with underlying graph given by the Graph::Directed object $graph

       new(-tree => $tree)
	  creates a network as a copy of the Bio::Tree::Tree object in $tree

       new(-mudata => \%mudata, -leaves => @leaves)
	  creates the network by reconstructing it from its mu-data stored in \%mudata and with set of leaves in @leaves.

       new(-mudata => \%mudata, -numleaves => $numleaves)
	  creates the network by reconstructing it from its mu-data stored in \%mudata and with set of leaves in ("l1".."l$numleaves").

   is_leaf
	Title	: is_leaf
	Usage	: my $b=$net->is_leaf($u)
	Function: tests if $u is a leaf in $net
	Returns : boolean
	Args	: scalar

   is_root
	Title	: is_root
	Usage	: my $b=$net->is_root($u)
	Function: tests if $u is the root of $net
	Returns : boolean
	Args	: scalar

   is_tree_node
	Title	: is_tree_node
	Usage	: my $b=$net->is_tree_node($u)
	Function: tests if $u is a tree node in $net
	Returns : boolean
	Args	: scalar

   is_hybrid_node
	Title	: is_hybrid_node
	Usage	: my $b=$net->is_hybrid_node($u)
	Function: tests if $u is a hybrid node in $net
	Returns : boolean
	Args	: scalar

   is_tree_child
	Title	: is_tree_child
	Usage	: my $b=$net->is_tree_child()
	Function: tests if $net is a Tree-Child phylogenetic network
	Returns : boolean
	Args	: Bio::PhyloNetwork

   nodes
	Title	: nodes
	Usage	: my @nodes=$net->nodes()
	Function: returns the set of nodes of $net
	Returns : array
	Args	: none

   leaves
	Title	: leaves
	Usage	: my @leaves=$net->leaves()
	Function: returns the set of leaves of $net
	Returns : array
	Args	: none

   roots
	Title	: roots
	Usage	: my @roots=$net->roots()
	Function: returns the set of roots of $net
	Returns : array
	Args	: none

   internal_nodes
	Title	: internal_nodes
	Usage	: my @internal_nodes=$net->internal_nodes()
	Function: returns the set of internal nodes of $net
	Returns : array
	Args	: none

   tree_nodes
	Title	: tree_nodes
	Usage	: my @tree_nodes=$net->tree_nodes()
	Function: returns the set of tree nodes of $net
	Returns : array
	Args	: none

   hybrid_nodes
	Title	: hybrid_nodes
	Usage	: my @hybrid_nodes=$net->hybrid_nodes()
	Function: returns the set of hybrid nodes of $net
	Returns : array
	Args	: none

   graph
	Title	: graph
	Usage	: my $graph=$net->graph()
	Function: returns the underlying graph of $net
	Returns : Graph::Directed
	Args	: none

   edges
	Title	: edges
	Usage	: my @edges=$net->edges()
	Function: returns the set of edges of $net
	Returns : array
	Args	: none

       Each element in the array is an anonimous array whose first element is the head of the edge and the second one is the tail.

   tree_edges
	Title	: tree_edges
	Usage	: my @tree_edges=$net->tree_edges()
	Function: returns the set of tree edges of $net
		  (those whose tail is a tree node)
	Returns : array
	Args	: none

   hybrid_edges
	Title	: hybrid_edges
	Usage	: my @hybrid_edges=$net->hybrid_edges()
	Function: returns the set of hybrid edges of $net
		  (those whose tail is a hybrid node)
	Returns : array
	Args	: none

   explode
	Title	: explode
	Usage	: my @trees=$net->explode()
	Function: returns the representation of $net by a set of
		  Bio::Tree:Tree objects
	Returns : array
	Args	: none

   mudata
	Title	: mudata
	Usage	: my %mudata=$net->mudata()
	Function: returns the representation of $net by its mu-data
	Returns : hash
	Args	: none

       $net->mudata() returns a hash with keys the nodes of $net and each value is a muVector object holding its mu-vector.

   heights
	Title	: heights
	Usage	: my %heights=$net->heights()
	Function: returns the heights of the nodes of $net
	Returns : hash
	Args	: none

       $net->heights() returns a hash with keys the nodes of $net and each value is its height.

   mu_distance
	Title	: mu_distance
	Usage	: my $dist=$net1->mu_distance($net2)
	Function: Computes the mu-distance between the networks $net1 and $net2 on
		  the same set of leaves
	Returns : scalar
	Args	: Bio::PhyloNetwork

   mu_distance_generalized
	Title	: mu_distance_generalized
	Usage	: my $dist=$net1->mu_distance($net2)
	Function: Computes the mu-distance between the topological restrictions of
		  networks $net1 and $net2 on its common set of leaves
	Returns : scalar
	Args	: Bio::PhyloNetwork

   tripartitions
	Title	: tripartitions
	Usage	: my %tripartitions=$net->tripartitions()
	Function: returns the set of tripartitions of $net
	Returns : hash
	Args	: none

       $net->tripartitions() returns a hash with keys the nodes of $net and each value is a string representing the tripartition of the leaves
       induced by the node.  A string "BCA..." associated with a node u (e.g.) means, the first leaf is in the set B(u), the second one in C(u),
       the third one in A(u), and so on.

   is_time_consistent
	Title	: is_time_consistent
	Usage	: my $b=$net->is_time_consistent()
	Function: tests if $net is (strong) time-consistent
	Returns : boolean
	Args	: none

   temporal_representation
	Title	: temporal_representation
	Usage	: my %time=$net->temporal_representation()
	Function: returns a hash containing a temporal representation of $net, or 0
		  if $net is not time-consistent
	Returns : hash
	Args	: none

   contract_elementary
	Title	: contract_elementary
	Usage	: my ($contracted,$blocks)=$net->contract_elementary();
	Function: Returns the network $contracted, obtained by contracting elementary
		  paths of $net into edges. The reference $blocks points to a hash
		  where, for each node of $contracted, gives the corresponding nodes
		  of $net that have been deleted.
	Returns : Bio::PhyloNetwork,reference to hash
	Args	: none

   optimal_alignment
	Title	: optimal_alignment
	Usage	: my ($weight,$alignment,$wgts)=$net->optimal_alignment($net2)
	Function: returns the total weight of an optimal alignment,
		  the alignment itself, and partial weights
		  between the networks $net1 and $net2 on the same set of leaves.
		  An optional argument allows one to use the Manhattan (default) or the
		  Hamming distance between mu-vectors.
	Returns : scalar,reference to hash,reference to hash
	Args	: Bio::PhyloNetwork,
		  -metric => string (optional)

       Supported strings for the -metric parameter are 'Manhattan' or 'Hamming'.

   optimal_alignment_generalized
	Title	: optimal_alignment_generalized
	Usage	: my ($weight,%alignment)=$net->optimal_alignment_generalized($net2)
	Function: returns the wieght of an optimal alignment, and the alignment itself,
		  between the topological restriction of the networks $net1 and $net2
		  on the set of common leaves.
		  An optional argument allows one to use the Manhattan (default) or the
		  Hamming distance between mu-vectors.
	Returns : scalar,hash
	Args	: Bio::PhyloNetwork,
		  -metric => string (optional)

       Supported strings for the -metric parameter are 'Manhattan' or 'Hamming'.

   topological_restriction
	Title	: topological_restriction
	Usage	: my ($netr1,$netr2)=$net1->topological_restriction($net2)
	Function: returns the topological restriction of $net1 and $net2 on its
		  common set of leaves
	Returns : Bio::PhyloNetwork, Bio::PhyloNetwork
	Args	: Bio::PhyloNetwork

   eNewick
	Title	: eNewick
	Usage	: my $str=$net->eNewick()
	Function: returns the eNewick representation of $net without labeling
		  internal tree nodes
	Returns : string
	Args	: none

   eNewick_full
	Title	: eNewick_full
	Usage	: my $str=$net->eNewick_full()
	Function: returns the eNewick representation of $net labeling
		  internal tree nodes
	Returns : string
	Args	: none

   display
	Title	: display
	Usage	: my $str=$net->display()
	Function: returns a string containing all the available information on $net
	Returns : string
	Args	: none

perl v5.14.2							    2012-03-02						    Bio::PhyloNetwork(3pm)