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Infers a species network(s) with a specified number of reticulation nodes using maximum likelihood. The returned species network(s) will have inferred branch lengths and inheritance probabilities. To find the optimal network, steepest descent is used. For every network topology being examined, we use Richard Brent's algorithm (from his book "Algorithms for Minimization without Derivatives", p. 79) to optimize the branch lengths to obtain the maximum likelihood score for that topology. The species network and gene trees must be specified in the Rich Newick Format. However, only topologies of them are used in the method.

The input gene trees can be gene tree distributions inferred from Bayesian methods like MrBayes. See the second example below.

Usage

InferNetwork_ml (gene_tree_ident1 [, gene_tree_ident2...|, gene_tree_ident2...]) numReticulations [-a taxa map] [-b threshold] [-s startingNetwork] [-n numNetReturned] [-m maxNetExamined] [-d maxDiameter] [-p (rel,abs)] [-r maxRounds] [-t maxTryPerBr] [-i improveThreshold] [-l maxBL] [-di] [result output file]

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By default, it is assumed that only one individual is sampled per species in gene trees. However, the option [-a taxa map] allows multiple alleles to be sampled.

Examples

#NEXUS

BEGIN TREES;

Tree geneTree1 = ((C,((B,D),A)),E);
Tree geneTree2 = (B,(D,(C,(A,E))));
Tree geneTree3 = (D,(B,((C,E),A)));
Tree geneTree4 = (D,((B,E),(C,A)));

END;


BEGIN PHYLONET;

InferNetwork_ML (geneTree1,geneTree2,geneTree3,geneTree4) 1;

END;

#NEXUS

BEGIN TREES;

Tree geneTree1 = [&W 0.9] ((C,((B,D),A)),E);
Tree geneTree2 = [&W 0.1] (B,(D,(C,(A,E))));
Tree geneTree3 = [&W 0.6] (D,(B,((C,E),A)));
Tree geneTree4 = [&W 0.4] (D,((B,E),(C,A)));

END;


BEGIN PHYLONET;

InferNetwork_ML (geneTree1,geneTree2,geneTree3,geneTree4) 1;

END;

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