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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 inference can be made using only topologies of gene trees, or using both topologies and branch lengths of gene trees. The latter one requires the input gene trees to be ultrametric.
The input gene trees can be gene tree distributions inferred from Bayesian methods like MrBayes. See the second example below.
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gene_tree_ident1 [, gene_tree_ident2...] | Comma delimited list of gene tree identifiers. | mandatory |
numReticulations | Maximum number of reticulations to added. | mandatory |
-b threshold | Gene trees bootstrap threshold. | optional |
-a taxa map | Gene tree / species tree taxa association. | optional |
| -bl | Use the branch lengths of the gene trees for the inference. | Opoptional |
-s startingNetwork | Specify the network to start search. Default value is the optimal MDC tree. | optional |
-n numNetReturned | Number of optimal networks to return. Default value is 1. | optional |
| -h {s1 [, s2...]} | A set of specified hybrid species. The size of this set equals the number of reticulation nodes in the inferred network. | optional |
| -w (w1, w2, w3, w4) | The weights of operations for network arrangement during the network search. Default value is (0.15, 0.15, 0.2, 0.5). | optional |
| -f maxFailure | The maximum number of consecutive failures before the search terminates. Default value is 100. | optional |
-x numRuns | The number of runs of the search. Default value is 5. | optional |
-m maxNetExamined | Maximum number of network topologies to examined. Default value is infinity. | optional |
-d maxDiameter | Maximum diameter to make an arrangement during network search. Default value is infinity. | optional |
-p (rel, abs) | The original stopping criterion of Brent’s algorithm. Default value is (0.01, 0.001). | optional |
-r maxRound | Maximum number of rounds to optimize branch lengths for a network topology. Default value is 100. | optional |
-t maxTryPerBr | Maximum number of trial per branch in one round to optimize branch lengths for a network topology. Default value is 100. | optional |
-i improveThreshold | Minimum threshold of improvement to continue the next round of optimization of branch lengths. Default value is 0.001. | optional |
-l maxBL | Maximum branch lengths considered. Default value is 6. | optional |
-pl numProcessors | Number of processors if you want the computation to be done in parallel. Default value is 1. | optional |
| -di | Output the Rich Newick string of the inferred network that can be read by Dendroscope . | optional |
result output file | Optional file destination for command output. | optional |
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| Code Block | ||
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#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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| Code Block | ||
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#NEXUS
BEGIN TREES;
Tree geneTree1 = ((C:3,((B:1,D:1):1,A:2):1):1,E:4);
Tree geneTree2 = (B:4,(D:3,(C:2,(A:1,E:1):1):1):1);
Tree geneTree3 = (D:4,(B:3,((C:1,E:1):1,A:2):1):1);
Tree geneTree4 = (D:3,((B:1,E:1):1,(C:1,A:1):1):1);
END;
BEGIN PHYLONET;
InferNetwork_ML (geneTree1,geneTree2,geneTree3,geneTree4) 1 -bl;
END;
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Command References
- Y. Yu, N. Ristic and L. Nakhleh. Fast algorithms and Heuristics for Phylogenomics under hybridization and incomplete lineage sorting. BMC Bioinformatics, vol. 14, no. Suppl 15, p. S6, 2013.
Y. Yu, J. Dong, K. Liu, and L. Nakhleh, Probabilistic inference of reticulate evolutionary histories, Under Review.
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