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The Analysis of Variance allows the comparison of the effects of all other variables (number of taxa, method of analysis, model of simulation, coevolution factor) on the dependent variable (the AUC), as well as checking if any of these variables interact with each other (for example, a given method of analysis being much better under a specific model of simulation).
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The lowest taxonomic entry is also rejected by the searchFullTaxa-method because the keyword 'environmental samples' is detected.
On simulated datasets with up to 16 taxa, our method correctly detected recombination breakpoints and the number of recombination events for each breakpoint.
When applied to genomic contigs of a recent human gut microbiome study, the taxa counting method provides information on numbers of different species and strains.
As the amino acid composition within proteins varies among taxa, our method removes the need to allow for the evolutionary gain and loss of amino acids and increases the generalization capability of SVM training.
Given that extraction of chloroplast DNA from a wide range of rainforest tree species is time consuming and would require optimisation across a range of taxa, this method using whole genomic DNA, sequenced on the Illumina platform, is preferable for large scale comparative studies of plants.
With small numbers of taxa both methods have similar performance.
Each family of gene sequences was limited to its unique representatives among AMPHORA taxa (see Methods).
In addition, polyploidy is known to occur based on previously reported chromosome counts in some taxa (see Methods and [ 29]).
Given the fact that few genes are sampled very completely across many taxa, supermatrix methods often sacrifice completeness in the interest of size.
Consequently, we analyzed data sets under the time-heterogeneous model "BP" [ 50, 51], which is designed to deal with compositional heterogeneity across taxa (see Methods).
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