The best gene(s) to eliminate can be identified by using the tree based on the concatenated sequences of all genes as the reference tree, and examining the maximum likelihood of each of the single gene trees as a fit to the reference tree, eliminating those with the lowest likelihood.
Following SPADE, we examined the colored trees to manually identify the ML, LPC, MB or BPC populations represented by each part of the tree based on their known cell surface markers (Additional file 1: Figure S2A).
The sequence Bdi| Brachypodium distachyon Bradi1g18780.1 was misplaced in both trees generated by the ML method and in the tree based on the PAE1 LOGO by the MP method.
We found a positive association between the length of the low-complexity sequence and its variability approximated by the total length of the tree based on a matrix of pairwise LCR size differences between strains (ρ = 0.21589, P < 0.001; fig. 3).
When compared with the phylogenetic tree based on whole genome alignment of MTBC as the gold standard, the tree based on SNP concatemers from CS showed essentially the same topology as the maximum-likelihood phylogenetic tree chosen by aLTR statics, whereas the tree based on the H37Rv sequence showed different positioning of RGTB327 and RGTB423.
In the present study, three SCSMV genotypes (I, II, and III) were also clustered by the phylogenetic tree based on the CP nucleotide (572 nt) sequences from YN-YZ211 isolate and 24 representative isolates published in the GenBank database).
By structuring the decision tree based on the results of previously reported trials, we extrapolated the costs and effects over a lifetime horizon, taking into account the probability of developing cerebral palsy and its effect on QALYs.
This hypothesis is supported by the fact that selecting the best tree based on the quartet support produced improved topological accuracy compared to selecting the best tree based on the pseudo-likelihood score, and that the quartet support optimization criterion is statistically consistent under the multi-species coalescent model [ 25].
Candidate Web pages are cleaned and converted into regular texts (A Web page in HTML format is cleaned by pruning the DOM tree based on typographical clues (punctuation signs and uppercase characters frequencies, length of sentences, number of non-alphanumeric characters, etc)., so as to remove irrelevant parts of the document such as menus, advertisements, abstracts, or copyright notifications).
The between-ness was calculated by the "breath-first tree" based algorithm as following steps [ 18, 37].
