These were calculated using the corresponding evolutionary model chosen by Modeltest by ML (100 replicates), distance NJ (1000 replicates), and MP (1000 replicates) methods using the accordant settings/evolutionary model for each dataset.
However, it seems that these two conditions may be playing a significant role for these by themselves do replicate the distance decay function.
Further experiments using parsimony (PROTPARS, one jumble per replicate) and distance (PROTDIST, Dayhoff PAM matrix and NEIGHBOR, neighbor joining method) tree construction methods implemented in PHYLIP v.3.66, 3.67 or 3.68 [47] also yielded congruent results.
Because the most fit allele size and the starting distribution of allele sizes were randomly determined for each replicate, this distance varied between replicates.
Node bootstrap values for the tree constructed using cytb sequences were determined in PAUP* v4.0beta10 using both Maximum Likelihood (ML; 1000 "fast" stepwise-addition replicates) and Distance (1000 full heuristic replicates) algorithms.
For genes with p<0.05, the average ratio was used as the final -fold change; for genes with p>0.05, the inconsistent replicates (with distance to the median of replicate ratios >0.8) were removed, and only the values from genes with at least two replicates in the same direction of regulation were further averaged.
We assessed the robustness of nodes using tree reconstructions of bootstrap-resampled data sets for 1,000 replicates under distance and parsimony criteria, and 200 replicates for ML criteria.
DNA methylation patterns of the technical replicates were highly similar, showing greater resemblance among the replicates (Euclidean distance, ED = 12.24) than within-twin pairs (ED = 13.86 for concordant and 15.75 for discordant pairs), and greater similarity within pairs than between-same sex unrelated individuals (ED = 21.65, Additional file 2: Figure S2).
But some aspects of teaching cannot be replicated by distance learning.
For this approach we compute the SNR as: text{SNR}(d) = frac{B(d)^{T} hat{beta}}{hat{sigma}_{b}(d)} where (hat {beta }) is the estimated spline weight and (hat {sigma }_{b}(d)) is the estimated standard deviation across replicates at distance d between the 5 spline fits at that altitude.
