When we aim to study divergence in different regions of the genome while avoiding some biases such as overlapping SDs or copies in non-comparable regions of the genome, we should use the non overlapping datasets, either for intraspecific divergence (dataset 2) or interspecific divergence (dataset 3).
Small numbers of absolute headcounts, with relatively larger fractions of locum GPs in rural areas cause unstable statistical estimates and divergences between datasets.
Subsequently, we estimated mean divergence within dataset composed of sequences from the four closely related focal species and compared it to estimated divergence at 3rd positions to get an idea about relative mutation rate in both datasets.
In order to group the Onthophagus sequences according to patterns of conservation and divergence across these datasets we filtered them for those with BLASTx sequence matches with proteins in the various datasets (E-value < 1 × 10-5), and then clustered them according to the bit scores.
Correlations were performed with the statistical package JMP 5.0.1a to evaluate the degree of divergence between paired datasets and estimate the impact of PCR approaches (Figure 1A), size fractionation (Figure 1B), and 18S rDNA versus 18S rRNA clones libraries (Figure 1C), on environmental diversity surveys.
When counts of polymorphism and divergence differed between datasets, they typically differed by a single count.
There was also regularity in the estimated divergence time among datasets, all results pointed towards numbers around 0.5 million years.
It is evident that on balanced datasets, divergence also shows significant predictive power in distinguishing between the two different kinds of N-terminal signals, even for the relatively closely related mammal species.
This practice enables to overcome divergences in some datasets associated with the solely use of MLE-We3.
As a consequence of the mismatch distribution analysis for the whole dataset, divergence dates of the main clades (Figure 4) were calculated under a Bayesian relaxed-clock method assuming constant population size.
Once again, the size and divergence of the dataset defines the limits to the power of the analyses.
