In S. cerevisiae, both forward and reverse mutation rate studies have shown that the MutS and MutL homologs are required for base correction [1], [2].
It now seems likely that most cancers are initiated in cells because of this normal mutation rate: studies that have measured the numbers of mutations in common cancers have provided evidence consistent with this natural rate.
In a previous comprehensive Y-STR mutation rate study where nearly 200 Y-STRs were investigated in almost 2,000 father son pairs confirmed by autosomal DNA analysis [Ballantyne et al., 2010], 13 Y-STRs with exceptionally high (>10−2 per locus per generation) mutation rates were identified and termed rapidly mutating (RM) Y-STRs.
Hence rather than studying the equilibrium probability of each weight value in an infinite population size with a given mutation rate, we studied the probability to observe a given weight value in a finite population of size N with a very small mutation rate.
Even though this observation might be biased due to the use of improper molecular models when calculating the trees, biased mutation rate in studied genes, or a biased taxon sampling, it could indicate that the diversifications of typical antbirds was characterized by some rapid speciation bursts.
We considered two mutation rates in our simulation study, namely zero mutation rate for studying the ideal case, and a high mutation rate of 1% with SNV to indel ratio of 9 1, for studying the more difficult situations.
To date, little attempts have been made to utilize such parasites with an accelerated mutation rate to study malaria parasite biology.
To investigate the interplay of helpers and parasites for the stability of the system (especially at high mutation rates), we study a simple system of ordinary differential equation (ODE) model.
The exact temporal scale of dispersal remains uncertain due to the absence of an evolutionary timeframe of cyanobacteria, as well as limits of the design of the study and the temporal resolution that is inherent to the mutation rate of the studied ITS fragment.
MDIV estimates the migration rate per gene per generation between populations scaled by the effective population size (M = 2Nem), the time since the two populations diverged scaled by the effective population size (T = t/2 Ne), and the parameter theta, which is a product of the effective population size and the mutation rate of the studied gene region, (θ = 4Neμ).
