A combination of individual based maximum likelihood methods and population based Bayesian methods were employed to overcome the potential challenge of detecting gene flow under low differentiation.
Using this suggested evolutionary model, tree reconstruction was accomplished using both distance based (Neighbor-Joining) and character based (Maximum Likelihood) methods.
A variance components-based maximum likelihood method was used to estimate the h2 of the different serum inflammatory markers while simultaneously adjusting for the effects of known CVD risk factors, such as age and smoking.
To test the selective pressure acting on OR genes in primates, we performed codon-based maximum likelihood method using CODEML nested in PAML package (Yang 2007).
Three different codon-based maximum likelihood methods, SLAC, FEL and REL, can be used to estimate the dN/dS ratio at every codon in the alignment.
Suggestions of positive, or diversifying, selection acting in IHHNV codon sites were evaluated using three different codon-based maximum likelihood methods: Random Effects Likelihood (REL), Fixed Effects Likelihood (FEL) and Single Likelihood Ancestor Counting (SLAC) [37].
We used three different codon-based maximum likelihood methods all with partitioning pre-analyses to screen for selection signals.
Four different codon-based maximum likelihood methods, SLAC, FEL, REL [49], and FUBAR [50], were used estimate the dN/ dS (also known as Ka/Ks or ω) ratio at every codon in the alignment.
However, despite the possibility of previous exposure to TTX (or a TTX-like molecule) being a credible hypothesis for the evolution of a TTX-insensitive channel in aphids, we were unable to detect any fingerprints of positive selection for TTX-insensitivity in the aphid channels on comparative analysis of multiple Nav1 sequence alignments using several codon-based maximum likelihood methods [36].
The proposed BVSA algorithm is also performs better than a recently proposed Levenberg-Marquardt optimization based Maximum Likelihood (LMML) method [ 18] and a previously developed sparse Bayesian regression method (SBRA) [ 7].
