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MLV phylogenies were also inferred using the program PhyML that implements the fast maximum likelihood (ML) method [38].

The phylogeny of the haplotypes was also inferred using the statistical parsimony (SP) criterion [ 76].

ML trees were also inferred using the high-throughput phylogenomics webserver, MultiPhyl [ 34].

Phylogenetic trees were also inferred using the Bayesian method implemented in MrBayes v3.2 [ 51].

In addition, mutation matrices were also inferred using the method of Nielsen et al. (2007) for both Arabidopsis species.

In addition, positively selected sites was also inferred using the Random effects likelihood (REL) method implemented in the DATAMONKEY web server [ 65].

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The phylogeny was also inferred using BI as the optimality criterion, which resulted in a nearly identical topology (branching patterns) and similar nodal support compared to ML (bootstrap proportions were largely correlated to posterior probability values as indicated by the first and second numbers at each node).

Phylogenies were also inferred using each of the other three MLST schemes (see Additional file 2).

The physical locations of the confidence intervals around genetic positions of centromeres were also inferred using local regression between the genetic and physical positions of the flanking markers.

The network structure was also inferred using a simulated annealing algorithm to overcome the potential risk of inferring networks at local maxima instead of global maximum [ 30].

ML trees and 1,000 bootstrap trees were also inferred using PhyML 2.4.4 [ 34] with the WAG model with 8 gamma rate categories and one category of invariable sites (when p-inv was not zero), and parameters estimated from the data.

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