Computational protocol: Discovery of the photosynthetic relatives of the "Maltese mushroom" Cynomorium

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Protocol publication

[…] The global and Saxifragales data sets were analyzed using maximum parsimony (MP) in PAUP* 4.0b10 [] and Bayesian inference (BI) methods in MrBayes 3.0b4 []. MP searches were performed using 100 random addition sequence replicates with tree-bisection-reconnection (TBR) branch-swapping, holding ten trees at each addition step, with all sites equally weighted. Fully partitioned Bayesian analyses were performed by partitioning each data set by gene and, for the protein-coding genes, by codon position. This resulted in a total of 10 partitions for the full data set and 11 partitions for the Saxifragales data set (Table ). MP trees were constructed for each gene (following the protocol described above), and these trees were used in PAUP* to evaluate 24 nucleotide substitution models for each data partition. For example, models were evaluated for the "rbcL Pos1" partition (the first codon position of the rbcL data set) on the MP tree for the rbcL data set. MrModelTest 2.0 [] was used to select an appropriate model from the PAUP* output via a second-order version of the Akaike Information Criterion (AICc) that takes sample size into account, as recommended by Posada and Buckley []. AICc values were computed using both the total number of characters and the number of variable characters per partition. For nearly all partitions, the Akaike weight for the chosen model was much higher than the Akaike weight of the next best model, so model-averaged analyses were not performed. The best-fitting models and their Akaike weights for each data partition are listed in Supplementary Data. Partitioned Bayesian analyses were performed with all model parameters unlinked (i.e., model parameters for each data partition were estimated separately from each partition), and with topology and branch lengths linked. Two separate analyses (with different MCMC seeds, random starting trees and default uniform priors for all parameters) were run for each data set for 15 million generations, with trees sampled every 500 generations. Trees recovered during the first 2.5 million generations (the first 5000 trees) in both runs were discarded as burn-in, leaving a total of 25,000 trees which were used to construct majority-rule consensus trees. […]

Pipeline specifications

Software tools PAUP*, MrBayes, MrModelTest
Application Phylogenetics