Towards a revised generic classification of lecanoroid lichens (Lecanoraceae, Ascomycota) based on molecular, morphological and chemical evidence

A key element to a successful Markov chain Monte Carlo (MCMC) inference is the programming and run performance of the Markov chain. However, the explicit use of quality assessments of the MCMC simulations-convergence diagnostics-in phylogenetics is still uncommon. Here, we present a simple tool that uses the output from MCMC simulations and visualizes a number of properties of primary interest in a Bayesian phylogenetic analysis, such as convergence rates of posterior split probabilities and branch lengths. Graphical exploration of the output from phylogenetic MCMC simulations gives intuitive and often crucial information on the success and reliability of the analysis. The tool presented here complements convergence diagnostics already available in other software packages primarily designed for other applications of MCMC. Importantly, the common practice of using trace-plots of a single parameter or summary statistic, such as the likelihood score of sampled trees, can be misleading for assessing the success of a phylogenetic MCMC simulation. The program is available as source under the GNU General Public License and as a web application at http://ceb.scs.fsu.edu/awty.

Multiple alignment of DNA sequences is an important step in various molecular biological analyses. As a large amount of sequence data is becoming available through genome and other large-scale sequencing projects, scalability, as well as accuracy, is currently required for a multiple sequence alignment (MSA) program. In this chapter, we outline the algorithms of an MSA program MAFFT and provide practical advice, focusing on several typical situations a biologist sometimes faces. For genome alignment, which is beyond the scope of MAFFT, we introduce two tools: TBA and MAUVE.

The problem of inferring confidence sets of gene trees is discussed without assuming that the substitution model or the branching pattern of any of the investigated trees is correct. In this case, widely used methods to compare genealogies can give highly contradicting results. Here, three methods to infer confidence sets that are robust against model misspecification are compared, including a new approach based on estimating the confidence in a specific tree using expected-likelihood weights. The power of the investigated methods is studied by analysing HIV-1 and mtDNA sequence data as well as simulated sequences. Finally, guidelines for choosing an appropriate method to compare multiple gene trees are provided.