A description of the genetic makeup of a species based on a single genome is often insufficient because it ignores the variability in gene repertoire among multiple strains. The estimation of the pangenome of a species is a solution to this issue as it provides an overview of genes that are shared by all strains and genes that are present in only some of the genomes. These different sets of genes can then be analyzed functionally to explore correlations with unique phenotypes and adaptations. This protocol presents the usage of Roary, a Linux-native pangenome application. Roary is a straightforward software that provides 1) an overview about core and accessory genes for those interested in general trends and, also, 2) detailed information on gene presence/absence in each genome for in-depth analyses. Results are provided both in text and graphic format.

Low complexity regions (LCRs) are a common feature shared by many genomes, but their evolutionary and functional significance remains mostly unknown. At the core of the uncertainty is a poor understanding of the mechanisms that regulate their retention in genomes, whether driven by natural selection or neutral evolution. Applying a comparative approach of LCRs to multiple strains and species is a powerful approach to identify patterns of conservation in these regions. Using this method, we investigate the evolutionary history of LCRs in the genus Plasmodium based on orthologous protein coding genes shared by 11 species and strains from primate and rodent-infecting pathogens. We find multiple lines of evidence in support of natural selection as a major evolutionary force shaping the composition and conservation of LCRs through time and signatures that their evolutionary paths are species specific. Our findings add a comparative analysis perspective to the debate on the evolution of LCRs and harness the power of sequence comparisons to identify potential functionally important LCR candidates.

Low complexity regions (LCRs) are a ubiquitous feature in genomes and yet their evolutionary history and functional roles are unclear. Previous studies have shown contrasting evidence in favor of both neutral and selective mechanisms of evolution for different sets of LCRs suggesting that modes of identification of these regions may play a role in our ability to discern their evolutionary history. To further investigate this issue, we used a multiple threshold approach to identify species-specific profiles of proteome complexity and, by comparing properties of these sets, determine the influence that starting parameters have on evolutionary inferences. We find that, although qualitatively similar, quantitatively each species has a unique LCR profile which represents the frequency of these regions within each genome. Inferences based on these profiles are more accurate in comparative analyses of genome complexity as they allow to determine the relative complexity of multiple genomes as well as the type of repetitiveness that is most common in each. Based on the multiple threshold LCR sets obtained, we identified predominant evolutionary mechanisms at different complexity levels, which show neutral mechanisms acting on highly repetitive LCRs (e.g., homopolymers) and selective forces becoming more important as heterogeneity of the LCRs increases. Our results show how inferences based on LCRs are influenced by the parameters used to identify these regions. Sets of LCRs are heterogeneous aggregates of regions that include homo- and heteropolymers and, as such, evolve according to different mechanisms. LCR profiles provide a new way to investigate genome complexity across species and to determine the driving mechanism of their evolution.

Using calibrations to obtain absolute divergence times is standard practice in molecular clock studies. While the use of primary (e.g., fossil) calibrations is preferred, this approach can be limiting because of their rarity in fast-growing datasets. Thus, alternatives need to be explored, such as the use of secondary (molecularly-derived) calibrations that can anchor a timetree in a larger number of nodes. However, the use of secondary calibrations has been discouraged in the past because of concerns in the error rates of the node estimates they produce with an apparent high precision. Here, we quantify the amount of errors in estimates produced by the use of secondary calibrations relative to true times and primary calibrations placed on distant nodes. We find that, overall, the inaccuracies in estimates based on secondary calibrations are predictable and mirror errors associated with primary calibrations and their confidence intervals. Additionally, we find comparable error rates in estimated times from secondary calibrations and distant primary calibrations, although the precision of estimates derived from distant primary calibrations is roughly twice as good as that of estimates derived from secondary calibrations. This suggests that increasing dataset size to include primary calibrations may produce divergence times that are about as accurate as those from secondary calibrations, albeit with a higher precision. Overall, our results suggest that secondary calibrations may be useful to explore the parameter space of plausible evolutionary scenarios when compared to time estimates obtained with distant primary calibrations.

The large size of modern datasets has inspired a variety of strategies to alter genes, sites and/or species compositions to improve the accuracy of phylogenetic reconstructions. Each of these data-filtering approaches leads to the exclusion of different types of data, which is known to affect phylogenetic outcome. However, the choice of a filtering strategy is often subjective and the robustness of the results to alternative strategies is not usually investigated. A case study is provided by archaean phylogenies, that have recently relied on filtering fast evolving sites. Here we compare the outcomes of two filtering strategies: fast evolving genes or fast evolving sites to investigate the robustness of Archaea phylogenies to data manipulation. Our results show that the two approaches lead to different outcomes and that excluding fast evolving sites might not be as effective as removing fast evolving genes. Topologies obtained from filtering either fast evolving or random sites are twice as likely to be similar (72%) than those obtained from filtering fast genes (36%). Our results suggest that the phylogenies of Archaea from many recent studies may be driven by the data filtering choice and that robustness to different types of data manipulation should be systematically investigated.