BackgroundGenomic islands play an important role in microbial genome evolution, providing a mechanism for strains to adapt to new ecological conditions. A variety of computational methods, both genome-composition based and comparative, have been developed to identify them. Some of these methods are explicitly designed to work in single strains, while others make use of multiple strains. In general, existing methods do not identify islands in the context of the phylogeny in which they evolved. Even multiple strain approaches are best suited to identifying genomic islands that are present in one strain but absent in others. They do not automatically recognize islands which are shared between some strains in the clade or determine the branch on which these islands inserted within the phylogenetic tree.ResultsWe have developed a software package, xenoGI, that identifies genomic islands and maps their origin within a clade of closely related bacteria, determining which branch they inserted on. It takes as input a set of sequenced genomes and a tree specifying their phylogenetic relationships. Making heavy use of synteny information, the package builds gene families in a species-tree-aware way, and then attempts to combine into islands those families whose members are adjacent and whose most recent common ancestor is shared. The package provides a variety of text-based analysis functions, as well as the ability to export genomic islands into formats suitable for viewing in a genome browser. We demonstrate the capabilities of the package with several examples from enteric bacteria, including an examination of the evolution of the acid fitness island in the genus Escherichia. In addition we use output from simulations and a set of known genomic islands from the literature to show that xenoGI can accurately identify genomic islands and place them on a phylogenetic tree.ConclusionsxenoGI is an effective tool for studying the history of genomic island insertions in a clade of microbes. It identifies genomic islands, and determines which branch they inserted on within the phylogenetic tree for the clade. Such information is valuable because it helps us understand the adaptive path that has produced living species.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2038-0) contains supplementary material, which is available to authorized users.

The Drake Passage Time‐series (DPT) is used to quantify the spatial and seasonal variability of historically undersampled, biogeochemically relevant properties across the Drake Passage. From 2004–2017, discrete ship‐based observations of surface macronutrients (silicate, nitrate, and phosphate), temperature, and salinity have been collected 5–8 times per year as part of the DPT program. Using the DPT and Antarctic Circumpolar Current (ACC) front locations derived from concurrent expendable bathythermograph data, the distinct physical and biogeochemical characteristics of ACC frontal zones are characterized. Biogeochemical‐Argo floats in the region confirm that the near‐surface sampling scheme of the DPT robustly captures mixed‐layer biogeochemistry. While macronutrient concentrations consistently increase toward the Antarctic continent, their meridional distribution, variability, and biogeochemical gradients are unique across physical ACC fronts, suggesting a combination of physical and biological processes controlling nutrient availability and nutrient front location. The Polar Front is associated with the northern expression of the Silicate Front, marking the biogeographically relevant location between silicate‐poor and silicate‐rich waters. South of the northern Silicate Front, the silicate‐to‐nitrate ratio increases, with the sharpest gradient in silicate associated with the Southern ACC Front (i.e., the southern expression of the Silicate Front). Nutrient cycling is an important control on variability in the surface ocean partial pressure of carbon dioxide (pCO2). The robust characterization of the spatiotemporal variability of nutrients presented here highlights the utility of biogeochemical time series for diagnosing and potentially reducing biases in modeling Southern Ocean pCO2 variability, and by inference, air‐sea CO2 flux.

Background:Genomic islands play an important role in microbial genome evolution, providing a mechanism for strains to adapt to new ecological conditions. A variety of computational methods, both genome-composition based and comparative have been developed to identify them. Some of these methods are explicitly designed to work in single strains, while others make use of multiple strains. In general, existing methods do not identify islands in the context of the phylogeny in which they evolved. Even multiple strain approaches are best suited to identifying genomic islands that are present in one strain but absent in others. They do not automatically recognize islands which are shared between some strains in the clade or determine the branch on which these islands inserted within the phylogenetic tree. Results:We have developed a software package, xenoGI, that identifies genomic islands and maps their origin within a clade of closely related bacteria, determining which branch they inserted on. It takes as input a set of sequenced genomes and a tree specifying their phylogenetic relationships. Making heavy use of synteny information, the package builds gene families in a species-tree-aware way, and then attempts to combine into islands those families whose members are adjacent and whose most recent common ancestor is shared. The package provides a variety of text-based analysis functions, as well as the ability to export genomic islands into formats suitable for viewing in a genome browser. We demonstrate the capabilities of the package with several examples from enteric bacteria, including an examination of the evolution of the acid fitness island in the genus Escherichia. In addition we use output from simulations and a set of known genomic islands from the literature to show that xenoGI can accurately identify genomic islands and place them on a phylogenetic tree. Conclusions:xenoGI is an effective tool for studying the history of genomic island insertions in a clade of microbes. It identifies genomic islands, and determines which branch they inserted on within the phylogenetic tree for the clade. Such information is valuable because it helps us understand the adaptive path that has produced living species. Given the large and growing number of sequenced microbial genomes, this sort of analysis will become increasingly useful in the future.