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 Qinghai-Tibet Plateau is an ecologically fragile region. The changes in physicochemical parameters of water quality [PPOWQ] and land use types [LUT] in different regions will affect the phytoplankton community in rivers, thus threatening the ecosystem. Taking the phytoplankton community as an indicator variable, it is of great significance to study the relative influence of symbiotic factors on regulating human activities and river ecological protection. The results showed that the proportions of Bacillariophyta, Cyanophyta, and Chlorophyta were >84% in the phytoplankton community of taxa composition. The abundance of the phytoplankton community varied from 1.47 × 105 to 7.58 × 105 cells/L. Bacillariophyta had the highest average abundance (>82%). The results of the variance partitioning analysis showed that PPOWQ was the main variable affecting the changes in the phytoplankton community. Redundancy analysis showed that local factors (total nitrogen, salinity, water temperature) and regional factors (forestland, grassland, unused land) (p < 0.05) were the main factors causing the changes in community structure and abundance of dominant algae. The analysis of structural equation models showed that LUT had the least direct impact on the abundance of the phytoplankton community, mainly through changing nutrients and physical parameters. Water temperature and nutrients are still the main factors affecting phytoplankton community abundance. Farmland and forestland are the main sources of total nitrogen in rivers. In general, in the ecologically vulnerable area, it is of guiding significance for the ecological monitoring and management of plateau rivers. In addition to considering water quality, it is also necessary to reasonably plan the LUT around rivers.
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.
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