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Nucleic Acids Res. 2016 Jul 8;44(W1):W22-8. doi: 10.1093/nar/gkw255. Epub 2016 Apr 20.

EDGAR 2.0: an enhanced software platform for comparative gene content analyses.

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Bioinformatics & Systems Biology, Justus-Liebig-University Giessen, 35392 Giessen, Hesse, Germany
Bioinformatics & Systems Biology, Justus-Liebig-University Giessen, 35392 Giessen, Hesse, Germany.
Institute of Microbiology, University Hospital Center and University of Lausanne, 1011 Lausanne, VD, Switzerland SIB Swiss Institute of Bioinformatics, 1015 Lausanne, VD, Switzerland.
Center for Familial Breast and Ovarian Cancer, Medical Faculty, University Hospital Cologne, University of Cologne, 50931 Cologne, NRW, Germany.


The rapidly increasing availability of microbial genome sequences has led to a growing demand for bioinformatics software tools that support the functional analysis based on the comparison of closely related genomes. By utilizing comparative approaches on gene level it is possible to gain insights into the core genes which represent the set of shared features for a set of organisms under study. Vice versa singleton genes can be identified to elucidate the specific properties of an individual genome. Since initial publication, the EDGAR platform has become one of the most established software tools in the field of comparative genomics. Over the last years, the software has been continuously improved and a large number of new analysis features have been added. For the new version, EDGAR 2.0, the gene orthology estimation approach was newly designed and completely re-implemented. Among other new features, EDGAR 2.0 provides extended phylogenetic analysis features like AAI (Average Amino Acid Identity) and ANI (Average Nucleotide Identity) matrices, genome set size statistics and modernized visualizations like interactive synteny plots or Venn diagrams. Thereby, the software supports a quick and user-friendly survey of evolutionary relationships between microbial genomes and simplifies the process of obtaining new biological insights into their differential gene content. All features are offered to the scientific community via a web-based and therefore platform-independent user interface, which allows easy browsing of precomputed datasets. The web server is accessible at

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