The recent advent of DNA sequencing technologies facilitates the use of genome sequencing data that provide means for more informative and precise classification and identification of members of the Bacteria and Archaea. Because the current species definition is based on the comparison of genome sequences between type and other strains in a given species, building a genome database with correct taxonomic information is of paramount need to enhance our efforts in exploring prokaryotic diversity and discovering novel species as well as for routine identifications. Here we introduce an integrated database, called EzBioCloud, that holds the taxonomic hierarchy of the Bacteria and Archaea, which is represented by quality-controlled 16S rRNA gene and genome sequences. Whole-genome assemblies in the NCBI Assembly Database were screened for low quality and subjected to a composite identification bioinformatics pipeline that employs gene-based searches followed by the calculation of average nucleotide identity. As a result, the database is made of 61 700 species/phylotypes, including 13 132 with validly published names, and 62 362 whole-genome assemblies that were identified taxonomically at the genus, species and subspecies levels. Genomic properties, such as genome size and DNA G+C content, and the occurrence in human microbiome data were calculated for each genus or higher taxa. This united database of taxonomy, 16S rRNA gene and genome sequences, with accompanying bioinformatics tools, should accelerate genome-based classification and identification of members of the Bacteria and Archaea. The database and related search tools are available at www.ezbiocloud.net/.

Average nucleotide identity (ANI) is a category of computational analysis that can be used to define species boundaries of Archaea and Bacteria. Calculating ANI usually involves the fragmentation of genome sequences, followed by nucleotide sequence search, alignment, and identity calculation. The original algorithm to calculate ANI used the BLAST program as its search engine. An improved ANI algorithm, called OrthoANI, was developed to accommodate the concept of orthology. Here, we compared four algorithms to compute ANI, namely ANIb (ANI algorithm using BLAST), ANIm (ANI using MUMmer), OrthoANIb (OrthoANI using BLAST) and OrthoANIu (OrthoANI using USEARCH) using >100,000 pairs of genomes with various genome sizes. By comparing values to the ANIb that is considered a standard, OrthoANIb and OrthoANIu exhibited good correlation in the whole range of ANI values. ANIm showed poor correlation for ANI of <90%. ANIm and OrthoANIu runs faster than ANIb by an order of magnitude. When genomes that are larger than 7 Mbp were analysed, the run-times of ANIm and OrthoANIu were shorter than that of ANIb by 53- and 22-fold, respectively. In conclusion, ANI calculation can be greatly sped up by the OrthoANIu method without losing accuracy. A web-service that can be used to calculate OrthoANIu between a pair of genome sequences is available at http://www.ezbiocloud.net/tools/ani . For large-scale calculation and integration in bioinformatics pipelines, a standalone JAVA program is available for download at http://www.ezbiocloud.net/tools/orthoaniu .

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The free modulation of interlayer distance in a layered high-transition temperature (high-Tc) superconductor is of crucial importance not only for the study of the superconducting mechanism but also for the practical application of high-Tc superconducting materials. Two-dimensional (2D) superconductors were achieved by intercalating a long-chain organic compound into bismuth-based high-Tc cuprates. Although the intercalation of the organic chain increased the interlayer distance remarkably, to tens of angstroms, the superconducting transition temperature of the intercalate was nearly the same as that of the pristine material, suggesting the 2D nature of the high-Tc superconductivity.