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Determines the core set of genes in a group of small genomes. CoreGenes is a global JAVA-based interactive data mining tool which allows for the identification, characterization, catalog and visualization of putatively essential core genes in sets of two to five genomes. The software performs hierarchical and iterative BLASTP analyses using one genome as a reference and another as a query. It has been validated with representative genomes from several families of viruses, as well as mitochondrion and chloroplast genomes.


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Allows parallel genome-scale in silico ‘subtractive hybridization’ analyses of reference genomes. mGenomeSubtractor can compare a reference genome and a set of subject genomes from a full listing of complete bacterial genomes, regularly updated by NCBI Microbial Genome Resources. The software allows users to obtain the core/accessory regions, using an mpiBLAST-based procedure. It includes options such as pre-definition of certain genomic regions as core or accessory or the matching to whole or partial subject set genomes.


An efficient system for rapidly locating differentially abundant genomic content in bacterial populations using an exact k-mer matching strategy, while accommodating k-mer mismatches. Neptune’s loci discovery process identifies sequences that are sufficiently common to a group of target sequences and sufficiently absent from non-targets using probabilistic models. Neptune uses parallel computing to efficiently identify and extract these loci from draft genome assemblies without requiring multiple sequence alignments or other computationally expensive comparative sequence analyses.

CaSSiS / Comprehensive and Sensitive Signature Search

Computes comprehensive collections of sequence and sequence group specific oligonucleotide signatures from large sets of hierarchically clustered nucleic acid sequence data. CaSSiS is based on the ARB Positional Tree (PT-)Server and a Bipartite Graph Representation Tree (BGRT) data structure. This tool not only determines sequence-specific signatures and perfect group covering signatures for every node within the cluster (i.e. target groups), but also signatures with maximal group coverage (sensitivity) within a user-defined range of non-target hits (specificity) for groups lacking a perfect common signature.