GeneOrder: comparing the order of genes in small genomes

CoreGenes5.0: An Updated User-Friendly Webserver for the Determination of Core Genes from Sets of Viral and Bacterial Genomes

The determination of core genes in viral and bacterial genomes is crucial for a better understanding of their relatedness and for their classification. CoreGenes5.0 is an updated user-friendly web-based software tool for the identification of core genes in and data mining of viral and bacterial genomes. This tool has been useful in the resolution of several issues arising in the taxonomic analysis of bacteriophages and has incorporated many suggestions from researchers in that community. The webserver displays result in a format that is easy to understand and allows for automated batch processing, without the need for any user-installed bioinformatics software. CoreGenes5.0 uses group protein clustering of genomes with one of three algorithm options to output a table of core genes from the input genomes. Previously annotated "unknown genes" may be identified with homologues in the output. The updated version of CoreGenes is able to handle more genomes, is faster, and is more robust, providing easier analysis of custom or proprietary datasets. CoreGenes5.0 is accessible at coregenes.org, migrating from a previous site.

Insights into structural variations and genome rearrangements in prokaryotic genomes

Structural variations (SVs) are genomic rearrangements that affect fairly large fragments of DNA. Most of the SVs such as inversions, deletions and translocations have been largely studied in context of genetic diseases in eukaryotes. However, recent studies demonstrate that genome rearrangements can also have profound impact on prokaryotic genomes, leading to altered cell phenotype. In contrast to single-nucleotide variations, SVs provide a much deeper insight into organization of bacterial genomes at a much better resolution. SVs can confer change in gene copy number, creation of new genes, altered gene expression and many other functional consequences. High-throughput technologies have now made it possible to explore SVs at a much refined resolution in bacterial genomes. Through this review, we aim to highlight the importance of the less explored field of SVs in prokaryotic genomes and their impact. We also discuss its potential applicability in the emerging fields of synthetic biology and genome engineering where targeted SVs could serve to create sophisticated and accurate genome editing.

CGAP: a new comprehensive platform for the comparative analysis of chloroplast genomes

Background

Chloroplast is an essential organelle in plants which contains independent genome. Chloroplast genomes have been widely used for plant phylogenetic inference recently. The number of complete chloroplast genomes increases rapidly with the development of various genome sequencing projects. However, no comprehensive platform or tool has been developed for the comparative and phylogenetic analysis of chloroplast genomes. Thus, we constructed a comprehensive platform for the comparative and phylogenetic analysis of complete chloroplast genomes which was named as chloroplast genome analysis platform (CGAP).

Results

CGAP is an interactive web-based platform which was designed for the comparative analysis of complete chloroplast genomes. CGAP integrated genome collection, visualization, content comparison, phylogeny analysis and annotation functions together. CGAP implemented four web servers including creating complete and regional genome maps of high quality, comparing genome features, constructing phylogenetic trees using complete genome sequences, and annotating draft chloroplast genomes submitted by users.

Conclusions

Both CGAP and source code are available at http://www.herbbol.org:8000/chloroplast. CGAP will facilitate the collection, visualization, comparison and annotation of complete chloroplast genomes. Users can customize the comparative and phylogenetic analysis using their own unpublished chloroplast genomes.

Taxonomic parsing of bacteriophages using core genes and in silico proteome-based CGUG and applications to small bacterial genomes

A combined genomics and in situ proteomics approach can be used to determine and classify the relatedness of organisms. The common set of proteins shared within a group of genomes is encoded by the "core" set of genes, which is increasingly recognized as a metric for parsing viral and bacterial species. These can be described by the concept of a "pan-genome", which consists of this "core" set and a "dispensable" set, i.e., genes found in one or more but not all organisms in the grouping. "CoreGenesUniqueGenes" (CGUG) is a web-based tool that determines this core set of proteins in a set of genomes as well as parses the dispensable set of unique proteins in a pair of viral or small bacterial genomes. This proteome-based methodology is validated using bacteriophages, aiding the reevaluation of current classifications of bacteriophages. The utility of CGUG in the analysis of small bacterial genomes and the annotation of hypothetical proteins is also presented.

Rapid pair-wise synteny analysis of large bacterial genomes using web-based GeneOrder4.0

Background

The growing whole genome sequence databases necessitate the development of user-friendly software tools to mine these data. Web-based tools are particularly useful to wet-bench biologists as they enable platform-independent analysis of sequence data, without having to perform complex programming tasks and software compiling.

Findings

GeneOrder4.0 is a web-based "on-the-fly" synteny and gene order analysis tool for comparative bacterial genomics (ca. 8 Mb). It enables the visualization of synteny by plotting protein similarity scores between two genomes and it also provides visual annotation of "hypothetical" proteins from older archived genomes based on more recent annotations.

Conclusions

The web-based software tool GeneOrder4.0 is a user-friendly application that has been updated to allow the rapid analysis of synteny and gene order in large bacterial genomes. It is developed with the wet-bench researcher in mind.

CGUG: in silico proteome and genome parsing tool for the determination of "core" and unique genes in the analysis of genomes up to ca. 1.9 Mb

BACKGROUND

Viruses and small-genome bacteria (~2 megabases and smaller) comprise a considerable population in the biosphere and are of interest to many researchers. These genomes are now sequenced at an unprecedented rate and require complementary computational tools to analyze. "CoreGenesUniqueGenes" (CGUG) is an in silico genome data mining tool that determines a "core" set of genes from two to five organisms with genomes in this size range. Core and unique genes may reflect similar niches and needs, and may be used in classifying organisms.

FINDINGS

CGUG is available at http://binf.gmu.edu/geneorder.html as a web-based on-the-fly tool that performs iterative BLASTP analyses using a reference genome and up to four query genomes to provide a table of genes common to these genomes. The result is an in silico display of genomes and their proteomes, allowing for further analysis. CGUG can be used for "genome annotation by homology", as demonstrated with Chlamydophila and Francisella genomes.

CONCLUSION

CGUG is used to reanalyze the ICTV-based classifications of bacteriophages, to reconfirm long-standing relationships and to explore new classifications. These genomes have been problematic in the past, due largely to horizontal gene transfers. CGUG is validated as a tool for reannotating small genome bacteria using more up-to-date annotations by similarity or homology. These serve as an entry point for wet-bench experiments to confirm the functions of these "hypothetical" and "unknown" proteins.

In silico identification of genes in bacteriophage DNA

One of the most satisfying aspects of a genome sequencing project is the identification of the genes contained within it.These are of two types: those which encode tRNAs and those which produce proteins. After a general introduction on the properties of protein-encoding genes and the utility of the Basic Local Alignment Search Tool (BLASTX) to identify genes through homologs, a variety of tools are discussed by their creators. These include for genome annotation: GeneMark, Artemis, and BASys; and, for genome comparisons: Artemis Comparison Tool (ACT), Mauve, CoreGenes, and GeneOrder.

Unifying classical and molecular taxonomic classification: analysis of the Podoviridae using BLASTP-based tools

We defined phage genera by measuring genome relationships by the numbers of shared homologous/orthologous proteins. We used BLAST-based tools (CoreExtractor.vbs and CoreGenes) to analyze 55 fully sequenced bacteriophage genomes from the NCBI and EBI databases. This approach was first applied to the T7-related phages. Using a cut-off score of 40% homologous proteins, we identified three genera within the T7-related phages, redefined the phi29-related phages, and introduced five novel genera. The T7- and phi29-related phages were given subfamily status and named "Autographivirinae" and "Picovirinae", respectively. Our results confirm and refine the ICTV phage classification, enable elimination of errors in public databases, and provide a straightforward tool for the molecular classification of new phage genomes.

PSAT: a web tool to compare genomic neighborhoods of multiple prokaryotic genomes

Background

The conservation of gene order among prokaryotic genomes can provide valuable insight into gene function, protein interactions, or events by which genomes have evolved. Although some tools are available for visualizing and comparing the order of genes between genomes of study, few support an efficient and organized analysis between large numbers of genomes. The Prokaryotic Sequence homology Analysis Tool (PSAT) is a web tool for comparing gene neighborhoods among multiple prokaryotic genomes.

Results

PSAT utilizes a database that is preloaded with gene annotation, BLAST hit results, and gene-clustering scores designed to help identify regions of conserved gene order. Researchers use the PSAT web interface to find a gene of interest in a reference genome and efficiently retrieve the sequence homologs found in other bacterial genomes. The tool generates a graphic of the genomic neighborhood surrounding the selected gene and the corresponding regions for its homologs in each comparison genome. Homologs in each region are color coded to assist users with analyzing gene order among various genomes. In contrast to common comparative analysis methods that filter sequence homolog data based on alignment score cutoffs, PSAT leverages gene context information for homologs, including those with weak alignment scores, enabling a more sensitive analysis. Features for constraining or ordering results are designed to help researchers browse results from large numbers of comparison genomes in an organized manner. PSAT has been demonstrated to be useful for helping to identify gene orthologs and potential functional gene clusters, and detecting genome modifications that may result in loss of function.

Conclusion

PSAT allows researchers to investigate the order of genes within local genomic neighborhoods of multiple genomes. A PSAT web server for public use is available for performing analyses on a growing set of reference genomes through any web browser with no client side software setup or installation required. Source code is freely available to researchers interested in setting up a local version of PSAT for analysis of genomes not available through the public server. Access to the public web server and instructions for obtaining source code can be found at .

Computational identification of strain-, species- and genus-specific proteins

Background

The identification of unique proteins at different taxonomic levels has both scientific and practical value. Strain-, species- and genus-specific proteins can provide insight into the criteria that define an organism and its relationship with close relatives. Such proteins can also serve as taxon-specific diagnostic targets.

Description

A pipeline using a combination of computational and manual analyses of BLAST results was developed to identify strain-, species-, and genus-specific proteins and to catalog the closest sequenced relative for each protein in a proteome. Proteins encoded by a given strain are preliminarily considered to be unique if BLAST, using a comprehensive protein database, fails to retrieve (with an e-value better than 0.001) any protein not encoded by the query strain, species or genus (for strain-, species- and genus-specific proteins respectively), or if BLAST, using the best hit as the query (reverse BLAST), does not retrieve the initial query protein. Results are manually inspected for homology if the initial query is retrieved in the reverse BLAST but is not the best hit. Sequences unlikely to retrieve homologs using the default BLOSUM62 matrix (usually short sequences) are re-tested using the PAM30 matrix, thereby increasing the number of retrieved homologs and increasing the stringency of the search for unique proteins. The above protocol was used to examine several food- and water-borne pathogens. We find that the reverse BLAST step filters out about 22% of proteins with homologs that would otherwise be considered unique at the genus and species levels. Analysis of the annotations of unique proteins reveals that many are remnants of prophage proteins, or may be involved in virulence. The data generated from this study can be accessed and further evaluated from the CUPID (Core and Unique Protein Identification) system web site (updated semi-annually) at .

Conclusion

CUPID provides a set of proteins specific to a genus, species or a strain, and identifies the most closely related organism.

GeneOrder3.0: software for comparing the order of genes in pairs of small bacterial genomes

BACKGROUND

An increasing number of whole viral and bacterial genomes are being sequenced and deposited in public databases. In parallel to the mounting interest in whole genomes, the number of whole genome analyses software tools is also increasing. GeneOrder was originally developed to provide an analysis of genes between two genomes, allowing visualization of gene order and synteny comparisons of any small genomes. It was originally developed for comparing virus, mitochondrion and chloroplast genomes. This is now extended to small bacterial genomes of sizes less than 2 Mb.

RESULTS

GeneOrder3.0 has been developed and validated successfully on several small bacterial genomes (ca. 580 kb to 1.83 Mb) archived in the NCBI GenBank database. It is an updated web-based "on-the-fly" computational tool allowing gene order and synteny comparisons of any two small bacterial genomes. Analyses of several bacterial genomes show that a large amount of gene and genome re-arrangement occurs, as seen with earlier DNA software tools. This can be displayed at the protein level using GeneOrder3.0. Whole genome alignments of genes are presented in both a table and a dot plot. This allows the detection of evolutionary more distant relationships since protein sequences are more conserved than DNA sequences.

CONCLUSIONS

GeneOrder3.0 allows researchers to perform comparative analysis of gene order and synteny in genomes of sizes up to 2 Mb "on-the-fly."

AVAILABILITY

http://binf.gmu.edu/genometools.html and http://pasteur.atcc.org:8050/GeneOrder3.0.

CoreGenes: a computational tool for identifying and cataloging "core" genes in a set of small genomes

BACKGROUND

Improvements in DNA sequencing technology and methodology have led to the rapid expansion of databases comprising DNA sequence, gene and genome data. Lower operational costs and heightened interest resulting from initial intriguing novel discoveries from genomics are also contributing to the accumulation of these data sets. A major challenge is to analyze and to mine data from these databases, especially whole genomes. There is a need for computational tools that look globally at genomes for data mining.

RESULTS

CoreGenes is a global JAVA-based interactive data mining tool that identifies and catalogs a "core" set of genes from two to five small whole genomes simultaneously. CoreGenes performs hierarchical and iterative BLASTP analyses using one genome as a reference and another as a query. Subsequent query genomes are compared against each newly generated "consensus." These iterations lead to a matrix comprising related genes from this set of genomes, e. g., viruses, mitochondria and chloroplasts. Currently the software is limited to small genomes on the order of 330 kilobases or less.

CONCLUSION

A computational tool CoreGenes has been developed to analyze small whole genomes globally. BLAST score-related and putatively essential "core" gene data are displayed as a table with links to GenBank for further data on the genes of interest. This web resource is available at http://pumpkins.ib3.gmu.edu:8080/CoreGenes or http://www.bif.atcc.org/CoreGenes.

Comparisons of gene colinearity in genomes using GeneOrder2.0

Comparative genomics is enhanced by data mining the rapidly expanding DNA sequence databases. Because of the immense amount of data, computational tools and methods are needed to augment traditional manual visualizations and manipulations of these data. GeneOrder2.0, a Java-based interactive software programme, organizes genome sequence data into tabular and graphical visualizations of the extent of colinearity of genes between any two chromosome genomes of < or =250 kilobases. Both GenBank and proprietary data can be analyzed with this tool.