Dataset Information

ClustAGE: a tool for clustering and distribution analysis of bacterial accessory genomic elements.

ABSTRACT: BACKGROUND:The non-conserved accessory genome of bacteria can be associated with important adaptive characteristics that can contribute to niche specificity or pathogenicity of strains. High degrees of structural and compositional diversity in genomic islands and other elements of the accessory genome can complicate characterization of accessory genome contents among populations of strains. Methods for easily and effectively defining the distributions of discrete elements of the accessory genome among bacterial strains in a population are needed to explore the relationships between the flexible genome and bacterial adaptive traits. RESULTS:We have developed the open-source software package ClustAGE. This program, written in Perl, uses BLAST to cluster nucleotide accessory genomic elements from the genomes of multiple bacterial strains and to identify their distribution within the study population. The program output can be used in combination with strain phenotype data or other characteristics to detect associations. Optional graphical output is available for visualizing accessory genome gene content and distribution patterns. The capabilities of the software are demonstrated on a collection of 14 Pseudomonas aeruginosa genome sequences. CONCLUSIONS:The ClustAGE software and utilities are effective for identifying characteristics and distributions of accessory genomic elements among groups of bacterial genomes. The ability to easily and effectively characterize the accessory genome of a sequence collection may provide a better understanding of the accessory genome's contribution to a species' adaptation and pathogenesis. The ClustAGE source code can be downloaded from https://clustage.sourceforge.io and a limited web-based implementation is available at http://vfsmspineagent.fsm.northwestern.edu/cgi-bin/clustage.cgi .

SUBMITTER: Ozer EA

PROVIDER: S-EPMC5910555 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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ClustAGE: a tool for clustering and distribution analysis of bacterial accessory genomic elements.

Ozer Egon A EA

BMC bioinformatics 20180420 1

<h4>Background</h4>The non-conserved accessory genome of bacteria can be associated with important adaptive characteristics that can contribute to niche specificity or pathogenicity of strains. High degrees of structural and compositional diversity in genomic islands and other elements of the accessory genome can complicate characterization of accessory genome contents among populations of strains. Methods for easily and effectively defining the distributions of discrete elements of the accessor ...[more]

PMID: 29678129

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Project description:BackgroundThe pan-genome of a bacterial species consists of a core and an accessory gene pool. The accessory genome is thought to be an important source of genetic variability in bacterial populations and is gained through lateral gene transfer, allowing subpopulations of bacteria to better adapt to specific niches. Low-cost and high-throughput sequencing platforms have created an exponential increase in genome sequence data and an opportunity to study the pan-genomes of many bacterial species. In this study, we describe a new online pan-genome sequence analysis program, Panseq.ResultsPanseq was used to identify Escherichia coli O157:H7 and E. coli K-12 genomic islands. Within a population of 60 E. coli O157:H7 strains, the existence of 65 accessory genomic regions identified by Panseq analysis was confirmed by PCR. The accessory genome and binary presence/absence data, and core genome and single nucleotide polymorphisms (SNPs) of six L. monocytogenes strains were extracted with Panseq and hierarchically clustered and visualized. The nucleotide core and binary accessory data were also used to construct maximum parsimony (MP) trees, which were compared to the MP tree generated by multi-locus sequence typing (MLST). The topology of the accessory and core trees was identical but differed from the tree produced using seven MLST loci. The Loci Selector module found the most variable and discriminatory combinations of four loci within a 100 loci set among 10 strains in 1 s, compared to the 449 s required to exhaustively search for all possible combinations; it also found the most discriminatory 20 loci from a 96 loci E. coli O157:H7 SNP dataset.ConclusionPanseq determines the core and accessory regions among a collection of genomic sequences based on user-defined parameters. It readily extracts regions unique to a genome or group of genomes, identifies SNPs within shared core genomic regions, constructs files for use in phylogeny programs based on both the presence/absence of accessory regions and SNPs within core regions and produces a graphical overview of the output. Panseq also includes a loci selector that calculates the most variable and discriminatory loci among sets of accessory loci or core gene SNPs.AvailabilityPanseq is freely available online at http://76.70.11.198/panseq. Panseq is written in Perl.

Project description:Fungi comprise a great diversity of species with distinct ecological functions and lifestyles. Similar to other eukaryotes, fungi rely on interactions with prokaryotes and one of the most important symbiotic events was the acquisition of mitochondria. Mitochondria are organelles found in eukaryotic cells whose main function is to generate energy through aerobic respiration. Mitogenomes (mtDNAs) are double-stranded circular or linear DNA from mitochondria that may contain core genes and accessory elements that can be replicated, transcribed, and independently translated from the nuclear genome. Despite their importance, investigative studies on the diversity of fungal mitogenomes are scarce. Herein, we have evaluated 788 curated fungal mitogenomes available at NCBI database to assess discrepancies and similarities among them and to better understand the mechanisms involved in fungal mtDNAs variability. From a total of 12 fungal phyla, four do not have any representative with available mitogenomes, which highlights the underrepresentation of some groups in the current available data. We selected representative and non-redundant mitogenomes based on the threshold of 90% similarity, eliminating 81 mtDNAs. Comparative analyses revealed considerable size variability of mtDNAs with a difference of up to 260 kb in length. Furthermore, variation in mitogenome length and genomic composition are generally related to the number and length of accessory elements (introns, HEGs, and uORFs). We identified an overall average of 8.0 (0-39) introns, 8.0 (0-100) HEGs, and 8.2 (0-102) uORFs per genome, with high variation among phyla. Even though the length of the core protein-coding genes is considerably conserved, approximately 36.3% of the mitogenomes evaluated have at least one of the 14 core coding genes absent. Also, our results revealed that there is not even a single gene shared among all mitogenomes. Other unusual genes in mitogenomes were also detected in many mitogenomes, such as dpo and rpo, and displayed diverse evolutionary histories. Altogether, the results presented in this study suggest that fungal mitogenomes are diverse, contain accessory elements and are absent of a conserved gene that can be used for the taxonomic classification of the Kingdom Fungi.

Dataset Information

ClustAGE: a tool for clustering and distribution analysis of bacterial accessory genomic elements.

Publications

ClustAGE: a tool for clustering and distribution analysis of bacterial accessory genomic elements.

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