Dataset Information

Genome-wide gene order distances support clustering the gram-positive bacteria.

ABSTRACT: Initially using 143 genomes, we developed a method for calculating the pair-wise distance between prokaryotic genomes using a Monte Carlo method to estimate the conservation of gene order. The method was based on repeatedly selecting five or six non-adjacent random orthologs from each of two genomes and determining if the chosen orthologs were in the same order. The raw distances were then corrected for gene order convergence using an adaptation of the Jukes-Cantor model, as well as using the common distance correction D' = -ln(1-D). First, we compared the distances found via the order of six orthologs to distances found based on ortholog gene content and small subunit rRNA sequences. The Jukes-Cantor gene order distances are reasonably well correlated with the divergence of rRNA (R (2) = 0.24), especially at rRNA Jukes-Cantor distances of less than 0.2 (R (2) = 0.52). Gene content is only weakly correlated with rRNA divergence (R (2) = 0.04) over all distances, however, it is especially strongly correlated at rRNA Jukes-Cantor distances of less than 0.1 (R (2) = 0.67). This initial work suggests that gene order may be useful in conjunction with other methods to help understand the relatedness of genomes. Using the gene order distances in 143 genomes, the relations of prokaryotes were studied using neighbor joining and agreement subtrees. We then repeated our study of the relations of prokaryotes using gene order in 172 complete genomes better representing a wider-diversity of prokaryotes. Consistently, our trees show the Actinobacteria as a sister group to the bulk of the Firmicutes. In fact, the robustness of gene order support was found to be considerably greater for uniting these two phyla than for uniting any of the proteobacterial classes together. The results are supportive of the idea that Actinobacteria and Firmicutes are closely related, which in turn implies a single origin for the gram-positive cell.

SUBMITTER: House CH

PROVIDER: S-EPMC4299520 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genome-wide gene order distances support clustering the gram-positive bacteria.

House Christopher H CH Pellegrini Matteo M Fitz-Gibbon Sorel T ST

Frontiers in microbiology 20150120

Initially using 143 genomes, we developed a method for calculating the pair-wise distance between prokaryotic genomes using a Monte Carlo method to estimate the conservation of gene order. The method was based on repeatedly selecting five or six non-adjacent random orthologs from each of two genomes and determining if the chosen orthologs were in the same order. The raw distances were then corrected for gene order convergence using an adaptation of the Jukes-Cantor model, as well as using the co ...[more]

PMID: 25653643

Similar Datasets

Project description:BACKGROUND: Genome-wide prediction of protein subcellular localization is an important type of evidence used for inferring protein function. While a variety of computational tools have been developed for this purpose, errors in the gene models and use of protein sorting signals that are not recognized by the more commonly accepted tools can diminish the accuracy of their output. RESULTS: As part of an effort to manually curate the annotations of 19 strains of Shewanella, numerous insights were gained regarding the use of computational tools and proteomics data to predict protein localization. Identification of the suite of secretion systems present in each strain at the start of the process made it possible to tailor-fit the subsequent localization prediction strategies to each strain for improved accuracy. Comparisons of the computational predictions among orthologous proteins revealed inconsistencies in the computational outputs, which could often be resolved by adjusting the gene models or ortholog group memberships. While proteomic data was useful for verifying start site predictions and post-translational proteolytic cleavage, care was needed to distinguish cellular versus sample processing-mediated cleavage events. Searches for lipoprotein signal peptides revealed that neither TatP nor LipoP are designed for identification of lipoprotein substrates of the twin arginine translocation system and that the +2 rule for lipoprotein sorting does not apply to this Genus. Analysis of the relationships between domain occurrence and protein localization prediction enabled identification of numerous location-informative domains which could then be used to refine or increase confidence in location predictions. This collective knowledge was used to develop a general strategy for predicting protein localization that could be adapted to other organisms. CONCLUSION: Improved localization prediction accuracy is not simply a matter of developing better computational algorithms. It also entails gathering key knowledge regarding the host architecture and translocation machinery and associated substrate recognition via experimentation and integration of diverse computational analyses from many proteins and, where possible, that are derived from different species within the same genus.

Dataset Information

Genome-wide gene order distances support clustering the gram-positive bacteria.

Publications

Genome-wide gene order distances support clustering the gram-positive bacteria.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets