Bacterial genomes exhibit widespread horizontal gene transfer, resulting in highly variable genome content that complicates the inference of genetic interactions. In this study, we develop a method for detecting coevolving genes from large datasets of bacterial genomes based on pairwise comparisons of closely related individuals, analogous to a pedigree study in eukaryotic populations. We apply our method to pairs of genes from the Staphylococcus aureus accessory genome of over 75,000 annotated gene families using a database of over 40,000 whole genomes. We find many pairs of genes that appear to be gained or lost in a coordinated manner, as well as pairs where the gain of one gene is associated with the loss of the other. These pairs form networks of rapidly coevolving genes, primarily consisting of genes involved in virulence, mechanisms of horizontal gene transfer, and antibiotic resistance, particularly the SCCmec complex. While we focus on gene gain and loss, our method can also detect genes that tend to acquire substitutions in tandem, or genotype-phenotype or phenotype-phenotype coevolution. Finally, we present the R package DeCoTUR that allows for the computation of our method.

BACKGROUND: OmniLog™ phenotype microarrays (PMs) have the capability to measure and compare the growth responses of biological samples upon exposure to hundreds of growth conditions such as different metabolites and antibiotics over a time course of hours to days. In order to manage the large amount of data produced from the OmniLog™ instrument, PheMaDB (Phenotype Microarray DataBase), a web-based relational database, was designed. PheMaDB enables efficient storage, retrieval and rapid analysis of the OmniLog™ PM data. DESCRIPTION: PheMaDB allows the user to quickly identify records of interest for data analysis by filtering with a hierarchical ordering of Project, Strain, Phenotype, Replicate, and Temperature. PheMaDB then provides various statistical analysis options to identify specific growth pattern characteristics of the experimental strains, such as: outlier analysis, negative controls analysis (signal/background calibration), bar plots, pearson's correlation matrix, growth curve profile search, k-means clustering, and a heat map plot. This web-based database management system allows for both easy data sharing among multiple users and robust tools to phenotype organisms of interest. CONCLUSIONS: PheMaDB is an open source system standardized for OmniLog™ PM data. PheMaDB could facilitate the banking and sharing of phenotype data. The source code is available for download at http://phemadb.sourceforge.net.

UNLABELLED: DIYA (Do-It-Yourself Annotator) is a modular and configurable open source pipeline software, written in Perl, used for the rapid annotation of bacterial genome sequences. The software is currently used to take DNA contigs as input, either in the form of complete genomes or the result of shotgun sequencing, and produce an annotated sequence in Genbank file format as output. AVAILABILITY: Distribution and source code are available at (https://sourceforge.net/projects/diyg/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: New, high-throughput sequencing technologies have made it feasible to cheaply generate vast amounts of sequence information from a genome of interest. The computational reconstruction of the complete sequence of a genome is complicated by specific features of these new sequencing technologies, such as the short length of the sequencing reads and absence of mate-pair information. In this article we propose methods to overcome such limitations by incorporating information from optical restriction maps. RESULTS: We demonstrate the robustness of our methods to sequencing and assembly errors using extensive experiments on simulated datasets. We then present the results obtained by applying our algorithms to data generated from two bacterial genomes Yersinia aldovae and Yersinia kristensenii. The resulting assemblies contain a single scaffold covering a large fraction of the respective genomes, suggesting that the careful use of optical maps can provide a cost-effective framework for the assembly of genomes. AVAILABILITY: The tools described here are available as an open-source package at ftp://ftp.cbcb.umd.edu/pub/software/soma