Bacterial pathogens subvert host cells by manipulating cellular pathways for survival and replication; in turn, host cells respond to the invading pathogen through cascading changes in gene expression. Deciphering these complex temporal and spatial dynamics to identify novel bacterial virulence factors or host response pathways is crucial for improved diagnostics and therapeutics. Dual RNA sequencing (dRNA-Seq) has recently been developed to simultaneously capture host and bacterial transcriptomes from an infected cell. This approach builds on the high sensitivity and resolution of RNA sequencing technology and is applicable to any bacteria that interact with eukaryotic cells, encompassing parasitic, commensal or mutualistic lifestyles. Several laboratory protocols have been presented that outline the collection, extraction and sequencing of total RNA for dRNA-Seq experiments, but there is relatively little guidance available for the detailed bioinformatic analyses required. This protocol outlines a typical dRNA-Seq experiment, based on a Chlamydia trachomatis-infected host cell, with a detailed description of the necessary bioinformatic analyses with currently available software tools.

SUMMARY: Circleator is a Perl application that generates circular figures of genome-associated data. It leverages BioPerl to support standard annotation and sequence file formats and produces publication-quality SVG output. It is designed to be both flexible and easy to use. It includes a library of circular track types and predefined configuration files for common use-cases, including. (i) visualizing gene annotation and DNA sequence data from a GenBank flat file, (ii) displaying patterns of gene conservation in related microbial strains, (iii) showing Single Nucleotide Polymorphisms (SNPs) and indels relative to a reference genome and gene set and (iv) viewing RNA-Seq plots. AVAILABILITY AND IMPLEMENTATION: Circleator is freely available under the Artistic License 2.0 from http://jonathancrabtree.github.io/Circleator/ and is integrated with the CloVR cloud-based sequence analysis Virtual Machine (VM), which can be downloaded from http://clovr.org or run on Amazon EC2.

MOTIVATION: The growth of sequence data has been accompanied by an increasing need to analyze data on distributed computer clusters. The use of these systems for routine analysis requires scalable and robust software for data management of large datasets. Software is also needed to simplify data management and make large-scale bioinformatics analysis accessible and reproducible to a wide class of target users. RESULTS: We have developed a workflow management system named Ergatis that enables users to build, execute and monitor pipelines for computational analysis of genomics data. Ergatis contains preconfigured components and template pipelines for a number of common bioinformatics tasks such as prokaryotic genome annotation and genome comparisons. Outputs from many of these components can be loaded into a Chado relational database. Ergatis was designed to be accessible to a broad class of users and provides a user friendly, web-based interface. Ergatis supports high-throughput batch processing on distributed compute clusters and has been used for data management in a number of genome annotation and comparative genomics projects. AVAILABILITY: Ergatis is an open-source project and is freely available at http://ergatis.sourceforge.net.

BACKGROUND: The availability of complete genomic sequences for hundreds of organisms promises to make obtaining genome-wide estimates of substitution rates, selective constraints and other molecular evolution variables of interest an increasingly important approach to addressing broad evolutionary questions. Two of the programs most widely used for this purpose are codeml and baseml, parts of the PAML (Phylogenetic Analysis by Maximum Likelihood) suite. A significant drawback of these programs is their lack of a graphical user interface, which can limit their user base and considerably reduce their efficiency. RESULTS: We have developed IDEA (Interactive Display for Evolutionary Analyses), an intuitive graphical input and output interface which interacts with PHYLIP for phylogeny reconstruction and with codeml and baseml for molecular evolution analyses. IDEA's graphical input and visualization interfaces eliminate the need to edit and parse text input and output files, reducing the likelihood of errors and improving processing time. Further, its interactive output display gives the user immediate access to results. Finally, IDEA can process data in parallel on a local machine or computing grid, allowing genome-wide analyses to be completed quickly. CONCLUSION: IDEA provides a graphical user interface that allows the user to follow a codeml or baseml analysis from parameter input through to the exploration of results. Novel options streamline the analysis process, and post-analysis visualization of phylogenies, evolutionary rates and selective constraint along protein sequences simplifies the interpretation of results. The integration of these functions into a single tool eliminates the need for lengthy data handling and parsing, significantly expediting access to global patterns in the data.