Biogem: an effective tool-based approach for scaling up open source software development in bioinformatics

Summary: Biogem provides a software development environment for the Ruby programming language, which encourages community-based software development for bioinformatics while lowering the barrier to entry and encouraging best practices

Bioinformatics tools and database resources for systems genetics analysis in mice—a short review and an evaluation of future needs

During a meeting of the SYSGENET working group ‘Bioinformatics’, currently available software tools and databases for systems genetics in mice were reviewed and the needs for future developments discussed. The group evaluated interoperability and performed initial feasibility studies. To aid future compatibility of software and exchange of already developed software modules, a strong recommendation was made by the group to integrate HAPPY and R/qtl analysis toolboxes, GeneNetwork and XGAP database platforms, and TIQS and xQTL processing platforms. R should be used as the principal computer language for QTL data analysis in all platforms and a ‘cloud’ should be used for software dissemination to the community. Furthermore, the working group recommended that all data models and software source code should be made visible in public repositories to allow a coordinated effort on the use of common data structures and file formats

Identification of Quantitative Trait Loci in Experimental Epidermolysis Bullosa Acquisita

Epidermolysis bullosa acquisita (EBA) is a chronic mucocutaneous autoimmune skin blistering disease. Several lines of evidence underscore the contribution of autoantibodies against type VII collagen (COL7) to the pathogenesis of EBA. Furthermore, EBA susceptibility is associated with the MHC haplotype in patients (HLA-DR2) and in immunization-induced EBA in mice (H2s). The latter study indicated an additional contribution of non-MHC genes to disease susceptibility. To identify non-MHC genes controlling EBA susceptibility, we intercrossed EBA-susceptible MRL/MpJ with EBA-resistant NZM2410/J and BXD2/TyJ as well as Cast mice. Mice of the fourth generation of this four-way autoimmune-prone advanced intercross line were immunized with a fragment of murine COL7 to induce EBA. Anti-COL7 autoantibodies were detected in 84% of mice, whereas deposition of complement at the dermal–epidermal junction (DEJ) was observed in 50% of the animals; 33% of immunized mice presented with overt clinical EBA. Onset of clinical disease was associated with several quantitative trait loci (QTLs) located on chromosomes 9, 12, 14, and 19, whereas maximum disease severity was linked to QTLs on chromosomes 1, 15, and 19. This more detailed insight into the pathogenesis of EBA may eventually lead to new treatment strategies for EBA and other autoantibody-mediated diseases

Using bio.tools to generate and annotate workbench tool descriptions

Workbench and workflow systems such as Galaxy, Taverna, Chipster, or Common Workflow Language (CWL)-based frameworks, facilitate the access to bioinformatics tools in a user-friendly, scalable and reproducible way. Still, the integration of tools in such environments remains a cumbersome, time consuming and error-prone process. A major consequence is the incomplete or outdated description of tools that are often missing important information, including parameters and metadata such as publication or links to documentation. ToolDog (Tool DescriptiOn Generator) facilitates the integration of tools - which have been registered in the ELIXIR tools registry (https://bio.tools) - into workbench environments by generating tool description templates. ToolDog includes two modules. The first module analyses the source code of the bioinformatics software with language-specific plugins, and generates a skeleton for a Galaxy XML or CWL tool description. The second module is dedicated to the enrichment of the generated tool description, using metadata provided by bio.tools. This last module can also be used on its own to complete or correct existing tool descriptions with missing metadata

Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community

Background: A steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure. Results: Cloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tool's functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds. Conclusions: Cloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them

Towards a better understanding of the bacterial pan-genome

The bacterial pan-genome is a relatively new concept that refers to the number of genes observed in a given set of bacterial genome sequences, either at the intra- or inter-species level. Determining the pan-genome of a given species of bacteria using a large number of strains allows one to compare multiple genes and to determine evolutionary links between isolates. This information can help to determine population structure, diversity in terms of prevalence in a given environment and pathogenicity of microorganisms. Within this review, we explain the most important issues related to pan-genome studies. We also include a brief description of some selected bacterial pan-genomes. Finally, we propose an easy-toperform workflow to study bacterial pan-genomes that will facilitate nonexperts in a pan-genome-based investigation