ABSTRACT
BACKGROUND: With next-generation sequencing (NGS) technologies, the life sciences face a deluge of raw data. Classical analysis processes for such data often begin with an assembly step, needing large amounts of computing resources, and potentially removing or modifying parts of the biological information contained in the data. Our approach proposes to focus directly on biological questions, by considering raw unassembled NGS data, through a suite of six command-line tools. FINDINGS: Dedicated to 'whole-genome assembly-free' treatments, the Colib'read tools suite uses optimized algorithms for various analyses of NGS datasets, such as variant calling or read set comparisons. Based on the use of a de Bruijn graph and bloom filter, such analyses can be performed in a few hours, using small amounts of memory. Applications using real data demonstrate the good accuracy of these tools compared to classical approaches. To facilitate data analysis and tools dissemination, we developed Galaxy tools and tool shed repositories. CONCLUSIONS: With the Colib'read Galaxy tools suite, we enable a broad range of life scientists to analyze raw NGS data. More importantly, our approach allows the maximum biological information to be retained in the data, and uses a very low memory footprint.
Subject(s)
Computational Biology/methods , High-Throughput Nucleotide Sequencing/methods , Information Storage and Retrieval/methods , Software , Base Sequence , Cluster Analysis , Genome/genetics , Genomics/methods , Molecular Sequence Data , Reproducibility of ResultsABSTRACT
BACKGROUND: Many bioinformatics tools use reference data, such as genome assemblies or sequence databanks. Galaxy offers multiple ways to give access to this data through its web interface. However, the process of adding new reference data was customarily manual and time consuming, even more so when this data needed to be indexed in a variety of formats (e.g. Blast, Bowtie, BWA, or 2bit). BioMAJ is a widely used and stable software that is designed to automate the download and transformation of data from various sources. This data can be used directly from the command line, in more complex systems, such as Mobyle, or by using a REST API. FINDINGS: To ease the process of giving access to reference data in Galaxy, we have developed the BioMAJ2Galaxy module, which enables the gap between BioMAJ and Galaxy to be bridged. With this module, it is now possible to configure BioMAJ to automatically download some reference data, to then convert and/or index it in various formats, and then make this data available in a Galaxy server using data libraries or data managers. CONCLUSIONS: The developments presented in this paper allow us to integrate the reference data in Galaxy in an automatic, reliable, and diskspace-saving way. The code is freely available on the GenOuest GitHub account (https://github.com/genouest/biomaj2galaxy).
Subject(s)
Computational Biology , Internet , User-Computer Interface , AutomationABSTRACT
We report the development of the ReproGenomics Viewer (RGV), a multi- and cross-species working environment for the visualization, mining and comparison of published omics data sets for the reproductive science community. The system currently embeds 15 published data sets related to gametogenesis from nine model organisms. Data sets have been curated and conveniently organized into broad categories including biological topics, technologies, species and publications. RGV's modular design for both organisms and genomic tools enables users to upload and compare their data with that from the data sets embedded in the system in a cross-species manner. The RGV is freely available at http://rgv.genouest.org.
Subject(s)
Gametogenesis/genetics , Software , Animals , Data Mining , Female , Genomics , Humans , Internet , Male , Mice , Rats , Spermatogenesis/geneticsABSTRACT
Linux container technologies, as represented by Docker, provide an alternative to complex and time-consuming installation processes needed for scientiï¬c software. The ease of deployment and the process isolation they enable, as well as the reproducibility they permit across environments and versions, are among the qualities that make them interesting candidates for the construction of bioinformatic infrastructures, at any scale from single workstations to high throughput computing architectures. The Docker Hub is a public registry which can be used to distribute bioinformatic software as Docker images. However, its lack of curation and its genericity make it difï¬cult for a bioinformatics user to ï¬nd the most appropriate images needed. BioShaDock is a bioinformatics-focused Docker registry, which provides a local and fully controlled environment to build and publish bioinformatic software as portable Docker images. It provides a number of improvements over the base Docker registry on authentication and permissions management, that enable its integration in existing bioinformatic infrastructures such as computing platforms. The metadata associated with the registered images are domain-centric, including for instance concepts deï¬ned in the EDAM ontology, a shared and structured vocabulary of commonly used terms in bioinformatics. The registry also includes user deï¬ned tags to facilitate its discovery, as well as a link to the tool description in the ELIXIR registry if it already exists. If it does not, the BioShaDock registry will synchronize with the registry to create a new description in the Elixir registry, based on the BioShaDock entry metadata. This link will help users get more information on the tool such as its EDAM operations, input and output types. This allows integration with the ELIXIR Tools and Data Services Registry, thus providing the appropriate visibility of such images to the bioinformatics community.
