BioBlend.objects: metacomputing with Galaxy

Leo, Simone; Pireddu, Luca; Cuccuru, Gianmauro; Lianas, Luca; Soranzo, Nicola; Afgan, Enis; Zanetti, Gianluigi

doi:10.1093/bioinformatics/btu386

Cited by 11 publications

(11 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, we have included Python code (Fig. 1 b) that leverages the BioBlend software library [ 21 ] to access the Galaxy Application Programming Interface (API), and we have automated pipeline execution using the Galaxy workflow engine running inside the container. Additional scripts implemented inside the Bio-Docklets containers (Fig.…”

Section: Methodsmentioning

confidence: 99%

Bio-Docklets: virtualization containers for single-step execution of NGS pipelines

et al. 2017

View full text Add to dashboard Cite

Processing of next-generation sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized postanalysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers toward seamless deployment of preconfigured bioinformatics software and pipelines on any computational platform. We present an approach for abstracting the complex data operations of multistep, bioinformatics pipelines for NGS data analysis. As examples, we have deployed 2 pipelines for RNA sequencing and chromatin immunoprecipitation sequencing, preconfigured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines as simply as running a single bioinformatics tool. This is achieved using a “meta-script” that automatically starts the Bio-Docklets and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface. The pipeline output is postprocessed by integration with the Visual Omics Explorer framework, providing interactive data visualizations that users can access through a web browser. Our goal is to enable easy access to NGS data analysis pipelines for nonbioinformatics experts on any computing environment, whether a laboratory workstation, university computer cluster, or a cloud service provider. Beyond end users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets.

show abstract

Section: Methodsmentioning

confidence: 99%

Bio-Docklets: virtualization containers for single-step execution of NGS pipelines

et al. 2017

View full text Add to dashboard Cite

show abstract

“…In addition, we have implemented Python code ( Fig. 1b) that leverages the BioBlend software library [14] to access Galaxy API, and automate pipeline execution through the Galaxy pipeline engine running inside the container. Additional scripts inside the containers ( Fig.…”

Section: Methodsmentioning

confidence: 99%

Bio-Docklets: Virtualization Containers for Single-Step Execution of NGS Pipelines

Kim

Ali

Lijeron

et al. 2017

Preprint

View full text Add to dashboard Cite

Background: Processing of Next-Generation Sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized post-analysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers, towards seamless deployment of preconfigured bioinformatics software and pipelines on any computational platform.

show abstract

“…BioBlend makes it possible to automate large data analysis within Galaxy using the Cloud environment of CloudMan [44]. …”

Section: Scientific Workflows: Advantages and Limitationsmentioning

confidence: 99%

Trends in IT Innovation to Build a Next Generation Bioinformatics Solution to Manage and Analyse Biological Big Data Produced by NGS Technologies

Brevern

Meyniel²,

Fairhead

et al. 2015

BioMed Research International

View full text Add to dashboard Cite

Sequencing the human genome began in 1994, and 10 years of work were necessary in order to provide a nearly complete sequence. Nowadays, NGS technologies allow sequencing of a whole human genome in a few days. This deluge of data challenges scientists in many ways, as they are faced with data management issues and analysis and visualization drawbacks due to the limitations of current bioinformatics tools. In this paper, we describe how the NGS Big Data revolution changes the way of managing and analysing data. We present how biologists are confronted with abundance of methods, tools, and data formats. To overcome these problems, focus on Big Data Information Technology innovations from web and business intelligence. We underline the interest of NoSQL databases, which are much more efficient than relational databases. Since Big Data leads to the loss of interactivity with data during analysis due to high processing time, we describe solutions from the Business Intelligence that allow one to regain interactivity whatever the volume of data is. We illustrate this point with a focus on the Amadea platform. Finally, we discuss visualization challenges posed by Big Data and present the latest innovations with JavaScript graphic libraries.

show abstract

BioBlend.objects: metacomputing with Galaxy

Cited by 11 publications

References 5 publications

Bio-Docklets: virtualization containers for single-step execution of NGS pipelines

Bio-Docklets: virtualization containers for single-step execution of NGS pipelines

Bio-Docklets: Virtualization Containers for Single-Step Execution of NGS Pipelines

Trends in IT Innovation to Build a Next Generation Bioinformatics Solution to Manage and Analyse Biological Big Data Produced by NGS Technologies

Contact Info

Product

Resources

About