Enhanced reproducibility of SADI web service workflows with Galaxy and Docker

Aranguren, Mikel Egaña; Wilkinson, Mark D.

doi:10.1186/s13742-015-0092-3

Cited by 14 publications

(10 citation statements)

References 15 publications

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“…The advantage of exposing Bio2RDF as a set of SADI services is that the data in Bio2RDF becomes discoverable -software does not need to know, a priori, what data/relations exist in which Bio2RDF endpoint. Moreover, when exposed as SADI Services, Bio2RDF data can more easily be integrated into workflows using popular workflow editors such as Taverna 105 or as demonstrated by our use of these services within Galaxy workflows 106 .…”

Section: Bio2rdf2sadimentioning

confidence: 99%

BioHackathon series in 2013 and 2014: improvements of semantic interoperability in life science data and services

et al. 2019

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Publishing databases in the Resource Description Framework (RDF) model is becoming widely accepted to maximize the syntactic and semantic interoperability of open data in life sciences. Here we report advancements made in the 6th and 7th annual BioHackathons which were held in Tokyo and Miyagi respectively. This review consists of two major sections covering: 1) improvement and utilization of RDF data in various domains of the life sciences and 2) meta-data about these RDF data, the resources that store them, and the service quality of SPARQL Protocol and RDF Query Language (SPARQL) endpoints. The first section describes how we developed RDF data, ontologies and tools in genomics, proteomics, metabolomics, glycomics and by literature text mining. The second section describes how we defined descriptions of datasets, the provenance of data, and quality assessment of services and service discovery. By enhancing the harmonization of these two layers of machine-readable data and knowledge, we improve the way community wide resources are developed and published. Moreover, we outline best practices for the future, and prepare ourselves for an exciting and unanticipatable variety of real world applications in coming years.

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Section: Bio2rdf2sadimentioning

confidence: 99%

BioHackathon series in 2013 and 2014: improvements of semantic interoperability in life science data and services

et al. 2019

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“…Docker provides a standard way to supply ready-to-use applications, and it's beginning to be a common way to share works [ 15 – 22 ]. In Aranguren and Wilkinson [ 23 ], the authors make the assumption that the reproducibility could be implemented at 2 levels: (1) at the Docker container level: the encapsulation of a tool with all its dependencies would ensure the sustainability, traceability, and reproducibility of the tool; and (2) at the workflow level: the reproducibility is ensured by Galaxy workflow definition. They developed a containerized Galaxy Docker platform in the context of the OpenLifeData2SADI research project.…”

Section: Introductionmentioning

confidence: 99%

An architecture for genomics analysis in a clinical setting using Galaxy and Docker

et al. 2017

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Next-generation sequencing is used on a daily basis to perform molecular analysis to determine subtypes of disease (e.g., in cancer) and to assist in the selection of the optimal treatment. Clinical bioinformatics handles the manipulation of the data generated by the sequencer, from the generation to the analysis and interpretation. Reproducibility and traceability are crucial issues in a clinical setting. We have designed an approach based on Docker container technology and Galaxy, the popular bioinformatics analysis support open-source software. Our solution simplifies the deployment of a small-size analytical platform and simplifies the process for the clinician. From the technical point of view, the tools embedded in the platform are isolated and versioned through Docker images. Along the Galaxy platform, we also introduce the AnalysisManager, a solution that allows single-click analysis for biologists and leverages standardized bioinformatics application programming interfaces. We added a Shiny/R interactive environment to ease the visualization of the outputs. The platform relies on containers and ensures the data traceability by recording analytical actions and by associating inputs and outputs of the tools to EDAM ontology through ReGaTe. The source code is freely available on Github at https://github.com/CARPEM/GalaxyDocker.

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“…Thus, Dockers technology is very suitable for the application in the field of bioinformatics, because of its advantages and characteristics that allow applications to run in an isolated, self-contained package that can be efficiently distributed and executed in a portable manner across a wide range of computing platforms. ( Belmann et al, 2015;Hosny et al, 2016;Aranguren and Wilkinson, 2015), At present, there are many bioinformatics tools based on docker are developed and published, such as perl and bioperl (Martini, 2016), python and biopython (Moghedrin et al, 2016), R and Bioconductor (Eddelbuettel et al, 2016), contribute their Official Docker image; famous Galaxy also contribute docker galaxy (Björn, 2016). It's reasonable to predict that docker will become more and more extensive in the field of bioinformatics.…”

Section: Introductionmentioning

confidence: 99%

BGDMdocker: a Docker workflow for analysis and visualization pan-genome and biosynthetic gene clusters of bacterial

Cheng

Zhou

et al. 2017

Preprint

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Motivation: At present Docker technology has received increasing level of attention throughout the bioinformatics community. However, its implementation details have not yet been mastered by most biologists and applied widely in biological researches. In order to popularizing this technology in the bioinformatics and sufficiently use plenty of public resources of bioinformatics tools (Dockerfile and image of scommunity, officially and privately) in Docker Hub Registry and other Docker sources based on Docker, we introduced full and accurate instance of a bioinformatics workflow based on Docker to analyse and visualize pan-genome and biosynthetic gene clusters of a bacteria in this article, provided the solutions for mining bioinformatics big data from various public biology databases. You could be guided step-by-step through the workflow process from docker file to build up your own images and run an container fast creating an workflow. Results: We presented a BGDMdocker (bacterial genome data mining docker-based) workflow based on docker. The workflow consists of three integrated toolkits, Prokka v1.11, panX, and antiSMASH3.0. The dependencies were all written in Dockerfile, to build docker image and run container for analysing pan-genome of total 44 Bacillus amyloliquefaciens strains, which were retrieved from public ??? database. The pangenome totally includes 172,432 gene, 2,306 Core gene cluster. The visualized pangenomic data such as alignment, phylogenetic trees, maps mutations within that cluster to the branches of the tree, infers loss and gain of genes on the core-genome phylogeny for each gene cluster were presented. Besides, 997 known (MIBiG database) and 553 unknown (antiSMASH-predicted clusters and Pfam database) genes of biosynthesis gene clusters types and orthologous groups were mined in all strains. This workflow could also be used for other species pan-genome analysis and visualization. The display of visual data can completely duplicated as well as done in this paper. All result data and relevant tools and files can be downloaded from our website with no need to register. The pan-genome and biosynthetic gene clusters analysis and visualization can be fully . CC-BY 4.0 International license not peer-reviewed) is the author/funder. It is made available under a

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Enhanced reproducibility of SADI web service workflows with Galaxy and Docker

Cited by 14 publications

References 15 publications

BioHackathon series in 2013 and 2014: improvements of semantic interoperability in life science data and services

BioHackathon series in 2013 and 2014: improvements of semantic interoperability in life science data and services

An architecture for genomics analysis in a clinical setting using Galaxy and Docker

BGDMdocker: a Docker workflow for analysis and visualization pan-genome and biosynthetic gene clusters of bacterial

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