BioContainers: an open-source and community-driven framework for software standardization

Leprevost, Felipe da Veiga; Grüning, Björn; Aflitos, Saulo Alves; Röst, Hannes L.; Uszkoreit, Julian; Barsnes, Harald; Vaudel, Marc; Moreno, Pablo; Guy, Laurent; Weber, Jonas; Bai, Mingze; Jiménez, Rafael C.; Sachsenberg, Timo; Pfeuffer, Julianus; Alvarez, Roberto Vera; Griss, Johannes; Nesvizhskii, Alexey I.; Perez‐Riverol, Yasset

doi:10.1093/bioinformatics/btx192

Cited by 376 publications

(211 citation statements)

References 7 publications

(8 reference statements)

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“…However, it can be used in combination with other tools which ensure a reproducible environment, such as container based tools [7,37] or package managers which emphasize reproducibility such as Nix [17] (Jug is available as a nix package in the main nixpkgs repository).…”

Section: Discussionmentioning

confidence: 99%

Jug: Software for Parallel Reproducible Computation in Python

Coelho¹

2017

JORS

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Section: Discussionmentioning

confidence: 99%

Jug: Software for Parallel Reproducible Computation in Python

Coelho¹

2017

JORS

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“…We evaluate the query time for 1, 2, 5, 10, and 15 concurrent requests. Since the database service is just a lightweight HTTP server we use a AWS EC2 t2.micro 22 instance to host it. From the results in Table 1 we see a significant improvement in response time when the database service caches the results from the database lookups.…”

Section: Discussionmentioning

confidence: 99%

“…There are repositories such as BioContainers [22] and BioBoxes [23] that provide containers preinstalled with software for doing analyses and running different applications. Systems such as Galaxy now allow researchers to build analysis pipelines where each step is executed within a software container.…”

Section: Containerized Analysismentioning

confidence: 99%

Building applications for interactive data exploration in systems biology

Fjukstad

Dumeaux

Olsen

et al. 2017

Preprint

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As the systems biology community generates and collects data at an unprecedented rate, there is a growing need for interactive data exploration tools to explore the datasets. These tools need to combine advanced statistical analyses, relevant knowledge from biological databases, and interactive visualizations in an application with clear user interfaces. To answer specific research questions tools must provide specialized user interfaces and visualizations. While these are application-specific, the underlying components of a data analysis tool can be shared and reused later. Application developers can therefore compose applications of reusable services rather than implementing a single monolithic application from the ground up for each project.Our approach for developing data exploration applications in systems biology builds on the microservice architecture. Microservice architectures separates an application into smaller components that communicate using language-agnostic protocols. We show that this design is suitable in bioinformatics applications where applications often use different tools, written in different languages, by different research groups. Packaging each service in a software container enables re-use and sharing of key components between applications, reducing development, deployment, and maintenance time.We demonstrate the viability of our approach through a web application, MIxT blood-tumor, for exploring and comparing transcriptional profiles from blood and tumor samples in breast cancer patients. The application integrates advanced statistical software, up-to-date information from biological databases, and modern data visualization libraries. The web application for exploring transcriptional profiles, MIxT, is online at mixt-blood-tumor. bci.mcgill.ca and open-sourced at github.com/ fjukstad/mixt. Packages to build the supporting microservices are open-sourced as a part of Kvik at github.com/fjukstad/kvik.

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“…Docker containers are commonly shared via Docker Hub ( https://hub.docker.com ). There are also initiatives for standardizing containers in the life sciences such as BioContainers [11] . Containers have seen an increasing uptake in the life sciences, both for delivering software tools and for facilitating data analysis in various ways [12][13][14][15][16] .…”

Section: Containersmentioning

confidence: 99%

Approaches for containerized scientific workflows in cloud environments with applications in life science

Spjuth

Capuccini

Carone

et al. 2018

Preprint

Self Cite

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Containers are gaining popularity in life science research as they encompass all dependencies of provisioned tools and simplifies software installations for end users, as well as offering a form of isolation between processes. Scientific workflows are ideal to chain containers into data analysis pipelines to sustain reproducible science. In this manuscript we review the different approaches to use containers inside the workflow tools Nextflow, Galaxy, Pachyderm, Luigi, and SciPipe when deployed in cloud environments. A particular focus is placed on the workflow tool's interaction with the Kubernetes container orchestration framework.

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BioContainers: an open-source and community-driven framework for software standardization

Cited by 376 publications

References 7 publications

Jug: Software for Parallel Reproducible Computation in Python

Jug: Software for Parallel Reproducible Computation in Python

Building applications for interactive data exploration in systems biology

Approaches for containerized scientific workflows in cloud environments with applications in life science

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