AlgoRun: a Docker-based packaging system for platform-agnostic implemented algorithms

Hosny, Abdelrahman; Vera-Licona, Paola; Laubenbacher, Reinhard; Favre, Thibauld

doi:10.1093/bioinformatics/btw120

Cited by 35 publications

(23 citation statements)

References 10 publications

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“…In addition, we provide a ready-to-use Docker container for each using the AlgoRun framework (cf. [57]) and a Web interface to instances of the software running on the AlgoRun project's servers at algorun.org. This framework is used for experimental benchmarks which are presented in Section 5.…”

Section: Existing Algorithmsmentioning

confidence: 99%

The Minimal Hitting Set Generation Problem: Algorithms and Computation

Gainer-Dewar¹,

Vera-Licona²

2017

SIAM J. Discrete Math.

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Finding inclusion-minimal hitting sets for a given collection of sets is a fundamental combinatorial problem with applications in domains as diverse as Boolean algebra, computational biology, and data mining. Much of the algorithmic literature focuses on the problem of recognizing the collection of minimal hitting sets; however, in many of the applications, it is more important to generate these hitting sets. We survey twenty algorithms from across a variety of domains, considering their history, classification, useful features, and computational performance on a variety of synthetic and real-world inputs. We also provide a suite of implementations of these algorithms with a ready-to-use, platform-agnostic interface based on Docker containers and the AlgoRun framework, so that interested computational scientists can easily perform similar tests with inputs from their own research areas on their own computers or through a convenient Web interface.

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Section: Existing Algorithmsmentioning

confidence: 99%

The Minimal Hitting Set Generation Problem: Algorithms and Computation

Gainer-Dewar¹,

Vera-Licona²

2017

SIAM J. Discrete Math.

Self Cite

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“…Di Tommaso et al (2015) showed that containers have negligible impact on the performance of bioinformatics applications. Other examples of software distributed as Docker software containers include: AlgoRun (Hosny et al, 2016), which provides a modular software container with frequently used bioinformatics tools and algorithms that can be accessed through a browser or a Web application programming interface; ReproPhylo (Szitenberg et al, 2015), which implements a phylogenomics workflow with reproducibility features; and GEN3VA (Gundersen et al, 2016), which is a platform for gene-expression analysis available as a web-based system.…”

Section: /18mentioning

confidence: 99%

Peer Review #2 of "GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis (v0.1)"

2017

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There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managing the resulting data and its respective metadata are challenging tasks. Additionally, a great amount of effort is equally required to run in-silico experiments to structure and compose the information as needed for analysis. Different programs may need to be applied and different files are produced during the experiment cycle. In this context, the availability of a platform supporting experiment execution is paramount. We present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. It includes GeNNet-Wf, a scientific workflow that pre-loads biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and gene set enrichment analysis. A user-friendly web interface, GeNNet-Web, allows for setting parameters, executing, and visualizing the results of GeNNet-Wf executions. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment in different analysis scenarios. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships. The

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“…due to package dependency resolution. This problem has been addressed by the AlgoRun package (Hosny et al (2016)), providing a Docker-based repository of tools. Being a web-based service, it is limited to web-applicable data sizes, or local data must be made available to the Docker container in the cloud.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "From command-line bioinformatics to bioGUI (v0.1)"

2019

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Bioinformatics is a highly interdisciplinary field providing (bioinformatics) applications for scientists from many disciplines. Installing and starting applications on the command-line (CL) is inconvenient and/or inefficient for many scientists. Nonetheless, most methods are implemented with a command-line interface only. Providing a graphical user interface (GUI) for bioinformatics applications is one step towards routinely making CL-only applications available to more scientists and, thus, towards a more effective interdisciplinary work. With our bioGUI framework we address two main problems of using CL bioinformatics applications: First, many tools work on UNIX-systems only, while many scientists use Microsoft Windows. Second, scientists refrain from using command-line tools which, however, could well support them in their research.With bioGUI install modules and templates, installing and using CL tools is made possible for most scientistseven on Windows, due to bioGUI's support for Windows Subsystem for Linux (WSL). In addition, bioGUI templates can easily be created, making the bioGUI framework highly rewarding for developers. From the bioGUI repository it is possible to download, install and use bioinformatics tools with just a few clicks.

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AlgoRun: a Docker-based packaging system for platform-agnostic implemented algorithms

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 35 publications

References 10 publications

The Minimal Hitting Set Generation Problem: Algorithms and Computation

The Minimal Hitting Set Generation Problem: Algorithms and Computation

Peer Review #2 of "GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis (v0.1)"

Peer Review #1 of "From command-line bioinformatics to bioGUI (v0.1)"

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