Dugong: a Docker image, based on Ubuntu Linux, focused on reproducibility and replicability for bioinformatics analyses

Menegídio, Fabiano Bezerra; Jabès, Daniela; Oliveira, Regina Costa de; Nunes, Luiz R.

doi:10.1093/bioinformatics/btx554

Cited by 23 publications

(24 citation statements)

References 6 publications

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“…The applications and pipelines in bioinformatics require a substantial effort to configure, therefore containerbased platforms, such as Docker [70], have emerged to allow the deployment of individual applications that have an isolated environment for the installation and execution of a specific software, without affecting other parts of the system. In this regard, many docker-based platforms have been produced such as BioContainer [71], a community-driven, open-source project based on the Docker container that can be easily accessed via GitHub; Bio-Docklets [72], a bioinformatics pipeline for next generation sequencing (NGS) data analysis; and Dugong [73], a Ubuntu-based docker that automates the installation of bioinformatics tools along with their libraries and dependencies on alternate computational environments. The above-mentioned platforms utilize the Jupyter Notebook as an integration platform for delivery and exchange of consistent and reproducible protocols and results across laboratories, assisting in the development of open-science.…”

Section: Open Sciencementioning

confidence: 99%

Towards reproducible computational drug discovery

et al. 2020

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The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

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Section: Open Sciencementioning

confidence: 99%

Towards reproducible computational drug discovery

et al. 2020

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“…In the review phase, reviewers are able use the same infrastructure to reproduce the authors' results. DUGONG is a preconfigured Docker container for bioinformatics and computational biology research [109]. It packages over 3,000 dependencies and applications, including Jupyter Notebook.…”

Section: Recomputation and Reproducibilitymentioning

confidence: 99%

State of the Art and Future Trends in Data Reduction for High-Performance Computing

2020

JSFI

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Research into data reduction techniques has gained popularity in recent years as storage capacity and performance become a growing concern. This survey paper provides an overview of leveraging points found in high-performance computing (HPC) systems and suitable mechanisms to reduce data volumes. We present the underlying theories and their application throughout the HPC stack and also discuss related hardware acceleration and reduction approaches. After introducing relevant use-cases, an overview of modern lossless and lossy compression algorithms and their respective usage at the application and file system layer is given. In anticipation of their increasing relevance for adaptive and in situ approaches, dimensionality reduction techniques are summarized with a focus on non-linear feature extraction. Adaptive approaches and in situ compression algorithms and frameworks follow. The key stages and new opportunities to deduplication are covered next. An unconventional but promising method is recomputation, which is proposed at last. We conclude the survey with an outlook on future developments.

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“…Recently container technology, a lightweight Operation System (OS)-level virtualization, was explored in the area of Bioinformatics to make easier the distribution, the utilization and the maintenance of bioinformatics software [ 11 – 13 ]. Indeed, since applications and their dependencies are packaged together in the container image, the users have not to download and install all the dependencies required by an application, thus avoiding all the cases where the dependencies are not well documented or not available at all.…”

Section: Introductionmentioning

confidence: 99%

Reproducible bioinformatics project: a community for reproducible bioinformatics analysis pipelines

et al. 2018

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BackgroundReproducibility of a research is a key element in the modern science and it is mandatory for any industrial application. It represents the ability of replicating an experiment independently by the location and the operator. Therefore, a study can be considered reproducible only if all used data are available and the exploited computational analysis workflow is clearly described. However, today for reproducing a complex bioinformatics analysis, the raw data and the list of tools used in the workflow could be not enough to guarantee the reproducibility of the results obtained. Indeed, different releases of the same tools and/or of the system libraries (exploited by such tools) might lead to sneaky reproducibility issues.ResultsTo address this challenge, we established the Reproducible Bioinformatics Project (RBP), which is a non-profit and open-source project, whose aim is to provide a schema and an infrastructure, based on docker images and R package, to provide reproducible results in Bioinformatics. One or more Docker images are then defined for a workflow (typically one for each task), while the workflow implementation is handled via R-functions embedded in a package available at github repository. Thus, a bioinformatician participating to the project has firstly to integrate her/his workflow modules into Docker image(s) exploiting an Ubuntu docker image developed ad hoc by RPB to make easier this task. Secondly, the workflow implementation must be realized in R according to an R-skeleton function made available by RPB to guarantee homogeneity and reusability among different RPB functions. Moreover she/he has to provide the R vignette explaining the package functionality together with an example dataset which can be used to improve the user confidence in the workflow utilization.ConclusionsReproducible Bioinformatics Project provides a general schema and an infrastructure to distribute robust and reproducible workflows. Thus, it guarantees to final users the ability to repeat consistently any analysis independently by the used UNIX-like architecture.

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Dugong: a Docker image, based on Ubuntu Linux, focused on reproducibility and replicability for bioinformatics analyses

Abstract: Luiz.nunes@ufabc.edu.br.

Cited by 23 publications

References 6 publications

Towards reproducible computational drug discovery

Towards reproducible computational drug discovery

State of the Art and Future Trends in Data Reduction for High-Performance Computing

Reproducible bioinformatics project: a community for reproducible bioinformatics analysis pipelines

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