CC BY-NC-ND 4.0 · Methods Inf Med
DOI: 10.1055/s-0040-1712460
Original Article

APRICOT: Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools

Vicent Giménez-Alventosa
1  Instituto de Instrumentación para Imagen Molecular (I3M), Centro mixto CSIC—Universitat Politècnica de València, Valencia, Spain
,
José Damián Segrelles
1  Instituto de Instrumentación para Imagen Molecular (I3M), Centro mixto CSIC—Universitat Politècnica de València, Valencia, Spain
,
Germán Moltó
1  Instituto de Instrumentación para Imagen Molecular (I3M), Centro mixto CSIC—Universitat Politècnica de València, Valencia, Spain
,
Mar Roca-Sogorb
2  Quantitative Imaging Biomarkers in Medicine (QUIBIM), Valencia, Spain
› Author Affiliations
Funding This study was supported by the program “Ayudas para la contratación de personal investigador en formación de carácter predoctoral, programa VALi + d” under grant number ACIF/2018/148 from the Conselleria d'Educació of the Generalitat Valenciana and the “Fondo Social Europeo” (FSE). The authors would like to thank the Spanish “Ministerio de Economía, Industria y Competitividad” for the project “BigCLOE” with reference number TIN2016–79951-R and the European Commission, Horizon 2020 grant agreement No 826494 (PRIMAGE). The MRI prostate study case used in this article has been retrospectively collected from a project of prostate MRI biomarkers validation.
  

Abstract

Background Scientific publications are meant to exchange knowledge among researchers but the inability to properly reproduce computational experiments limits the quality of scientific research. Furthermore, bibliography shows that irreproducible preclinical research exceeds 50%, which produces a huge waste of resources on nonprofitable research at Life Sciences field. As a consequence, scientific reproducibility is being fostered to promote Open Science through open databases and software tools that are typically deployed on existing computational resources. However, some computational experiments require complex virtual infrastructures, such as elastic clusters of PCs, that can be dynamically provided from multiple clouds. Obtaining these infrastructures requires not only an infrastructure provider, but also advanced knowledge in the cloud computing field.

Objectives The main aim of this paper is to improve reproducibility in life sciences to produce better and more cost-effective research. For that purpose, our intention is to simplify the infrastructure usage and deployment for researchers.

Methods This paper introduces Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools (APRICOT), an open source extension for Jupyter to deploy deterministic virtual infrastructures across multiclouds for reproducible scientific computational experiments. To exemplify its utilization and how APRICOT can improve the reproduction of experiments with complex computation requirements, two examples in the field of life sciences are provided. All requirements to reproduce both experiments are disclosed within APRICOT and, therefore, can be reproduced by the users.

Results To show the capabilities of APRICOT, we have processed a real magnetic resonance image to accurately characterize a prostate cancer using a Message Passing Interface cluster deployed automatically with APRICOT. In addition, the second example shows how APRICOT scales the deployed infrastructure, according to the workload, using a batch cluster. This example consists of a multiparametric study of a positron emission tomography image reconstruction.

Conclusion APRICOT's benefits are the integration of specific infrastructure deployment, the management and usage for Open Science, making experiments that involve specific computational infrastructures reproducible. All the experiment steps and details can be documented at the same Jupyter notebook which includes infrastructure specifications, data storage, experimentation execution, results gathering, and infrastructure termination. Thus, distributing the experimentation notebook and needed data should be enough to reproduce the experiment.



Publication History

Received: 13 February 2020

Accepted: 09 April 2020

Publication Date:
10 August 2020 (online)

© 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

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