Unifying package managers, workflow engines, and containers: Computational reproducibility with BioNix

Author:

Bedő Justin12ORCID,Di Stefano Leon13,Papenfuss Anthony T14567ORCID

Affiliation:

1. Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, 1G Royal Pde., Parkville, VIC 3052, Australia

2. School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia

3. Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, Maryland, U.S.A

4. Peter MacCallum Cancer Centre, 305 Grattan St., Melbourne, VIC 3000, Australia

5. Department of Medical Biology, University of Melbourne, Melbourne, VIC 3010, Australia

6. Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC 3010, Australia

7. School of Mathematics and Statistics, University of Melbourne, Melbourne, VIC 3010, Australia

Abstract

Abstract Motivation A challenge for computational biologists is to make our analyses reproducible—i.e. to rerun, combine, and share, with the assurance that equivalent runs will generate identical results. Current best practice aims at this using a combination of package managers, workflow engines, and containers. Results We present BioNix, a lightweight library built on the Nix deployment system. BioNix manages software dependencies, computational environments, and workflow stages together using a single abstraction: pure functions. This lets users specify workflows in a clean, uniform way, with strong reproducibility guarantees. Availability and Implementation BioNix is implemented in the Nix expression language and is released on GitHub under the 3-clause BSD license: https://github.com/PapenfussLab/bionix (biotools:BioNix) (BioNix, RRID:SCR_017662).

Funder

National Health and Medical Research Council

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Health Informatics

Reference54 articles.

1. Reality check on reproducibility;Nature,2016

2. Challenges in irreproducible research;Nature,2018

3. Bioconda: Sustainable and comprehensive software distribution for the life sciences;Grüning;Nat Methods,2018

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