ReproPhylo: An Environment for Reproducible Phylogenomics

Szitenberg, Amir; John, Max; Blaxter, Mark L.; Lunt, David H.

Abstract

© 2015 Szitenberg et al. The reproducibility of experiments is key to the scientific process, and particularly necessary for accurate reporting of analyses in data-rich fields such as phylogenomics. We present ReproPhylo, a phylogenomic analysis environment developed to ensure experimental reproducibility, to facilitate the handling of large-scale data, and to assist methodological experimentation. Reproducibility, and instantaneous repeatability, is built in to the ReproPhylo system and does not require user intervention or configuration because it stores the experimental workflow as a single, serialized Python object containing explicit provenance and environment information. This ‘single file’ approach ensures the persistence of provenance across iterations of the analysis, with changes automatically managed by the version control program Git. This file, along with a Git repository, are the primary reproducibility outputs of the program. In addition, ReproPhylo produces an extensive human-readable report and generates a comprehensive experimental archive file, both of which are suitable for submission with publications. The system facilitates thorough experimental exploration of both parameters and data. ReproPhylo is a platform independent CC0 Python module and is easily installed as a Docker image or a WinPython self-sufficient package, with a Jupyter Notebook GUI, or as a slimmer version in a Galaxy distribution.

Citation

Szitenberg, A., John, M., Blaxter, M. L., & Lunt, D. H. (2015). ReproPhylo: An Environment for Reproducible Phylogenomics. PLoS Computational Biology, 11(9), Article ARTN e1004447. https://doi.org/10.1371/journal.pcbi.1004447

• Journal Article Type: Article
• Acceptance Date: Jul 13, 2015
• Online Publication Date: Sep 3, 2015
• Publication Date: Sep 3, 2015
• Deposit Date: Sep 2, 2015
• Publicly Available Date: Oct 27, 2022
• Journal: PLoS computational biology
• Print ISSN: 1553-734X
• Electronic ISSN: 1553-7358
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 9
• Article Number: ARTN e1004447
• DOI: https://doi.org/10.1371/journal.pcbi.1004447
• Keywords: Phylogenetics, Phylogenomics, Reproducibility, Experimental parameter selection, Comparative analysis, Workflow, Provenance
• Public URL: https://hull-repository.worktribe.com/output/378621
• Publisher URL: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004447
• Additional Information: Copy of article first published in: PLoS computational biology, 2015, v.11, issue 9.