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ReproPhylo: An Environment for Reproducible Phylogenomics

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


Amir Szitenberg

Max John

Mark L. Blaxter


Paul P Gardner


© 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.


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.

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 Sep 3, 2015
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
Keywords Phylogenetics, Phylogenomics, Reproducibility, Experimental parameter selection, Comparative analysis, Workflow, Provenance
Public URL
Publisher URL
Additional Information Copy of article first published in: PLoS computational biology, 2015, v.11, issue 9.


Article.pdf (2.3 Mb)

Copyright Statement
© 2015 Szitenberg et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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