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Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome

How easy is it to reproduce the results found in a typical computational biology paper? Either through experience or intuition the reader will already know that the answer is with difficulty or not at all. In this paper we attempt to quantify this difficulty by reproducing a previously published pap...

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Detalles Bibliográficos
Autores principales: Garijo, Daniel, Kinnings, Sarah, Xie, Li, Xie, Lei, Zhang, Yinliang, Bourne, Philip E., Gil, Yolanda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3842296/
https://www.ncbi.nlm.nih.gov/pubmed/24312207
http://dx.doi.org/10.1371/journal.pone.0080278
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author Garijo, Daniel
Kinnings, Sarah
Xie, Li
Xie, Lei
Zhang, Yinliang
Bourne, Philip E.
Gil, Yolanda
author_facet Garijo, Daniel
Kinnings, Sarah
Xie, Li
Xie, Lei
Zhang, Yinliang
Bourne, Philip E.
Gil, Yolanda
author_sort Garijo, Daniel
collection PubMed
description How easy is it to reproduce the results found in a typical computational biology paper? Either through experience or intuition the reader will already know that the answer is with difficulty or not at all. In this paper we attempt to quantify this difficulty by reproducing a previously published paper for different classes of users (ranging from users with little expertise to domain experts) and suggest ways in which the situation might be improved. Quantification is achieved by estimating the time required to reproduce each of the steps in the method described in the original paper and make them part of an explicit workflow that reproduces the original results. Reproducing the method took several months of effort, and required using new versions and new software that posed challenges to reconstructing and validating the results. The quantification leads to “reproducibility maps” that reveal that novice researchers would only be able to reproduce a few of the steps in the method, and that only expert researchers with advance knowledge of the domain would be able to reproduce the method in its entirety. The workflow itself is published as an online resource together with supporting software and data. The paper concludes with a brief discussion of the complexities of requiring reproducibility in terms of cost versus benefit, and a desiderata with our observations and guidelines for improving reproducibility. This has implications not only in reproducing the work of others from published papers, but reproducing work from one’s own laboratory.
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spelling pubmed-38422962013-12-05 Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome Garijo, Daniel Kinnings, Sarah Xie, Li Xie, Lei Zhang, Yinliang Bourne, Philip E. Gil, Yolanda PLoS One Research Article How easy is it to reproduce the results found in a typical computational biology paper? Either through experience or intuition the reader will already know that the answer is with difficulty or not at all. In this paper we attempt to quantify this difficulty by reproducing a previously published paper for different classes of users (ranging from users with little expertise to domain experts) and suggest ways in which the situation might be improved. Quantification is achieved by estimating the time required to reproduce each of the steps in the method described in the original paper and make them part of an explicit workflow that reproduces the original results. Reproducing the method took several months of effort, and required using new versions and new software that posed challenges to reconstructing and validating the results. The quantification leads to “reproducibility maps” that reveal that novice researchers would only be able to reproduce a few of the steps in the method, and that only expert researchers with advance knowledge of the domain would be able to reproduce the method in its entirety. The workflow itself is published as an online resource together with supporting software and data. The paper concludes with a brief discussion of the complexities of requiring reproducibility in terms of cost versus benefit, and a desiderata with our observations and guidelines for improving reproducibility. This has implications not only in reproducing the work of others from published papers, but reproducing work from one’s own laboratory. Public Library of Science 2013-11-27 /pmc/articles/PMC3842296/ /pubmed/24312207 http://dx.doi.org/10.1371/journal.pone.0080278 Text en © 2013 Garijo et al http://creativecommons.org/licenses/by/4.0/ 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 properly credited.
spellingShingle Research Article
Garijo, Daniel
Kinnings, Sarah
Xie, Li
Xie, Lei
Zhang, Yinliang
Bourne, Philip E.
Gil, Yolanda
Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome
title Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome
title_full Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome
title_fullStr Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome
title_full_unstemmed Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome
title_short Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome
title_sort quantifying reproducibility in computational biology: the case of the tuberculosis drugome
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3842296/
https://www.ncbi.nlm.nih.gov/pubmed/24312207
http://dx.doi.org/10.1371/journal.pone.0080278
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