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Advantages and Limits in the Adoption of Reproducible Research and R-Tools for the Analysis of Omic Data

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9874))

Abstract

Reproducible (computational) Research is crucial to produce transparent and high quality scientific papers. First, we illustrate the benefits that scientific community can receive from the adoption of Reproducible Research standards in the analysis of high-throughput omic data. Then, we describe several tools useful to researchers to increase the reproducibility of their works. Moreover, we face the advantages and limits of reproducible research and how they could be addressed and solved. Overall, this paper should be considered as a proof of concept on how and what characteristic - in our opinion - should be considered to conduct a study in the spirit of Reproducible Research. Therefore, the scope of this paper is two-fold. The first goal consists in presenting and discussing some easy-to-use instruments for data analysts to promote reproducible research in their analyses. The second aim is to encourage developers to incorporate automatic reproducibility features in their tools.

F. Russo—Now at Istitute of Protein and Biochemistry - CNR, Napoli, Italy.

F. Russo and D. Righelli—Equally contributing authors.

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Acknowledgments

This work was supported by the Epigen Project.

Author information

Authors and Affiliations

  1. Istituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, Napoli, Italy

    Francesco Russo, Dario Righelli & Claudia Angelini

  2. Dipartimento di Scienze Aziendali - Management and Innovation Systems (DISA-MIS), Universitá di Salerno, Fisciano, Italy

    Dario Righelli

Authors
  1. Francesco Russo
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  2. Dario Righelli
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  3. Claudia Angelini
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Corresponding author

Correspondence to Dario Righelli .

Editor information

Editors and Affiliations

  1. CNR, Istituto per le Applicazioni del Calcolo, Naples, Italy

    Claudia Angelini

  2. Center for Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milano, Italy

    Paola MV Rancoita

  3. DIBRIS, University of Genoa, Genova, Italy

    Stefano Rovetta

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Russo, F., Righelli, D., Angelini, C. (2016). Advantages and Limits in the Adoption of Reproducible Research and R-Tools for the Analysis of Omic Data. In: Angelini, C., Rancoita, P., Rovetta, S. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2015. Lecture Notes in Computer Science(), vol 9874. Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_19

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  • DOI: https://doi.org/10.1007/978-3-319-44332-4_19

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