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Experimenting with reproducibility: a case study of robustness in bioinformatics
Yang-Min Kim1,2,3,4, Jean-Baptiste Poline5,6, Guillaume Dumas1,2,3,4
1Human Genetics and Cognitive Functions Unit, Institut Pasteur, 25 rue du Docteur Roux 75015 Paris, France.
Gigascience
|July 3, 2018
Summary
Reproducibility challenges in bioinformatics are common. This study details difficulties in re-running a published method and proposes solutions for better scientific data reusability and research efficiency.
Area of Science:
- Bioinformatics
- Computational Biology
- Scientific Reproducibility
Background:
- Limited reproducibility is a significant issue across scientific disciplines.
- Reusability of scientific data and methods is often poorly documented.
- Challenges in reproducing published bioinformatics methods hinder scientific progress.
Purpose of the Study:
- To investigate difficulties in reproducing a published bioinformatics method.
- To assess the reusability of scientific data and code.
- To propose solutions for enhancing research reproducibility and efficiency.
Main Methods:
- Attempted to re-run a published bioinformatics analysis using provided code and data.
- Reimplemented the method in a Python package (StratiPy) to improve accessibility and execution.
- Evaluated the reusability of the reimplementation and its documentation.
Main Results:
- Encountered difficulties in reproducing the original bioinformatics method.
- Developed a Python package (StratiPy) with documentation and tutorials to facilitate reproducibility.
- Identified specific challenges related to software dependencies and execution environments.
Conclusions:
- Improving reproducibility requires accessible code, data, and clear documentation.
- Developing reusable software packages and providing execution environments (e.g., Docker) can enhance research efficiency.
- Addressing reproducibility issues is crucial for the integrity and advancement of scientific research.
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