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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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Reprohackathons: promoting reproducibility in bioinformatics through training.

Thomas Cokelaer^1,2, Sarah Cohen-Boulakia³, Frédéric Lemoine^2,4

¹Institut Pasteur, Université Paris Cité, Plate-Forme Technologique Biomics, 75015 Paris, France.

Bioinformatics (Oxford, England)

|June 30, 2023

Summary

This summary is machine-generated.

Integrating reproducibility training into Master

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Area of Science:

Bioinformatics
Computational Biology
Scientific Reproducibility

Background:

The reproducibility crisis necessitates better bioinformatics data analysis practices.
Tools for reproducibility exist but face adoption challenges.
Master's-level education is key to widespread adoption.

Purpose of the Study:

To present the Reprohackathon, a Master's course designed to teach bioinformatics reproducibility.
To evaluate the effectiveness of integrating reproducibility concepts and tools into a Master's curriculum.

Main Methods:

A 3-year Master's course (Reprohackathon) involving 123 students.
Curriculum covers reproducibility challenges, version control, containerization, and workflow systems.
Students undertake a 3-4 month project reanalyzing published data.

Main Results:

Implementing reproducible analyses is complex and demands significant effort.
In-depth teaching significantly enhances student understanding and skills in reproducible bioinformatics.
The Reprohackathon provided valuable insights into teaching reproducibility.

Conclusions:

Master's programs are crucial for establishing reproducibility as a standard in bioinformatics.
Dedicated training improves students' ability to conduct and share reproducible data analyses.
The Reprohackathon model effectively addresses the need for practical reproducibility skills.