Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers

Area of Science:

• Bioinformatics
• Genomic Data Analysis
• Computational Biology

Background:

• Analyzing high-throughput sequencing data presents significant challenges for biomedical researchers.
• The process from raw sequencing reads to a publishable result often involves complex, custom scripting and ad hoc analyses.
• Reproducibility in genomic data analysis is hindered by the lack of standardized, integrated tools.

Purpose of the Study:

• To describe a novel hybrid platform designed to simplify and integrate the entire bioinformatics workflow.
• To provide a reproducible solution for transforming raw sequencing data into publishable findings.
• To enable interactive data exploration alongside established analysis pathways.

Main Methods:

• Development of a hybrid computational platform integrating common bioinformatics analysis tools.
• Implementation of interactive data exploration capabilities within the platform.
• Focus on streamlining the 'raw data-to-publication' pathway for enhanced usability.

Main Results:

• The platform successfully combines standard analysis pipelines with interactive data exploration.
• It addresses the ad hoc nature of the exploratory stage in genomic data analysis.
• The developed system aims to make the entire data analysis process more accessible and reproducible.

Conclusions:

• The hybrid platform offers a comprehensive and simplified solution for genomic data analysis.
• It enhances reproducibility and reduces the complexity of transforming sequencing data into research publications.
• This approach empowers biomedical researchers by providing integrated and interactive bioinformatics tools.