Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines.

Area of Science:

• Computational biology
• Bioinformatics
• Data science

Background:

• Mass spectrometry advancements drive integration of proteomics and big data science.
• Bioinformatics analysis in proteomics and metabolomics is increasingly complex, utilizing multiple algorithms and tools.
• Existing computational tools often lack scalability and reproducibility due to single-tiered architectures.

Purpose of the Study:

• To summarize key data processing steps and tools in computational proteomics and metabolomics.
• To discuss the integration of software containers with workflow environments for large-scale analysis.
• To introduce a novel approach for reproducible and scalable data analysis in these fields.

Main Methods:

• Review of current computational proteomics and metabolomics tools and workflows.
• Discussion on the use of software containers (BioContainers) for managing dependencies and ensuring reproducibility.
• Integration of BioContainers with popular workflow management systems (Galaxy and Nextflow).

Main Results:

• Identification of limitations in current single-tiered software applications for large-scale data analysis.
• Demonstration of the potential of combining software containers with workflow environments.
• Introduction of a new framework for enhanced scalability and reproducibility in omics data analysis.

Conclusions:

• The integration of BioContainers with workflow environments like Galaxy and Nextflow offers a promising solution for scalable and reproducible computational proteomics and metabolomics.
• This approach addresses the limitations of existing tools, facilitating more robust big data analysis in life sciences.
• The proposed framework is expected to benefit the proteomics and metabolomics communities by improving data analysis efficiency and reliability.