A bioinformatics workflow to decipher transcriptomic data from vitamin D studies.

Area of Science:

• Bioinformatics and Genomics
• Molecular Biology
• Systems Biology

Background:

• Integrating experimental lab studies with bioinformatics is crucial for developing comprehensive analytical workflows.
• Existing bioinformatics tools and pipelines often lack seamless connectivity, hindering the exploitation of multi-level data.
• Gene expression data from large-scale functional genomic studies offers rich biological insights but requires sophisticated analysis pipelines.

Purpose of the Study:

• To describe a novel, user-friendly bioinformatics workflow for analyzing gene expression data at multiple biological levels.
• To demonstrate the workflow's utility by applying it to vitamin D research in myeloid cells.
• To showcase how integrating diverse bioinformatics tools can enhance the interpretation of functional genomic studies.

Main Methods:

• Development of a bioinformatics pipeline integrating various software and algorithms.
• Analysis of gene expression data from large-scale functional genomic experiments.
• Utilizing multiple databases for comprehensive interpretation of gene expression data at pathway, metabolic, ontology, and molecular levels.

Main Results:

• The workflow successfully contextualizes gene expression data at multiple biological levels, providing significant biological insight.
• Application to vitamin D-treated myeloid cells revealed modulation of crucial metabolic pathways.
• The developed pipeline is open, reproducible, and user-friendly, facilitating broader adoption.

Conclusions:

• The presented bioinformatics workflow effectively integrates diverse tools for multi-level analysis of gene expression data.
• Vitamin D significantly modulates key metabolic pathways in myeloid cells, impacting their immune function.
• This approach enhances the understanding and interpretation of functional genomic studies, particularly in the vitamin D research community.