Thinking BIG rheumatology: how to make functional genomics data work for you

Area of Science:

• Genomics
• Rheumatology
• Bioinformatics

Background:

• High-throughput sequencing generates vast biological data, posing challenges for integration into research.
• Functional genomics provides methods to leverage these large datasets for understanding complex diseases like rheumatic conditions.

Purpose of the Study:

• To provide a primer on applying functional genomics and "big data" to rheumatology research.
• To illustrate the execution of functional genomics research with examples from literature and author's work.

Main Methods:

• Emphasizes the importance of study design for selecting appropriate samples.
• Discusses integrating diverse sequencing data types (gene expression, chromatin structure, single-cell) into gene regulatory network models.
• Highlights the iterative, hypothesis-driven application of bioinformatic methods.

Main Results:

• Functional genomics approaches can uncover disease mechanisms by analyzing complex biological data.
• The integration of diverse data types is key to building comprehensive models of gene regulation.

Conclusions:

• Functional genomics offers powerful tools for advancing rheumatology research through big data analysis.
• Future directions include greater integration of big data, computationally trained researchers, and interactive databases in rheumatology.