Genomics pipelines and data integration: challenges and opportunities in the research setting

Area of Science:

• Genomics, proteomics, metabolomics, and lipidomics utilizing high-throughput technologies.

Background:

• High-throughput (HT) technologies enable large-scale genotype-phenotype correlation.
• Advances in cell biology, evolution, and clinical applications are driven by HT data.
• Massive datasets present significant big data challenges in analysis provenance, management, usability, and reproducibility.

Purpose of the Study:

• To review challenges in implementing large-scale bioinformatics best practices.
• To highlight opportunities for improving data tracking and auditing in bioinformatics pipelines.

Main Methods:

• Review of current bioinformatics challenges and best practices.
• Discussion of data management, provenance, and reproducibility in large-scale research.

Main Results:

• Identification of key obstacles in big data analysis for HT technologies.
• Emphasis on the need for robust bioinformatics pipelines.
• Demonstration of how data tracking and auditing enhance consistency.

Conclusions:

• Bioinformatics pipelines incorporating data tracking are crucial for reproducible research.
• These pipelines support basic, translational, and clinical settings.
• Addressing big data challenges is essential for leveraging HT technologies effectively.