Omics Analysis and Quality Control Pipelines in a High-Performance Computing Environment.

Area of Science:

• Multi-omics data analysis
• Bioinformatics pipeline development
• Quality control in life sciences

Background:

• Data quality is critical but often overlooked in omics studies, especially for exposures affecting epigenome and transcriptome.
• Portable quality control (QC) pipelines are essential for consistent analysis across diverse omics datasets.
• Existing methods may lack standardization for comprehensive QC across multiple omics assays.

Purpose of the Study:

• To develop and consolidate portable quality assurance (QA) metrics and pipelines for 12 different omics assays.
• To establish a unified framework for evaluating and ensuring the quality of omics data.
• To address the need for standardized QC in studies involving chemical and pathogen exposures.

Main Methods:

• Developed portable QA metrics and acceptability criteria for 12 omics assays.
• Created containerized QC pipelines using Singularity for portability and scalability.
• Evaluated pipeline performance on human data from the DARPA ECHO program.

Main Results:

• Successfully implemented 12 analytical QC pipelines leveraging standard bioinformatics tools.
• Demonstrated early identification of low-quality datasets and those with insufficient sequencing reads.
• Highlighted experimental protocols requiring refinement based on QC pipeline outputs.

Conclusions:

• The developed omics data analysis and QC pipelines enhance data reliability and facilitate experimental optimization.
• These open-source resources provide a scalable and portable solution for the omics and life sciences communities.
• Standardized QC is crucial for robust interpretation of epigenome and transcriptome data in exposure studies.