Summarizing performance for genome scale measurement of miRNA: reference samples and metrics

Area of Science:

• Biomolecular analysis
• Genomics and transcriptomics
• Biomarker discovery

Background:

• MicroRNA (miRNA) profiling is crucial for identifying biomarkers for early disease detection.
• Genome-scale miRNA measurements require robust processes for measurement assurance.
• This study evaluated the utility of specific total RNA samples as process controls for miRNA profiling.

Purpose of the Study:

• To assess the utility of defined total RNA samples as process controls for genome-scale miRNA profiling.
• To establish benchmark values for evaluating miRNA measurement systems.
• To develop data visualization and summary metrics for miRNA profiling assessment.

Main Methods:

• Utilized pure human RNA samples (brain, liver, placenta) and their mixtures as control samples.
• Evaluated samples across multiple measurement systems, sites, and rounds.
• Employed in silico modeling, next-generation sequencing (NGS), genome-scale hybridization assays, and RT-PCR for data generation and validation.

Main Results:

• Benchmark values were established by in silico modeling and physical mixtures.
• Data from participating biomarker development laboratories using routine workflows were collected and analyzed.
• A range of performance was observed, leading to the development of data visualization and summary metrics.
• An online data analysis pipeline was created to display performance metrics and serve as a repository.

Conclusions:

• The developed set of reference samples provides benchmark values for assessing genome-scale miRNA profiling.
• An online resource incorporating these metrics allows laboratories to periodically evaluate their performance.
• This facilitates the assessment of changes in measurement processes and ensures reliable biomarker discovery.