Using mixtures of biological samples as process controls for RNA-sequencing experiments

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Benchmarking genome-scale measurements is complex due to the vast diversity of biological molecules.
• Mixtures of known samples offer a method to assess measurement repeatability and reproducibility.
• RNA-sequencing (RNA-Seq) performance characterization is crucial for reliable genomic data.

Purpose of the Study:

• To describe and evaluate experiments for characterizing RNA-sequencing performance using sample mixtures.
• To establish effective process controls for genome-scale measurements.
• To develop a method for assessing bias and variability in RNA-Seq experiments.

Main Methods:

• Application of a linear model to total RNA mixture samples in RNA-seq experiments.
• Evaluation of model parameters to assess measurement bias and variability.
• Utilizing spike-in controls to determine enriched RNA content in total RNA samples.

Main Results:

• A linear model effectively describes mixture expression measures for performance benchmarking.
• Residuals from the model identify experimental steps affecting measurement precision and linearity.
• An experimental method for genome-scale process control using mixtures was demonstrated.

Conclusions:

• Genome-scale process controls can be developed using well-defined mixtures.
• Mixture analysis allows assessment of measurement performance by relating component knowledge to mixture data.
• The method enables estimation of unknown mixture proportions and accounts for differential RNA selection.