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

• DOI: 10.1186/s12864-015-1912-7
• Publication Date: 2015-09-17T13:08:11Z
• Authors: Jerod Parsons; Sarah Munro; P. Scott Pine; Jennifer McDaniel; Michele Mehaffey; Marc Salit
• Keywords: Benchmarking; Benchmark (surveying)

Genome-scale "-omics" measurements are challenging to benchmark due the enormous variety of unique biological molecules involved. Mixtures previously-characterized samples can be used repeatability and reproducibility using component proportions as truth for measurement. We describe evaluate experiments characterizing performance RNA-sequencing (RNA-Seq) measurements, discuss cases where mixtures serve effective process controls.We apply a linear model total RNA mixture in RNA-seq experiments. This provides context benchmarking. The parameters fit experimental results evaluated assess bias variability measurement mixture. A describes behavior expression measures Residuals from fitting data metric evaluating effect that an individual step has on response function precision underlying while identifying signals affected by interference other sources. Effective benchmarking requires well-defined mixtures, which RNA-Seq knowledge post-enrichment 'target RNA' content components. demonstrate method suitable use genome-scale control lay out utilizing spike-in controls determine enriched samples.Genome-scale derived mixtures. These relate prior components complex mixture, allowing assessment performance. target fraction accounts differential selection variable samples. Spike-in utilized measure this relationship between input RNA. Our analysis also enables estimation unknown even when component-specific markers not previously known, whenever pure measured alongside