Improving an rRNA depletion protocol with statistical design of experiments

rRNA depletion is the most expensive step in prokaryotic RNA-seq library preparation. rRNA is so abundant that small increases in depletion efficiency lead to large changes in mRNA sequencing coverage. A variety of commercial and home-made methods exist to lower the cost or increase the efficiency of rRNA removal. Many of these techniques are suboptimal when applied to new species of bacteria or when the protocol or reagents need to be changed. Re-optimizing a protocol by trial-and-error is an expensive and laborious process. Systematic frameworks like the statistical design of experiments (DOE) can improve processes by exploring the quantitative relationship between multiple factors. DOE allows experimenters to find factor interactions that may not be apparent when factors are studied in isolation. We used DOE to optimize an rRNA depletion protocol by updating reagents and identifying factors that maximize rRNA removal and minimize cost. The optimized protocol more efficiently removes rRNA, uses fewer reagents, and is less expensive than the original protocol. Our optimization required only 17 experiments and identified two significant interactions among three factors. Overall, our approach demonstrates the utility of a rational, DOE framework for improving complex protocols.