Return on investment and library complexity analysis for DNA sequencing

Abstract

Understanding the profiles of information acquisition during DNA sequencing experiments is critical to the design and implementation of large-scale studies in medical and population genetics. One known technical challenge and cost driver in next-generation sequencing data is the occurrence of non-independent observations that are created from sequencing artifacts and duplication events from polymerase chain reaction (PCR). The current study demonstrates improved return on investment (ROI) modeling strategies to better anticipate the impact of non-independent observations in multiple forms of next-generation sequencing data. Here, a physical modeling approach based on Pó1ya urn was evaluated using both multi-point estimation and duplicate set occupancy vectors. The results of this study can be used to reduce sequencing costs by improving aspects of experimental design including sample pooling strategies, top-up events, and termination of non-informative samples.