End-to-End Optimization of High-Throughput DNA Sequencing

At the core of Illumina's high-throughput DNA sequencing platforms lies a biophysical surface process that results in a random geometry of clusters of homogeneous short DNA fragments typically hundreds of base pairs long-bridge amplification. The statistical properties of this random process and the lengths of the fragments are critical as they affect the information that can be subsequently extracted, that is, density of successfully inferred DNA fragment reads. The ensembles of overlapping DNA fragment reads are then used to computationally reconstruct the much longer target genome sequence. The success of the reconstruction in turn depends on having a sufficiently large ensemble of DNA fragments that are sufficiently long. In this article using stochastic geometry, we model and optimize the end-to-end flow cell synthesis and target genome sequencing process, linking and partially controlling the statistics of the physical processes to the success of the final computational step. Based on a rough calibration of our model, we provide, for the first time, a mathematical framework capturing the salient features of the sequencing platform that serves as a basis for optimizing cost, performance, and/or sensitivity analysis to various parameters.