CIF:Small:Next Generation DNA Sequencing: Signal Processing Perspectives

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An individual's genetic makeup, inferred by means of DNA sequencing, will help determine the individual's susceptibility to a broad range of chronic and acute diseases or disorders, enable the discovery and clinical testing of new pharmaceutical products, and generally personalize and improve the delivery of health care. Before the promised benefits of personalized medicine come to fruition, DNA sequencing technology must become fast, affordable, and reliable. High cost and labor intensive nature of conventional sequencing technology render it unfit for routine sequencing tasks. Recently developed sequencing-by-synthesis is a novel high-throughput technique addressing these obstacles -- currently, it achieves a cost reduction of two orders of magnitude as compared to the conventional method. However, fidelity and sequence read-lengths of sequencing-by-synthesis are inferior to those of the costly conventional technology, and its overall performance is insufficient for most medical studies.The goal of this research is to develop signal processing techniques which enable accurate and reliable DNA sequence detection in sequencing-by-synthesis systems. The investigator specifically aims to: (1) Develop mathematical models of sequencing-by-synthesis process and derive techniques for inferring parameters of such models. (2) Design computationally efficient algorithms for optimal DNA sequence detection in sequencing-by-synthesis systems. (3) Validate the obtained theoretical results on experimental data. The results of the outlined work are expected to have a major impact on the development and applications of high-performance affordable DNA sequencing.