High accuracy DNA sequencing on a small, scalable platform via electrical detection of single base incorporations
Esfandyarpour, H.; Parizi, K. B.; Rezaei Barmi, M.; Rategh, H.; Wang, L.; Paliwal, S.; Golnabi, H. R.; Kenney, P.; Reel, R.; Lee, F.; Gomes, X.; Stern, S.; Ramachandran, A.; Sankar, S.; Doomson, S.; Ung, R.; Jouzi, M.; Akula Suresh Babu, R.; Nabi, A.; Castillo, N.; Lei, R.; Fallahi, M.; LoPrete, E.; Kemper, A.; Bagchi, S.; Tarbox, R.; Choudhary, P.; Nezamfar, H.; Hsie, L.; Monier, N.; Clark, T. A.; Spence, E.; Yang, F.; Bronson, B.; Sutton, G.; Schweidenback, C.; Lundy, J.; Ho, A.; Tangprasertchai, N.; Thomas, A.; Baxter, B.; Shastry, S.; Barua, A.; Chen, Y.; Hashemzadeh, H.; Shtern, D.; Kim,
Show abstract
High throughput DNA sequencing technologies have undergone tremendous development over the past decade. Although optical detection-based sequencing has constituted the majority of data output, it requires a large capital investment and aggregation of samples to achieve optimal cost per sample. We have developed a novel electronic detection-based platform capable of accurately detecting single base incorporations. The GenapSys technology with its electronic detection modality allows the system to be compact, accessible, and affordable. We demonstrate the performance of the system by sequencing several different microbial genomes with varying GC content. The platform is capable of generating up to 2 Gb of high-quality nucleic acid sequence in a single run. We routinely generate sequence data that exceeds 99% raw accuracy with read lengths of up to 175 bp. Average quality scores remain above Q30 (99.9% raw sequencing accuracy) beyond 150 bp, with more than 85% of total bases at or above Q30. The utility of the platform is highlighted by targeted sequencing of the human genome. We show high concordance of SNP detection on the human NA12878 HapMap cell line with data generated on the Illumina sequencing platform. In addition, we sequenced a targeted panel of cancer-associated genes in a well characterized reference standard. With multiple library preparation approaches on this sample, we were able to identify low frequency mutations at expected allele frequencies.
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