High accuracy DNA sequencing on a small, scalable platform via electrical detection of single base incorporations

Hesaam Esfandyarpour(Genapsys (United States)), Kosar B. Parizi(Genapsys (United States)), Meysam R. Barmi(Genapsys (United States)), Hamid Rategh(Genapsys (United States)), Lisen Wang(Genapsys (United States)), Saurabh Paliwal(Genapsys (United States)), HamidReza Golnabi(Genapsys (United States)), Paul Kenney(Genapsys (United States)), Richard Reel(Genapsys (United States)), Frank S. Lee(Genapsys (United States)), Xavier V. Gomes(Genapsys (United States)), Seth Stern(Genapsys (United States)), Ashok Ramachandran(Genapsys (United States)), Subra Sankar(Genapsys (United States)), Solomon Doomson(Genapsys (United States)), Rick Ung(Genapsys (United States)), Maryam Jouzi(Genapsys (United States)), Ramya Akula Suresh Babu(Genapsys (United States)), Ali Nabi(Genapsys (United States)), Néstor Castillo-Magallanes(Genapsys (United States)), R. Lei(Genapsys (United States)), Mohammad Fallahi(Genapsys (United States)), Eric LoPrete(Genapsys (United States)), Austin Kemper(Genapsys (United States)), Srijeeta Bagchi(Genapsys (United States)), Robert Tarbox(Genapsys (United States)), Pallavi K. Choudhary(Genapsys (United States)), Hooman Nezamfar(Genapsys (United States)), Linda Hsie(Genapsys (United States)), Nicolas Monier(Genapsys (United States)), Tyson A. Clark(Genapsys (United States)), Eric J. Spence(Genapsys (United States)), Fei Yang(Genapsys (United States)), Benjamin Bronson(Genapsys (United States)), Gina Sutton(Genapsys (United States)), Caterina T. H. Schweidenback(Genapsys (United States)), John D. Lundy(Genapsys (United States)), An Ho(Genapsys (United States)), Narin S. Tangprasertchai(Genapsys (United States)), A. W. Thomas(Genapsys (United States)), Brian Baxter(Genapsys (United States)), Shankar Shastry(Genapsys (United States)), Anooshka Barua(Genapsys (United States)), Yongzhi Chen(Genapsys (United States)), Hamid Hashemzadeh(Genapsys (United States)), David Shtern(Genapsys (United States)), Eugene Kim(Genapsys (United States)), Christopher A. Thomas(Genapsys (United States)), Patrice Tanti(Genapsys (United States)), Ali Mazouchi(Genapsys (United States)), Erden Tumurbaatar(Genapsys (United States)), Jordan Nieboer(Genapsys (United States)), Christopher Knopf(Genapsys (United States)), Hien Tram(Genapsys (United States)), Vipal Sood(Genapsys (United States)), S. W. Stingley(Genapsys (United States)), Megan Cahill(Genapsys (United States)), Sid Roy(Genapsys (United States)), Ky Sha(Genapsys (United States)), Bin Dong(Genapsys (United States)), Frank Witney(Genapsys (United States)), Ronald W. Davis(Genapsys (United States))
bioRxiv (Cold Spring Harbor Laboratory)
April 16, 2019
Cited by 10Open Access
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Abstract

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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