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Enabling high-accuracy long-read amplicon sequences using unique molecular identifiers

High-throughput amplicon sequencing is a powerful method for analysing variation in defined genetic regions. The method is therefore ideal for studying genetic populations with low abundant variants or high heterogeneity, such as cancer driver genes, virus populations and microbial communities, among others. Current short-read technologies are often employed for amplicon sequencing due to their high accuracy (~99.9%); but are incapable of generating amplicons over 500-2000 bp in length, which limits long-range information and assay resolution. Here, we describe a new approach to generate long amplicons with the Oxford Nanopore Technologies MinION that exceeds the accuracy of current short-read technologies while generating amplicons up to 10,000 bp in length. Our new method utilizes unique molecular identifiers (UMIs) annealed to each end of the template molecule prior to amplification and sequencing. A downstream bioinformatics approach was developed to bin reads based on their dual UMIs, filter chimeras, and generate polished consensus sequences. We demonstrated this new approach by generating over 10,000 amplicon consensus sequences from full-length ribosomal RNA (rRNA) operons of a mock microbial community (average 4500 bp in length) using both R9.4 and R10.3 flow cells. The average residual error rate in the amplicon sequences was 0.01%, with essentially no detectable chimeras. We anticipate the simplicity and low cost of this method to revolutionize virtually any amplicon sequencing application by allowing for full gene or operons to be accurately sequenced.

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Ryan Ziels

Ryan Ziels, The University of British Columbia

Abstract

High-throughput amplicon sequencing is a powerful method for analysing variation in defined genetic regions. The method is therefore ideal for studying genetic populations with low abundant variants or high heterogeneity, such as cancer driver genes, virus populations and microbial communities, among others. Current short-read technologies are often employed for amplicon sequencing due to their high accuracy (~99.9%); but are incapable of generating amplicons over 500-2000 bp in length, which limits long-range information and assay resolution. Here, we describe a new approach to generate long amplicons with the Oxford Nanopore Technologies MinION that exceeds the accuracy of current short-read technologies while generating amplicons up to 10,000 bp in length. Our new method utilizes unique molecular identifiers (UMIs) annealed to each end of the template molecule prior to amplification and sequencing. A downstream bioinformatics approach was developed to bin reads based on their dual UMIs, filter chimeras, and generate polished consensus sequences. We demonstrated this new approach by generating over 10,000 amplicon consensus sequences from full-length ribosomal RNA (rRNA) operons of a mock microbial community (average 4500 bp in length) using both R9.4 and R10.3 flow cells. The average residual error rate in the amplicon sequences was 0.01%, with essentially no detectable chimeras. We anticipate the simplicity and low cost of this method to revolutionize virtually any amplicon sequencing application by allowing for full gene or operons to be accurately sequenced.

Bio

Dr. Ryan Ziels is an Assistant Professor in the Department of Civil Engineering, with an appointment in the Genome Sciences and Technology Training Program at The University of British Columbia. His research focuses on the role of microbial communities in converting waste materials into high-value resources, such as bioenergy, nutrients, and clean water. He combines multi-omic sequencing with biological process modeling and fundamental engineering design to elucidate mechanisms of nutrient and carbon flow within engineered microbiomes. Over the past few years, his research has focused on new approaches for mapping metabolic networks by combining stable isotope probing with multi-omics sequencing data, including long-read metagenomic sequencing with the Oxford Nanopore Technologies platform.