Lin Xuan, Waring Katherine, Ghezzi Hans, Tropini Carolina, Tyson John, Ziels Ryan M
Civil Engineering, The University of British Columbia, 6250 Applied Science Ln #2002, Vancouver, BC, CanadaV6T 1Z4.
Graduate Program in Bioinformatics, The University of British Columbia, Vancouver, BC, CanadaV5Z 4S6.
PNAS Nexus. 2024 Oct 9;3(10):pgae411. doi: 10.1093/pnasnexus/pgae411. eCollection 2024 Oct.
Small subunit (SSU) ribosomal RNA (rRNA) gene amplicon sequencing is a foundational method in microbial ecology. Currently, short-read platforms are commonly employed for high-throughput applications of SSU rRNA amplicon sequencing, but at the cost of poor taxonomic classification due to limited fragment lengths. The Oxford Nanopore Technologies (ONT) platform can sequence full-length SSU rRNA genes, but its lower raw-read accuracy has so-far limited accurate taxonomic classification and de novo feature generation. Here, we present a sequencing workflow, termed , that combines unique molecular identifier (UMI)-based error correction with newer (R10.4+) ONT chemistry and sample barcoding to enable high throughput near full-length SSU rRNA (e.g. 16S rRNA) amplicon sequencing. The ssUMI workflow generated near full-length 16S rRNA consensus sequences with 99.99% mean accuracy using a minimum subread coverage of 3×, surpassing the accuracy of Illumina short reads. The consensus sequences generated with ssUMI were used to produce error-free de novo sequence features with no false positives with two microbial community standards. In contrast, Nanopore raw reads produced erroneous de novo sequence features, indicating that UMI-based error correction is currently necessary for high-accuracy microbial profiling with R10.4+ ONT sequencing chemistries. We showcase the cost-competitive scalability of the ssUMI workflow by sequencing 87 time-series wastewater samples and 27 human gut samples, obtaining quantitative ecological insights that were missed by short-read amplicon sequencing. ssUMI, therefore, enables accurate and low-cost full-length 16S rRNA amplicon sequencing on Nanopore, improving accessibility to high-resolution microbiome science.
小亚基（SSU）核糖体RNA（rRNA）基因扩增子测序是微生物生态学中的一种基础方法。目前，短读长平台常用于SSU rRNA扩增子测序的高通量应用，但由于片段长度有限，其分类学分类效果较差。牛津纳米孔技术（ONT）平台可以对全长SSU rRNA基因进行测序，但其原始读长准确性较低，迄今为止限制了准确的分类学分类和从头特征生成。在这里，我们提出了一种名为ssUMI的测序工作流程，该流程将基于独特分子标识符（UMI）的纠错与更新的（R10.4+）ONT化学和样本条形码相结合，以实现高通量的近全长SSU rRNA（如16S rRNA）扩增子测序。ssUMI工作流程使用至少3倍的子读覆盖度，生成了平均准确率为99.99%的近全长16S rRNA一致性序列，超过了Illumina短读长的准确性。用ssUMI生成的一致性序列用于产生无错误的从头序列特征，在两种微生物群落标准下没有假阳性。相比之下，纳米孔原始读长产生了错误的从头序列特征，这表明基于UMI的纠错对于使用R10.4+ ONT测序化学进行高精度微生物分析目前是必要的。我们通过对87个时间序列废水样本和27个人类肠道样本进行测序，展示了ssUMI工作流程在成本上具有竞争力的可扩展性，获得了短读长扩增子测序所遗漏的定量生态见解。因此，ssUMI能够在纳米孔上进行准确且低成本的全长16S rRNA扩增子测序，提高了对高分辨率微生物组学的可及性。
