bioRxivGenomicsPreprint
A single-molecule nanopore sequencing platform
Jiayuan ZhangiD, Yuning ZhangiD, Lele WangiD, Fei GuoiD, Quanxin Yun, Tao ZengiD, Yan XuiD, Lei Yu, Lei ChengiD, Wei WuiD, Xiao Shi, Junyi CheniD, Yuhui SuniD, Jingnan YangiD, Rongrong GuoiD, Xian‐Da ZhangiD, Liu’er Kong, Zongan WangiD, Junlei Yao, Yangsheng Tan, Liuxin ShiiD, Zhentao Zhao, Zhongwang Feng, Xiaopeng YuiD, Chuang LiiD, , Yuliang DongiD
- Citations
- 25
Abstract
Nanopore sequencing, a third-generation sequencing technology, has revolutionized the gene sequencing industry with its advantages of long reads, fast speed, real-time sequencing and analysis, and potential in detecting base modifications. This technology allows researchers to sequence longer DNA fragments in a single read, providing more comprehensive genomic information compared to previous methods. Nanopore sequencing operates on electrical signals generated by a nanopore embedded in a membrane separating two electrolyte-filled chambers. When single-stranded DNA (ssDNA) passes through the nanopore, it creates variations in the current that correspond to different DNA bases. By analyzing these current fluctuations with machine learning algorithms, the DNA sequence can be determined. In this study, we introduced several improvements to nanopore sequencing, including nanopore local chemistry sequencing, novel motor and pore proteins, chip design, and basecalling algorithms. Our new nanopore sequencing platform, CycloneSEQ, demonstrated long-duration sequencing (107 hours) on a single chip with high yield (>50 Gb). In human genomic DNA sequencing, CycloneSEQ was able to produce long reads with N50 33.6 kb and modal identity 97.0%. Preliminary findings on human whole-genome de novo assembly, variant calling, metagenomics sequencing, and single-cell RNA sequencing have further highlighted CycloneSEQs potential across different areas of genomics.
- organism: human
- assay: long-read sequencing
- research focus: variant calling
- research focus: deconvolution
- research focus: gene regulatory network inference
- approach: experimental assay
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