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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18889671 |
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Table of Contents:
- <p>=Raw Nanopore sequencing data of CD-pore-seq V1=</p> <p>==Author==<br>Zicheng Yu1, Zihao Chen1, Thomas C. Williams1,2, Craig J. Anderson1,3,4, Andrew P. Jackson1,*, Martin A.M. Reijns1,*, Martin S. Taylor1,*<br>1 MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, UK.<br>2 Child Life and Health, University of Edinburgh, UK.<br>3 German Cancer Research Center, Heidelberg, Germany<br>4 Department of Translational Oncology, German Cancer Research Center, Munich, Germany.</p> <p>* Correspondence should be addressed to andrew.jackson@ed.ac.uk, martin.reijns@ed.ac.uk and martin.taylor@ed.ac.uk</p> <p>==Citation==<br>'Generalised training for DNA modification detection by nanopore sequencing'</p> <p>==Description==<br>Nanopore sequencing data of libraries to train machine learning models to predict single DNA-embedded ribonucleotides.<br>Ligation kit: LSK-114 (all except for ygDNA) or SQK-NBD114.96 (ygDNA)<br>Flow-cell version: R10.4.1<br>Sequencing device: MinION or PromethION (./TelN/PromethION only)</p> <p>==ygDNA==<br>* Sequencing a pooled yeast gDNA libary on a MinION flow-cell, barcoded using ONT SQK-NBD114.96 kit (barcode05:rnh201-null; barcode06:pol2-M644G+rnh201-null; barcode08: pol3-L612M+rnh201-null).</p>