Direct high-throughput deconvolution of non-canonical bases via nanopore sequencing and bootstrapped learning

Mauricio Perez, Michiko Kimoto, Priscilla Rajakumar, Chayaporn Suphavilai, Rafael Peres da Silva, Hui Pen Tan, Nicholas Ting Xun Ong, Hannah Nicholas, Ichiro Hirao, Wei Leong Chew and Niranjan Nagarajan ()
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Mauricio Perez: Genome
Michiko Kimoto: Nanos
Priscilla Rajakumar: Genome
Chayaporn Suphavilai: Genome
Rafael Peres da Silva: Genome
Hui Pen Tan: Nanos
Nicholas Ting Xun Ong: Genome
Hannah Nicholas: Genome
Ichiro Hirao: Nanos
Wei Leong Chew: Genome
Niranjan Nagarajan: Genome

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract The discovery of non-canonical bases (NCBs) and development of synthetic xeno-nucleic acids (XNAs) has spawned interest in many applications in viral genomics, synthetic biology and DNA storage. However, inability to do high-throughput sequencing of NCBs has been a significant limitation. We demonstrate that XNAs with NCBs can be robustly sequenced on a MinION system ( > 2.3×106 reads/flowcell) to obtain significantly distinct signals from controls (median fold-change >6×). To enable AI-model training, we synthesized and sequenced a complex pool of 1,024 NCB-containing oligonucleotides with varied 6-mer contexts and high purity ( > 90%). Bootstrapped models assisted in data preparation, and data augmentation with spliced reads provided high context diversity, enabling learning of generalizable models to decipher NCB-containing sequences with high accuracy ( > 80%) and specificity (99%). These results highlight the versatility of nanopore sequencing for interrogating unusual nucleic acids, and the potential to transform the study of genetic material beyond those that use canonical bases.

Date: 2025
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DOI: 10.1038/s41467-025-62347-z

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