Training data diversity enhances the basecalling of novel RNA modification-induced nanopore sequencing readouts

Ziyuan Wang, Ziyang Liu, Yinshan Fang, Hao Helen Zhang, Xiaoxiao Sun, Ning Hao (), Jianwen Que () and Hongxu Ding ()
Additional contact information
Ziyuan Wang: University of Arizona
Ziyang Liu: University of Arizona
Yinshan Fang: Columbia University Medical Center
Hao Helen Zhang: University of Arizona
Xiaoxiao Sun: University of Arizona
Ning Hao: University of Arizona
Jianwen Que: Columbia University Medical Center
Hongxu Ding: University of Arizona

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract Accurately basecalling sequence backbones in the presence of nucleotide modifications remains a substantial challenge in nanopore sequencing bioinformatics. It has been extensively demonstrated that state-of-the-art basecallers are less compatible with modification-induced sequencing signals. A precise basecalling, on the other hand, serves as the prerequisite for virtually all the downstream analyses. Here, we report that basecallers exposed to diverse training modifications gain the generalizability to analyze novel modifications. With synthesized oligos as the model system, we precisely basecall various out-of-sample RNA modifications. From the representation learning perspective, we attribute this generalizability to basecaller representation space expanded by diverse training modifications. Taken together, we conclude increasing the training data diversity as a paradigm for building modification-tolerant nanopore sequencing basecallers.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-55974-z Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-55974-z

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-55974-z

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().