Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection

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

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning (IL) technique to improve the basecalling of modification-rich sequences, which are usually of high biological interest. With sequence backbones resolved, we further run anomaly detection (AD) on individual nucleotides to determine their modification status. By this means, our pipeline promises the single-molecule, single-nucleotide, and sequence context-free detection of modifications. We benchmark the pipeline using control oligos, further apply it in the basecalling of densely-modified yeast tRNAs and E.coli genomic DNAs, the cross-species detection of N6-methyladenosine (m6A) in mammalian mRNAs, and the simultaneous detection of N1-methyladenosine (m1A) and m6A in human mRNAs. Our IL-AD workflow is available at: https://github.com/wangziyuan66/IL-AD .

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-024-51639-5 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:15:y:2024:i:1:d:10.1038_s41467-024-51639-5

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

DOI: 10.1038/s41467-024-51639-5

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 ().