NanoCon: contrastive learning-based deep hybrid network for nanopore methylation detection.

ABSTRACT:

Motivation

5-Methylcytosine (5mC), a fundamental element of DNA methylation in eukaryotes, plays a vital role in gene expression regulation, embryonic development, and other biological processes. Although several computational methods have been proposed for detecting the base modifications in DNA like 5mC sites from Nanopore sequencing data, they face challenges including sensitivity to noise, and ignoring the imbalanced distribution of methylation sites in real-world scenarios.

Results

Here, we develop NanoCon, a deep hybrid network coupled with contrastive learning strategy to detect 5mC methylation sites from Nanopore reads. In particular, we adopted a contrastive learning module to alleviate the issues caused by imbalanced data distribution in nanopore sequencing, offeri

SUBMITTER: Yin C 

PROVIDER: S-EPMC10873575 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature