KinMethyl: robust methylation detection in prokaryotic SMRT sequencing via kinetic signal modeling and deep feature integration.

ABSTRACT:

Motivation

Accurate detection of 5-methylcytosine (5mC) from PacBio single-molecule real-time (SMRT) sequencing remains challenging in prokaryotes due to weak kinetic signals and motif diversity.

Results

Here, we present KinMethyl, a generalizable deep learning framework that integrates sequence and kinetic signals to improve methylation detection across diverse bacterial genomes. Central to our approach is a regression model trained on whole-genome amplified samples to estimate the expected kinetics signals of unmethylated sequences. These predicted signals are incorporated into a downstream classifier to enhance the performance under low signal-to-noise conditions. KinMethyl outperforms existing tools such as kineticstools and ccsmeth across multiple bacterial species, met

SUBMITTER: Zhang J 

PROVIDER: S-EPMC12552093 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature