RT Journal
A1 Teng, Haotian
A1 Stoiber, Marcus
A1 Bar-Joseph, Ziv
A1 Kingsford, Carl
T1 Detecting m6A RNA modification from nanopore sequencing using a semisupervised learning framework
JF Genome Research 
JO Genome Research 
YR 2024 
FD November 01 
VO 34 
IS 11 
SP 1987 
OP 1999 
DO 10.1101/gr.278960.124 
UL http://genome.cshlp.org/content/34/11/1987.abstract 
AB Direct nanopore-based RNA sequencing can be used to detect posttranscriptional base modifications, such as N6-methyladenosine (m6A) methylation, based on the electric current signals produced by the distinct chemical structures of modified bases. A key challenge is the scarcity of adequate training data with known methylation modifications. We present Xron, a hybrid encoder–decoder framework that delivers a direct methylation-distinguishing basecaller by training on synthetic RNA data and immunoprecipitation (IP)-based experimental data in two steps. First, we generate data with more diverse modification combinations through in silico cross-linking. Second, we use this data set to train an end-to-end neural network basecaller followed by fine-tuning on IP-based experimental data with label smoothing. The trained neural network basecaller outperforms existing methylation detection methods on both read-level and site-level prediction scores. Xron is a standalone, end-to-end m6A-distinguishing basecaller capable of detecting methylated bases directly from raw sequencing signals, enabling de novo methylome assembly.