TRNA isodecoder analysis using Nanopore ionic current signals and deep learning.

ABSTRACT: We describe application of Nanopore direct tRNA sequencing and ionic current signal for isodecoder-level analysis of bacterial and yeast tRNA. The computational advance is the application of deep learning for predicting tRNA isodecoders directly from raw nanopore ionic current signals. We then combined ionic current analysis with a pairwise sequence alignment strategy to improve tRNA sequence alignments. We applied this strategy to Nanopore data for E. coli and S. cerevisiae tRNA and aligned 2.6% and 13.1% more reads respectively when compared to present strategies for Nanopore tRNA analysis. Notably, this increase in alignments also came with a significant increase in alignment identity. The ionic current models confirmed enrichment of both an individual tRNA isotype and a t

SUBMITTER: Akeson S 

PROVIDER: S-EPMC12767535 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature