tRNA isodecoder analysis using Nanopore ionic current signals and deep learning

Akeson, S.; Kakhaki, P. D.; Esfahani, N. G.; Reinsch, J. L.; Barry, M. L.; Zamecnik, M.; Tzadikario, T.; Abu-Shumays, R. L.; Garcia, D. M.; Koutmou, K.; Jain, M.

doi:10.64898/2025.12.27.696718

bioRxiv · 10.64898/2025.12.27.696718

tRNA isodecoder analysis using Nanopore ionic current signals and deep learning

Akeson, S.·Kakhaki, P. D.·Esfahani, N. G.·Reinsch, J. L.·Barry, M. L.·Zamecnik, M.·Tzadikario, T.·Abu-Shumays, R. L.·Garcia, D. M.·Koutmou, K.·Jain, M.

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Abstract

tRNA are short non-coding RNA characterized by their distinct tertiary structure and abundant chemical modifications. Conventional analysis strategies do not fully characterize tRNA isodecoders. We demonstrate that this limitation can be resolved for tRNA using nanopore ionic current data. We developed tRNAZAP, a deep learning strategy that uses nanopore ionic current signal information to classify native tRNA strands at isodecoder-level resolution without relying on sequence information. Additionally, the ionic current level classification allows for pairwise alignment of read sequences to reference sequences, producing optimal tRNA alignments. We applied tRNAZAP to direct tRNA sequencing data from Escherichia coli and Saccharomyces cerevisiae, and recovered 2.6% and 13.1% more aligned reads than BWA-MEM, respectively. tRNAZAP resolved these reads at an isodecoder-level and with consistently higher alignment identity. tRNAZAP is a powerful complement to sequence-based profiling and can contribute towards resolving the isodecoder landscape in more complex organisms including humans.

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Akeson, S., Kakhaki, P. D., Esfahani, N. G., Reinsch, J. L., Barry, M. L., Zamecnik, M., Tzadikario, T., Abu-Shumays, R. L., Garcia, D. M., Koutmou, K., Jain, M.. 2025-12-28. tRNA isodecoder analysis using Nanopore ionic current signals and deep learning. https://doi.org/10.64898/2025.12.27.696718

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