Tsutomu Suzuki

When nanopore direct RNA sequencing reads tRNAs, modifications corrupt basecalling, forcing sequence-mapping pipelines to trade classification accuracy against usable reads. tyCooNN (nano tRNA classifier) instead classifies tRNAs directly from ionic current signals, bypassing basecalling and alignment; its ntc48 model reaches 98.2% classification accuracy across all 48 E. coli tRNA species. An equimolar mixture of 44 tRNAs calibrates and corrects read-count bias, yielding absolute abundances, and binary classifiers call specific modifications at the single-molecule level.

When nanopore direct RNA sequencing reads tRNAs, modifications corrupt basecalling, forcing sequence-mapping pipelines to trade classification accuracy against usable reads. tyCooNN (nano tRNA classifier) instead classifies tRNAs directly from ionic current signals, bypassing basecalling and alignment; its ntc48 model reaches 98.2% classification accuracy across all 48 E. coli tRNA species. An equimolar mixture of 44 tRNAs calibrates and corrects read-count bias, yielding absolute abundances, and binary classifiers call specific modifications at the single-molecule level.