# Nanopore Sequencing

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.

RawMod, released by a University of Maryland-led team, stacks the raw nanopore signal of many reads at each site and compares it base by base with the expected signal of the reference, so a single 0.23M-parameter model can say whether a site carries a modification without naming its type. Across 15 datasets spanning 5mC, 5hmC, 4mC, 6mA and 5hmU in bacterial, phage, plant, mouse, human and synthetic DNA, it reaches a mean AUROC of 0.964, against 0.799 for the best released model, Dorado 4mC+5mC. It needs no unmodified control from the same sample, but a modification type absent from training transfers zero-shot only when another modification of the same base was seen.

RawMod, released by a University of Maryland-led team, stacks the raw nanopore signal of many reads at each site and compares it base by base with the expected signal of the reference, so a single 0.23M-parameter model can say whether a site carries a modification without naming its type. Across 15 datasets spanning 5mC, 5hmC, 4mC, 6mA and 5hmU in bacterial, phage, plant, mouse, human and synthetic DNA, it reaches a mean AUROC of 0.964, against 0.799 for the best released model, Dorado 4mC+5mC. It needs no unmodified control from the same sample, but a modification type absent from training transfers zero-shot only when another modification of the same base was seen.

Raw nanopore current is continuous and has no native token vocabulary, and prior analyses built a separate model per task. Brieno first compresses the signal into discrete tokens with residual vector quantization, then pretrains a Transformer by masked-token prediction on 118 million reads, so frozen or fine-tuned features serve species identification, barcode demultiplexing, basecalling and methylation detection. On held-out runs, fine-tuned Brieno reached a read-level CpG 5mC AUROC of 0.978, above the 0.975 of Dorado's dedicated 5mCG model.

Raw nanopore current is continuous and has no native token vocabulary, and prior analyses built a separate model per task. Brieno first compresses the signal into discrete tokens with residual vector quantization, then pretrains a Transformer by masked-token prediction on 118 million reads, so frozen or fine-tuned features serve species identification, barcode demultiplexing, basecalling and methylation detection. On held-out runs, fine-tuned Brieno reached a read-level CpG 5mC AUROC of 0.978, above the 0.975 of Dorado's dedicated 5mCG model.