SWARM resolves nanopore signal interference between RNA modification types and reveals splicing-shaped pseudouridylation.

SWARM: AI framework resolving signal interference between RNA modification types on nanopore (m6A, pseudouridine, m5C)

This bioRxiv preprint presents SWARM, an AI framework for simultaneous detection of multiple RNA modification types (m6A, pseudouridine, m5C) by direct RNA nanopore sequencing. The central challenge is resolving electrical signal interference between different modification types on the same read. SWARM introduces a crosstalk-aware training strategy that drastically reduces false positives and reveals new splicing-shaped pseudouridylation profiles.

Synthesis written by Geno'X. For the full original abstract, please refer to the source publication.

Direct RNA nanopore sequencing promises to decode the epitranscriptome in a single read, but false positive rates from cross-modification interference limited its practical utility. SWARM resolves this methodological bottleneck and opens the door to diagnostic epitranscriptomic applications.

Clinical impact: 2/3 · Evidence strength: 3/3 · Novelty: 2/2 · Sample size: 1/1 · Publication status: 0/1 → Total: 8/10