Systematic benchmark of demultiplexing workflows for direct RNA sequencing

Direct RNA nanopore sequencing offers the capacity to directly profile native RNAs, enabling detection of diverse epitranscriptomic modifications based on current changes as the RNA passes through a pore. Innovations in chemistry, hardware and bioinformatic tools have increased sequencing capacity and accuracy, creating demand for sequencing multiple samples within individual runs. Two recent solutions enable barcoding and demultiplexing of samples, but independent comparisons of their performance is limited. Here we benchmark WarpDemuX and SeqTagger using orthogonal ground truths derived from species identity and matched IVT spike-ins across four RNA004 flow cells. Both achieve high demultiplexing precision but differ in their trade-offs in precision and retention. SeqTagger provided strong performance at its recommended settings, whilst WarpDemuX performance varied by model, barcode and read quality. IVT-derived score cutoffs allowed dataset-specific calibration and increased WarpDeMux assignment yield, although precision achieved on IVTs was not consistently reproduced in species reads. These results support matched IVTs as internal controls for guiding choice of demultiplexing settings, while demonstrating the need to validate demultiplexing precision in biological RNA populations.