Background Long-read sequencing platforms, particularly Oxford Nanopore Technologies (ONT), have transformed transcriptomics through direct RNA sequencing. However, their higher error rates – dominated by insertions and deletions – demand longer, more robust sequence barcodes than traditional short-read applications. Existing barcode generation tools suffer from exponential complexity, becoming computationally infeasible at lengths above 12 bp and leaving a critical gap for long-read applications. Methods We developed TagGen, a high-performance barcode generator implementing Monte Carlo candidate sampling with greedy diversity selection. TagGen includes an integrated demultiplexer that assigns ONT reads to their source barcodes regardless of the tag position using a kmer voting and banded edit-distance matching pipeline. We benchmarked TagGen using Badread-simulated reads and validated barcode resilience using a literature-based nanopore error model. Results TagGen generates 96 diverse 12 bp barcodes from 100,000 candidates in under 100 milliseconds, outperforming exhaustive enumeration by up to 13,600-fold. TagGen successfully generates barcodes at 14–30 bp lengths where other available tools fail. Noise simulation demonstrates that TagGen-generated 30 bp barcodes (minimum Hamming distance ≥8) maintain 100% correct assignment at 20% total error rate, whereas traditional 10 bp barcodes degrade to 83%. At typical nanopore error rates (10–15%), taggen-generated barcodes ≥14 bp achieve >97% theoretical resolution. When inserted within a read, our systematic benchmark shows that TagGen demultiplexer achieved >90% accuracy with zero wrong-sample assignments (“end” mode) for reads ≥20 bp. Levenshtein edit distance, recommended for ONT data, improved accuracy by 10–27 percentage points over Hamming distance at equivalent parameters. Conclusions TagGen uniquely enables robust barcode design for nanopore and direct RNA sequencing applications, providing researchers with error-tolerant barcodes validated against realistic long-read error profiles, and an integrated anchor-free demultiplexer for flexible read assignment. The software is freely available at https://github.com/Arnaroo/taggen.
Chowdhury et al. (Thu,) studied this question.