Benchmarking DNA barcode decoding strategies under high error rates

DNA barcoding enables multiplexed identification of biomolecules in pooled sequencing experiments, with broad applications including spatial transcriptomics. Photolithographic synthesis of high-density barcode arrays achieves library sizes exceeding [Formula: see text] unique sequences but introduces error rates of 10-20% per nucleotide through substitutions, insertions, and deletions. Classical error-correcting codes cannot scale to such library sizes while maintaining robust error correction under these conditions.

We benchmarked three computational barcode decoding approaches-Columba (FM-index-based lossless alignment), QUIK (k-mer filtering with GPU acceleration), and RandomBarcodes (trimer-based triage with GPU parallelization)-across simulated and empirical datasets. Simulations spanned barcode lengths of 28-36 nt, library sizes of 21,000-85,000 barcodes, and error rates of 9-32%. Real sequencing data were generated from photolithographically synthesized arrays at three printing density levels.

Under medium error rates (~23%), QUIK achieved the highest recall (87-89%) while maintaining precision [Formula: see text], outperforming RandomBarcodes (recall 56%, precision [Formula: see text]) and Columba (recall 35%, precision 98-100%). QUIK demonstrated superior scalability, processing 59,620 reads/second on a single GPU compared to RandomBarcodes (68 reads/second) and Columba (1550 reads/second with 8 CPU threads). Barcode length strongly influenced accuracy: 34-nt barcodes enabled 75% recall at 99.97% precision with QUIK, compared to 60% recall with 32-nt barcodes. On real data from a 42,000-spot subarray with 36-nt barcodes, QUIK managed a 57% assignment rate with perfect precision, versus 52% (Columba, precision 99.96) and 50% (RandomBarcodes, precision 99.82).

QUIK provides the optimal balance of speed, accuracy, and scalability for high-density spatial transcriptomics applications under realistic synthesis error conditions. Barcode lengths [Formula: see text] nt are recommended for applications requiring [Formula: see text] read recovery at [Formula: see text] precision.