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Background <p> 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 <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(10^5\)</EquationSource></InlineEquation> 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. </p> Methods <p>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. </p> Results <p> Under medium error rates (~23%), QUIK achieved the highest recall (87–89%) while maintaining precision <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(&gt;99.5\%\)</EquationSource></InlineEquation>, outperforming RandomBarcodes (recall 56%, precision <InlineEquation ID="IEq3"><EquationSource Format="TEX">\(&gt;99.8\%\)</EquationSource></InlineEquation>) 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). </p> Conclusions <p>QUIK provides the optimal balance of speed, accuracy, and scalability for high-density spatial transcriptomics applications under realistic synthesis error conditions. Barcode lengths <InlineEquation ID="IEq4"><EquationSource Format="TEX">\(\ge 34\)</EquationSource></InlineEquation> nt are recommended for applications requiring <InlineEquation ID="IEq5"><EquationSource Format="TEX">\(&gt;75\%\)</EquationSource></InlineEquation> read recovery at <InlineEquation ID="IEq6"><EquationSource Format="TEX">\(&gt;99.9\%\)</EquationSource></InlineEquation> precision.</p>

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Benchmarking DNA barcode decoding strategies under high error rates

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月23日

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  • Franco Poma-Soto,
  • Hanne Van Droogenbroeck,
  • Brecht Soulliaert,
  • Maya Giridhar,
  • Jürgen Behr,
  • Hamed Sabzalipoor,
  • Mark Somoza,
  • Pieter Mestdagh,
  • Jo Vandesompele

摘要

Background

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 \(10^5\) 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.

Methods

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.

Results

Under medium error rates (~23%), QUIK achieved the highest recall (87–89%) while maintaining precision \(>99.5\%\), outperforming RandomBarcodes (recall 56%, precision \(>99.8\%\)) 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).

Conclusions

QUIK provides the optimal balance of speed, accuracy, and scalability for high-density spatial transcriptomics applications under realistic synthesis error conditions. Barcode lengths \(\ge 34\) nt are recommended for applications requiring \(>75\%\) read recovery at \(>99.9\%\) precision.