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Photolithographic synthesis of high-density barcode arrays achieves library sizes exceeding\n                      <jats:inline-formula>\n                        <jats:tex-math>$$10^5$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      unique sequences but introduces error rates of 10\u201320% 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We benchmarked three computational barcode decoding approaches\u2014Columba (FM-index-based lossless alignment), QUIK (k-mer filtering with GPU acceleration), and RandomBarcodes (trimer-based triage with GPU parallelization)\u2014across simulated and empirical datasets. Simulations spanned barcode lengths of 28\u201336 nt, library sizes of 21,000\u201385,000 barcodes, and error rates of 9\u201332%. Real sequencing data were generated from photolithographically synthesized arrays at three printing density levels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Under medium error rates (~23%), QUIK achieved the highest recall (87\u201389%) while maintaining precision\n                      <jats:inline-formula>\n                        <jats:tex-math>$$&gt;99.5\\%$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      , outperforming RandomBarcodes (recall 56%, precision\n                      <jats:inline-formula>\n                        <jats:tex-math>$$&gt;99.8\\%$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      ) and Columba (recall 35%, precision 98\u2013100%). 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).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      QUIK provides the optimal balance of speed, accuracy, and scalability for high-density spatial transcriptomics applications under realistic synthesis error conditions. Barcode lengths\n                      <jats:inline-formula>\n                        <jats:tex-math>$$\\ge 34$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      nt are recommended for applications requiring\n                      <jats:inline-formula>\n                        <jats:tex-math>$$&gt;75\\%$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      read recovery at\n                      <jats:inline-formula>\n                        <jats:tex-math>$$&gt;99.9\\%$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      precision.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-026-06540-x","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T15:37:45Z","timestamp":1782229065000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Benchmarking DNA barcode decoding strategies under high error rates"],"prefix":"10.1186","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9885-4431","authenticated-orcid":false,"given":"Franco","family":"Poma-Soto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6676-7077","authenticated-orcid":false,"given":"Hanne","family":"Van Droogenbroeck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2249-4391","authenticated-orcid":false,"given":"Brecht","family":"Soulliaert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maya","family":"Giridhar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00fcrgen","family":"Behr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamed","family":"Sabzalipoor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8039-1341","authenticated-orcid":false,"given":"Mark","family":"Somoza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7821-9684","authenticated-orcid":false,"given":"Pieter","family":"Mestdagh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6274-0184","authenticated-orcid":false,"given":"Jo","family":"Vandesompele","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,23]]},"reference":[{"issue":"6","key":"6540_CR1","doi-asserted-by":"publisher","first-page":"5448","DOI":"10.1101\/pdb.prot5448","volume":"2010","author":"M Meyer","year":"2010","unstructured":"Meyer M, Kircher M. 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