{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T15:45:39Z","timestamp":1782661539793,"version":"3.54.5"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000},"content-version":"vor","delay-in-days":24,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003453","name":"Guangdong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2025A1515011628"],"award-info":[{"award-number":["2025A1515011628"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Long-read sequencing (LRS) platforms offer extended read lengths but present computational challenges due to high error rates and frequent insertion-deletion (indel) artifacts. While sample multiplexing is essential for cost-efficiency, existing demultiplexing solutions face a dichotomy: vendor-provided tools (e.g., Dorado) often lack the structural flexibility required for highly non-canonical designs, while open-source tools (e.g., Cutadapt) often lack the speed or algorithmic robustness to handle custom, high-complexity barcode designs. Here, we present ReadChop, a high-performance demultiplexer implemented in Rust. ReadChop leverages Myers\u2019 bit-parallel algorithm to efficiently model indel-rich error profiles and employs a streaming architecture to ensure low memory footprint. Benchmarking demonstrates that ReadChop achieves classification precision exceeding 99.99% on both simulated datasets\u2014even under ultra-high multiplexing conditions (e.g., 13\u2009824-plex)\u2014and empirical SARS-CoV-2 amplicons. Furthermore, it excels in filtering in silico chimeras (0.1% miss rate) and exhibits linear computational scalability on ultra-long templates (up to 100\u2009kb). Crucially, it significantly accelerates execution speeds\u2014being &amp;gt;6 times faster than Dorado, &amp;gt;2 times faster than Nanoplexer, and &amp;gt;30 times faster than Cutadapt\u2014with memory usage consistently below 200 MB. ReadChop provides a flexible, robust solution for processing massive LRS datasets with non-canonical experimental designs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and Implementation<\/jats:title>\n                    <jats:p>Source code and documentation are freely available under the MIT license at https:\/\/github.com\/cherryamme\/ReadChop.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag339","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T11:50:41Z","timestamp":1782388241000},"source":"Crossref","is-referenced-by-count":0,"title":["ReadChop: a high-performance demultiplexer for long-read sequencing data"],"prefix":"10.1093","volume":"42","author":[{"given":"Chen","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Biochemistry, Key Laboratory of Gene Engineering of the Ministry of Education, School of Life Sciences, Sun Yat-sen University , Guangzhou, 510275,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3227-5215","authenticated-orcid":false,"given":"Yuanyan","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, Key Laboratory of Gene Engineering of the Ministry of Education, School of Life Sciences, Sun Yat-sen University , Guangzhou, 510275,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,6,25]]},"reference":[{"key":"2026062811263572400_btag339-B1","doi-asserted-by":"publisher","first-page":"btae554","DOI":"10.1093\/bioinformatics\/btae554","article-title":"Mgikit: demultiplexing toolkit for MGI fastq files","volume":"40","author":"Al Bkhetan","year":"2024","journal-title":"Bioinformatics"},{"key":"2026062811263572400_btag339-B100","volume-title":"Nat Commun","author":"Bull RA, Adikari TN, Ferguson 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