{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T01:16:04Z","timestamp":1781140564235,"version":"3.54.1"},"reference-count":13,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,5,10]],"date-time":"2016-05-10T00:00:00Z","timestamp":1462838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2016,5,10]],"date-time":"2016-05-10T00:00:00Z","timestamp":1462838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Trimming of adapter sequences from short read data is a common preprocessing step during NGS data analysis. When performing paired-end sequencing, the overlap between forward and reverse read can be used to identify excess adapter sequences. This is exploited by several previously published adapter trimming tools. However, our evaluation on amplicon-based data shows that most of the current tools are not able to remove all adapter sequences and that adapter contamination may even lead to spurious variant calls.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Here we present SeqPurge (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/imgag\/ngs-bits\">https:\/\/github.com\/imgag\/ngs-bits<\/jats:ext-link>), a highly-sensitive adapter trimmer that uses a probabilistic approach to detect the overlap between forward and reverse reads of Illumina sequencing data. SeqPurge can detect very short adapter sequences, even if only one base long. Compared to other adapter trimmers specifically designed for paired-end data, we found that SeqPurge achieves a higher sensitivity. The number of remaining adapter bases after trimming is reduced by up to 90\u00a0%, depending on the compared tool. In simulations with different error rates, we found that SeqPurge is also the most error-tolerant adapter trimmer in the comparison.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>SeqPurge achieves a very high sensitivity and a high error-tolerance, combined with a specificity and runtime that are comparable to other state-of-the-art adapter trimmers. The very good adapter trimming performance, complemented with additional features such as quality-based trimming and basic quality control, makes SeqPurge an excellent choice for the pre-processing of paired-end NGS data.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-016-1069-7","type":"journal-article","created":{"date-parts":[[2016,5,10]],"date-time":"2016-05-10T01:25:53Z","timestamp":1462843553000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":146,"title":["SeqPurge: highly-sensitive adapter trimming for paired-end NGS data"],"prefix":"10.1186","volume":"17","author":[{"given":"Marc","family":"Sturm","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Schroeder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Bauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2016,5,10]]},"reference":[{"issue":"15","key":"1069_CR1","doi-asserted-by":"publisher","first-page":"2114","DOI":"10.1093\/bioinformatics\/btu170","volume":"30","author":"AM Bolger","year":"2014","unstructured":"Bolger AM, et al. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114\u201320.","journal-title":"Bioinformatics"},{"key":"1069_CR2","doi-asserted-by":"publisher","first-page":"895","DOI":"10.3390\/biology1030895","volume":"1","author":"M Dodt","year":"2012","unstructured":"Dodt M, et al. FLEXBAR - Flexible Barcode and Adapter Processing for Next-Generation Sequencing Platforms. Biology. 2012;1:895\u2013905.","journal-title":"Biology"},{"key":"1069_CR3","unstructured":"Garrison E, et al. Haplotype-based variant detection from short-read sequencing. 2012. http:\/\/arxiv.org\/abs\/1207.3907. Accessed 18 Jan 2016."},{"issue":"12","key":"1069_CR4","doi-asserted-by":"publisher","first-page":"e85024","DOI":"10.1371\/journal.pone.0085024","volume":"8","author":"FM Giorgi","year":"2013","unstructured":"Giorgi FM, et al. An Extensive Evaluation of Read Trimming Effects on Illumina NGS Data Analysis. 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BMC Bioinformatics. 2015;16:S2.","journal-title":"BMC Bioinformatics"},{"key":"1069_CR8","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1186\/1756-0500-5-337","volume":"5","author":"S Lindgreen","year":"2012","unstructured":"Lindgreen S. AdapterRemoval: easy cleaning of next-generation sequencing reads. BMC Res Notes. 2012;5:337.","journal-title":"BMC Res Notes"},{"key":"1069_CR9","doi-asserted-by":"publisher","first-page":"936","DOI":"10.1101\/gr.111120.110","volume":"21","author":"G Lunter","year":"2011","unstructured":"Lunter G, et al. Stampy: A statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. 2011;21:936\u20139.","journal-title":"Genome Res"},{"key":"1069_CR10","doi-asserted-by":"publisher","first-page":"10","DOI":"10.14806\/ej.17.1.200","volume":"17","author":"M Martin","year":"2011","unstructured":"Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. 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