{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T11:24:44Z","timestamp":1777893884813,"version":"3.51.4"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2016,10,28]],"date-time":"2016-10-28T00:00:00Z","timestamp":1477612800000},"content-version":"vor","delay-in-days":139,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Motivation: The alignment of sequencing reads to a transcriptome is a common and important step in many RNA-seq analysis tasks. When aligning RNA-seq reads directly to a transcriptome (as is common in the de novo setting or when a trusted reference annotation is available), care must be taken to report the potentially large number of multi-mapping locations per read. This can pose a substantial computational burden for existing aligners, and can considerably slow downstream analysis.<\/jats:p>\n                  <jats:p>Results: We introduce a novel concept, quasi-mapping, and an efficient algorithm implementing this approach for mapping sequencing reads to a transcriptome. By attempting only to report the potential loci of origin of a sequencing read, and not the base-to-base alignment by which it derives from the reference, RapMap\u2014our tool implementing quasi-mapping\u2014is capable of mapping sequencing reads to a target transcriptome substantially faster than existing alignment tools. The algorithm we use to implement quasi-mapping uses several efficient data structures and takes advantage of the special structure of shared sequence prevalent in transcriptomes to rapidly provide highly-accurate mapping information. We demonstrate how quasi-mapping can be successfully applied to the problems of transcript-level quantification from RNA-seq reads and the clustering of contigs from de novo assembled transcriptomes into biologically meaningful groups.<\/jats:p>\n                  <jats:p>Availability and implementation: RapMap is implemented in C\u2009++11 and is available as open-source software, under GPL v3, at https:\/\/github.com\/COMBINE-lab\/RapMap .<\/jats:p>\n                  <jats:p>Contact: \u00a0rob.patro@cs.stonybrook.edu<\/jats:p>\n                  <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btw277","type":"journal-article","created":{"date-parts":[[2016,6,15]],"date-time":"2016-06-15T11:43:52Z","timestamp":1465991032000},"page":"i192-i200","source":"Crossref","is-referenced-by-count":113,"title":["RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes"],"prefix":"10.1093","volume":"32","author":[{"given":"Avi","family":"Srivastava","sequence":"first","affiliation":[{"name":"Department of Computer Science, Stony Brook University Stony Brook, New York, NY 11794-2424, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hirak","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Stony Brook University Stony Brook, New York, NY 11794-2424, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nitish","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Stony Brook University Stony Brook, New York, NY 11794-2424, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rob","family":"Patro","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Stony Brook University Stony Brook, New York, NY 11794-2424, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2016,6,11]]},"reference":[{"key":"2023020112320560100_btw277-B1","first-page":"525","article-title":"Near-optimal probabilistic RNA-seq quantification","volume-title":"Nature Biotech","author":"Bray","year":"2016"},{"key":"2023020112320560100_btw277-B2","doi-asserted-by":"crossref","first-page":"e108095.","DOI":"10.1371\/journal.pone.0108095","article-title":"High-resolution transcriptome analysis with long-read RNA sequencing","volume":"9","author":"Cho","year":"2014","journal-title":"PLoS ONE"},{"key":"2023020112320560100_btw277-B3","doi-asserted-by":"crossref","first-page":"D662","DOI":"10.1093\/nar\/gku1010","article-title":"Ensembl 2015","volume":"43","author":"Cunningham","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023020112320560100_btw277-B4","first-page":"410.","article-title":"Corset: enabling differential gene expression analysis for de novo assembled transcriptomes","volume":"15","author":"Davidson","year":"2014","journal-title":"Genome Biol"},{"key":"2023020112320560100_btw277-B5","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1093\/bioinformatics\/bts635","article-title":"Star: ultrafast universal RNA-seq aligner","volume":"29","author":"Dobin","year":"2013","journal-title":"Bioinformatics"},{"key":"2023020112320560100_btw277-B6","doi-asserted-by":"crossref","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","article-title":"Cd-hit: accelerated for clustering the next-generation sequencing data","volume":"28","author":"Fu","year":"2012","journal-title":"Bioinformatics"},{"key":"2023020112320560100_btw277-B7","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/S1097-2765(04)00239-4","article-title":"Elongator interactions with nascent mrna revealed by RNA immunoprecipitation","volume":"14","author":"Gilbert","year":"2004","journal-title":"Mol. 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