{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T18:42:31Z","timestamp":1706812951905},"reference-count":14,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,4,16]],"date-time":"2016-04-16T00:00:00Z","timestamp":1460764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2016,4,16]],"date-time":"2016-04-16T00:00:00Z","timestamp":1460764800000},"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>Previously, we described ROVER, a DNA variant caller which identifies genetic variants from PCR-targeted massively parallel sequencing (MPS) datasets generated by the Hi-Plex protocol. ROVER permits stringent filtering of sequencing chemistry-induced errors by requiring reported variants to appear in both reads of overlapping pairs above certain thresholds of occurrence. ROVER was developed in tandem with Hi-Plex and has been used successfully to screen for genetic mutations in the breast cancer predisposition gene <jats:italic>PALB2<\/jats:italic>.<\/jats:p>\n                <jats:p>ROVER is applied to MPS data in BAM format and, therefore, relies on sequence reads being mapped to a reference genome. In this paper, we describe an improvement to ROVER, called UNDR ROVER (Unmapped primer-Directed ROVER), which accepts MPS data in FASTQ format, avoiding the need for a computationally expensive mapping stage. It does so by taking advantage of the location-specific nature of PCR-targeted MPS data.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The UNDR ROVER algorithm achieves the same stringent variant calling as its predecessor with a significant runtime performance improvement. In one indicative sequencing experiment, UNDR ROVER (in its fastest mode) required 8-fold less sequential computation time than the ROVER pipeline and 13-fold less sequential computation time than a variant calling pipeline based on the popular GATK tool.<\/jats:p>\n                <jats:p>UNDR ROVER is implemented in Python and runs on all popular POSIX-like operating systems (Linux, OS X). It requires as input a tab-delimited format file containing primer sequence information, a FASTA format file containing the reference genome sequence, and paired FASTQ files containing sequence reads. Primer sequences at the 5\u2032 end of reads associate read-pairs with their targeted amplicon and, thus, their expected corresponding coordinates in the reference genome. The primer-intervening sequence of each read is compared against the reference sequence from the same location and variants are identified using the same algorithm as ROVER. Specifically, for a variant to be \u2018called\u2019 it must appear at the same location in both of the overlapping reads above user-defined thresholds of minimum number of reads and proportion of reads.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>UNDR ROVER provides the same rapid and accurate genetic variant calling as its predecessor with greatly reduced computational costs.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-016-1014-9","type":"journal-article","created":{"date-parts":[[2016,4,16]],"date-time":"2016-04-16T00:53:47Z","timestamp":1460768027000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["UNDR ROVER - a fast and accurate variant caller for targeted DNA sequencing"],"prefix":"10.1186","volume":"17","author":[{"given":"Daniel J.","family":"Park","sequence":"first","affiliation":[]},{"given":"Roger","family":"Li","sequence":"additional","affiliation":[]},{"given":"Edmund","family":"Lau","sequence":"additional","affiliation":[]},{"given":"Peter","family":"Georgeson","sequence":"additional","affiliation":[]},{"given":"T\u00fa","family":"Nguyen-Dumont","sequence":"additional","affiliation":[]},{"given":"Bernard J.","family":"Pope","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,4,16]]},"reference":[{"issue":"1","key":"1014_CR1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.2144\/000114247","volume":"58","author":"T Nguyen-Dumont","year":"2014","unstructured":"Nguyen-Dumont T, Hammet F, Mahmoodi M, Pope BJ, Giles GG, Hopper GG, Southey MC, Park DJ. 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