Exploiting single-molecule transcript sequencing for eukaryotic gene prediction
Mostra el registre complet Registre parcial de l'ítem
- dc.contributor.author Minoche, André E.ca
- dc.contributor.author Dohm, Juliane C.ca
- dc.contributor.author Schneider, Jessicaca
- dc.contributor.author Holtgräwe, Danielaca
- dc.contributor.author Viehöver, Priscaca
- dc.contributor.author Montfort, Magdaca
- dc.contributor.author Sörensen, Thomas Rosleffca
- dc.contributor.author Weisshaar, Berndca
- dc.contributor.author Himmelbauer, Heinzca
- dc.date.accessioned 2015-11-12T14:10:47Z
- dc.date.available 2015-11-12T14:10:47Z
- dc.date.issued 2015
- dc.description.abstract We develop a method to predict and validate gene models using PacBio single-molecule, real-time (SMRT) cDNA reads. Ninety-eight percent of full-insert SMRT reads span complete open reading frames. Gene model validation using SMRT reads is developed as automated process. Optimized training and prediction settings and mRNA-seq noise reduction of assisting Illumina reads results in increased gene prediction sensitivity and precision. Additionally, we present an improved gene set for sugar beet (Beta vulgaris) and the first genome-wide gene set for spinach (Spinacia oleracea). The workflow and guidelines are a valuable resource to obtain comprehensive gene sets for newly sequenced genomes of non-model eukaryotes.ca
- dc.format.mimetype application/pdfca
- dc.identifier.citation Minoche AE, Dohm JC, Schneider J, Holtgräwe D, Viehöver P, Montfort M et al. Exploiting single-molecule transcript sequencing for eukaryotic gene prediction. Genome biology. 2015;16:184. DOI: 10.1186/s13059-015-0729-7ca
- dc.identifier.doi http://dx.doi.org/10.1186/s13059-015-0729-7
- dc.identifier.issn 1474-7596
- dc.identifier.uri http://hdl.handle.net/10230/25069
- dc.language.iso engca
- dc.publisher BioMed Centralca
- dc.relation.ispartof Genome biology. 2015;16:184
- dc.rights © 2015 Minoche et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.ca
- dc.rights.accessRights info:eu-repo/semantics/openAccessca
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/ca
- dc.subject.keyword Single-molecule real-time sequencing
- dc.subject.keyword Eukaryotic gene prediction
- dc.subject.keyword mRNA-seq
- dc.subject.keyword Caryophyllales
- dc.subject.keyword Sugar beet
- dc.subject.keyword Spinach
- dc.subject.keyword Non-model species
- dc.subject.keyword Genome annotation
- dc.subject.other Genòmicaca
- dc.subject.other RNA missatgerca
- dc.title Exploiting single-molecule transcript sequencing for eukaryotic gene predictionca
- dc.type info:eu-repo/semantics/articleca
- dc.type.version info:eu-repo/semantics/publishedVersionca