dc.contributor.author | van Mourik, Casper | |
dc.contributor.author | Ehsani, Rezvan | |
dc.contributor.author | Drabløs, Finn | |
dc.date.accessioned | 2021-06-04T08:35:22Z | |
dc.date.available | 2021-06-04T08:35:22Z | |
dc.date.created | 2021-05-18T09:46:39Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | BMC Research Notes. 2021, 14, . | en_US |
dc.identifier.issn | 1756-0500 | |
dc.identifier.uri | https://hdl.handle.net/11250/2757639 | |
dc.description.abstract | Objective
Properties of gene products can be described or annotated with Gene Ontology (GO) terms. But for many genes we have limited information about their products, for example with respect to function. This is particularly true for long non-coding RNAs (lncRNAs), where the function in most cases is unknown. However, it has been shown that annotation as described by GO terms to some extent can be predicted by enrichment analysis on properties of co-expressed genes.
Results
GAPGOM integrates two relevant algorithms, lncRNA2GOA and TopoICSim, into a user-friendly R package. Here lncRNA2GOA does annotation prediction by co-expression, whereas TopoICSim estimates similarity between GO graphs, which can be used for benchmarking of prediction performance, but also for comparison of GO graphs in general. The package provides an improved implementation of the original tools, with substantial improvements in performance and documentation, unified interfaces, and additional features. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | BioMed Central, Springer Nature | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | GAPGOM—an R package for gene annotation prediction using GO metrics | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.volume | 14 | en_US |
dc.source.journal | BMC Research Notes | en_US |
dc.identifier.doi | 10.1186/s13104-021-05580-1 | |
dc.identifier.cristin | 1910392 | |
dc.description.localcode | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | en_US |
dc.source.articlenumber | 162 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |