GAPGOM—an R package for gene annotation prediction using GO metrics

BMC Research Notes

Finn Drabløs¹  Casper van Mourik²  Rezvan Ehsani³ 

[1] Department of Cancer Research and Molecular Medicine, NTNU-Norwegian University of Science and Technology, 7491, Trondheim, Norway;Department of Cancer Research and Molecular Medicine, NTNU-Norwegian University of Science and Technology, 7491, Trondheim, Norway;Institute for Life Science & Technology, Hanze University of Applied Sciences, 9747 AS, Groningen, The Netherlands;Department of Mathematics, University of Zabol, Zabol, Iran;Department of Bioinformatics, University of Zabol, Zabol, Iran;Department of Informatics, University of Bergen, 5020, Bergen, Norway;

关键词: Gene ontology;    Annotation;    Prediction;    Long non-coding RNAs;   

DOI  :  10.1186/s13104-021-05580-1

来源: Springer

【 摘 要 】

ObjectiveProperties 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.ResultsGAPGOM 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.

【 授权许可】

CC BY