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Background: The accurate determination of the genomic coordinates for a given gene (its gene model) is of vital importance to the utility of its annotation, and the accuracy of bioinformatic analyses derived from it. Currently-available methods of computational gene prediction, while on the whole successful, often disagree on the model for a given predicted gene, with some or all of the variant gene models failing to match the biologically observed structure. Many prediction methods can be bolstered by using experimental data such as RNA-seq and mass spectrometry. However, these resources are not always available, and rarely give a comprehensive portrait of an organism's transcriptome due to temporal and tissue-specific expression profiles. Results: Orthology between genes provides evolutionary evidence to guide the construction of gene models. OMGene (Optimise My Gene) aims to optimise gene models in the absence of experimental data by optimising the derived amino acid alignments for gene models within orthogroups. Using RNA-seq data sets from plants and fungi, considering intron/exon junction representation and exon coverage, and assessing the intra-orthogroup consistency of subcellular localisation predictions, we demonstrate the utility of OMGene for improving gene models in annotated genomes. Conclusions: We show that significant improvements in the accuracy of gene model annotations can be made in both established and de novo annotated genomes by leveraging information from multiple species.

Original publication

DOI

10.1101/212530

Type

Journal article

Journal

BMC Genomics

Publisher

BioMed Central

Publication Date

27/04/2018