OMGene: mutual improvement of gene models through optimisation of evolutionary conservation

• Published in: BMC genomics Vol. 19; no. 1; p. 307
• Main Authors: Dunne, Michael P, Kelly, Steven
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27.04.2018; BioMed Central; BMC
• Subjects: Algorithms; Amino Acid Sequence; Analysis; Animals; Annotation errors; Biological Evolution; Computational Biology; Databases, Genetic; Fungi; Gene Expression Profiling; Gene model; Genome; Genome annotation; Genomics; Mammals; Models, Genetic; Molecular Sequence Annotation; Orthogroups; Orthology; Plants; RNA sequencing; Sequence Alignment; Sequence Analysis, RNA - methods; Software; Transcriptome; Genome annotation; Orthogroups; Orthology; Gene model; Annotation errors
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-018-4704-z

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, frequently disagree on the model for a given predicted gene, with some or all of the variant gene models often failing to match the biologically observed structure. Many prediction methods can be bolstered by using experimental data such as RNA-seq. 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. Orthology between genes provides evolutionary evidence to guide the construction of gene models. OMGene (Optimise My Gene) aims to improve gene model accuracy in the absence of experimental data by optimising the consistency of multiple sequence alignments of orthologous genes from multiple species. Using RNA-seq data sets from plants, mammals, 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. We show that significant improvements in the accuracy of gene model annotations can be made, both in established and in de novo annotated genomes, by leveraging information from multiple species.