High-throughput sequencing data and the impact of plant gene annotation quality. (24th December 2018)

• Record Type: Journal Article
• Authors: Vaattovaara, Aleksia; Leppälä, Johanna; Salojärvi, Jarkko; Wrzaczek, Michael
• Is Part Of: Journal of experimental botany. Volume 70:Number 4(2019)
• Page Start: 1069
• Page End: 1076
• Publication Date: 2018-12-24
• Subjects: Gene families -- genome annotation -- GWAS -- high-throughput sequencing -- phylogeny -- translational research; Botany -- Periodicals; Botany, Experimental -- Periodicals; Plant physiology -- Periodicals; 580
• DOI: 10.1093/jxb/ery434
• Languages: English
• ISSNs: 0022-0957

Abstract

Abstract: The use of draft genomes of different species and re-sequencing of accessions and populations are now common tools for plant biology research. The de novo assembled draft genomes make it possible to identify pivotal divergence points in the plant lineage and provide an opportunity to investigate the genomic basis and timing of biological innovations by inferring orthologs between species. Furthermore, re-sequencing facilitates the mapping and subsequent molecular characterization of causative loci for traits, such as those for plant stress tolerance and development. In both cases high-quality gene annotation—the identification of protein-coding regions, gene promoters, and 5′- and 3′-untranslated regions—is critical for investigation of gene function. Annotations are constantly improving but automated gene annotations still require manual curation and experimental validation. This is particularly important for genes with large introns, genes located in regions rich with transposable elements or repeats, large gene families, and segmentally duplicated genes. In this opinion paper, we highlight the impact of annotation quality on evolutionary analyses, genome-wide association studies, and the identification of orthologous genes in plants. Furthermore, we predict that incorporating accurate information from manual curation into databases will dramatically improve the performance of automated gene predictors.