Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105366
Title: High-throughput sequencing data and the impact of plant gene annotation quality
Authors: Vaattovaara, Aleksia
Leppälä, Johanna
Salojärvi, Jarkko
Wrzaczek, Michael
Keywords: Gene Families
Science::Biological sciences
Genome Annotation
Issue Date: 2018
Source: Vaattovaara, A., Leppälä, J., Salojärvi, J., & Wrzaczek, M. (2019). High-throughput sequencing data and the impact of plant gene annotation quality. Journal of Experimental Botany, 70(4), 1069-1076. doi:10.1093/jxb/ery434
Series/Report no.: Journal of Experimental Botany
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.
URI: https://hdl.handle.net/10356/105366
http://hdl.handle.net/10220/49526
ISSN: 0022-0957
DOI: 10.1093/jxb/ery434
Rights: © 2018 The Author(s). Published by Oxford University Press on behalf of the Society for Experimental Biology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SBS Journal Articles

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