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De novo transcriptome assembly of Vitis flexuosa grapevines inoculated with Elsinoe ampelina

Published online by Cambridge University Press:  16 July 2014

Soon Young Ahn
Affiliation:
Department of Horticulture and Life Science, Yeungnam University, Gyeongsan 712-749, Republic of Korea
Seon Ae Kim
Affiliation:
Department of Horticulture and Life Science, Yeungnam University, Gyeongsan 712-749, Republic of Korea
Sung Hwan Jo
Affiliation:
SEEDERS Inc., Daejeon 302-735, Republic of Korea
Hae Keun Yun*
Affiliation:
Department of Horticulture and Life Science, Yeungnam University, Gyeongsan 712-749, Republic of Korea
*
* Corresponding author. E-mail: haekeun@ynu.ac.kr
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Abstract

In this study, the transcriptome of Vitis flexuosa leaves inoculated with Elsinoe ampelina was analysed to identify useful genes and elucidate their function and differential expression patterns through assembly and annotation gene ontology of data from sequencing short reads on the Illumina platform. We assembled ~121 million high-quality trimmed reads using Velvet and Oases with optimal parameters into a non-redundant set of 70,899 transcripts ( ≥ 200 bp in length). The transcripts exhibited an average length of 1138 bp and a N50 length of 1695 bp, with the largest contig length being 9623 bp. Functional categorization revealed the conservation of genes involved in various molecular functions, including protein binding (21.1%) and oxidoreductase activity (11.7%), in V. flexuosa. The V. flexuosa transcript set generated in this study will serve as a resource for gene discovery and development of functional molecular markers.

Figure 0

Fig. 1 Flowchart of transcriptome analysis by the RNA-Seq method.

Figure 1

Fig. 2 Functional categories of highly up-regulated differentially expressed genes with putative functions assigned through gene ontology annotation at level 2 from Elsinoe ampelina-inoculated Vitis flexuosa grapevines. (a) Molecular function, (b) biological process and (c) cellular component. Assignments were based on the data available in TIGR Gene Indices Clustering Tools.