Long-read transcriptome data for improved gene prediction in Lentinula edodes

Sin-Gi Park; Seung Il Yoo; Dong Sung Ryu; Hyunsung Lee; Yong Ju Ahn; Hojin Ryu; Junsu Ko; Chang Pyo Hong

doi:10.1016/j.dib.2017.09.052

Long-read transcriptome data for improved gene prediction in Lentinula edodes

Data Brief. 2017 Sep 27:15:454-458. doi: 10.1016/j.dib.2017.09.052. eCollection 2017 Dec.

Authors

Sin-Gi Park¹, Seung Il Yoo¹, Dong Sung Ryu¹, Hyunsung Lee¹, Yong Ju Ahn¹, Hojin Ryu², Junsu Ko¹, Chang Pyo Hong¹

Affiliations

¹ Theragen Etex Bio Institute, Suwon 16229, Republic of Korea.
² Department of Biology, Chungbuk National University, Cheongju 28644, Republic of Korea.

Abstract

Lentinula edodes is one of the most popular edible mushrooms in the world and contains useful medicinal components such as lentinan. The whole-genome sequence of L. edodes has been determined with the objective of discovering candidate genes associated with agronomic traits, but experimental verification of gene models with correction of gene prediction errors is lacking. To improve the accuracy of gene prediction, we produced 12.6 Gb of long-read transcriptome data of variable lengths using PacBio single-molecule real-time (SMRT) sequencing and generated 36,946 transcript clusters with an average length of 2.2 kb. Evidence-driven gene prediction on the basis of long- and short-read RNA sequencing data was performed; a total of 16,610 protein-coding genes were predicted with error correction. Of the predicted genes, 42.2% were verified to be covered by full-length transcript clusters. The raw reads have been deposited in the NCBI SRA database under accession number PRJNA396788.

Keywords: GFF, general feature format; Gene model; Gene prediction; Lentinula edodes; PacBio Single-molecule real-time (SMRT) transcriptome sequencing; RNA-Seq, whole transcriptome sequencing.