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Long-read transcriptome data for improved gene prediction in Lentinula edodes.
Park, Sin-Gi; Yoo, Seung Il; Ryu, Dong Sung; Lee, Hyunsung; Ahn, Yong Ju; Ryu, Hojin; Ko, Junsu; Hong, Chang Pyo.
Afiliação
  • Park SG; Theragen Etex Bio Institute, Suwon 16229, Republic of Korea.
  • Yoo SI; Theragen Etex Bio Institute, Suwon 16229, Republic of Korea.
  • Ryu DS; Theragen Etex Bio Institute, Suwon 16229, Republic of Korea.
  • Lee H; Theragen Etex Bio Institute, Suwon 16229, Republic of Korea.
  • Ahn YJ; Theragen Etex Bio Institute, Suwon 16229, Republic of Korea.
  • Ryu H; Department of Biology, Chungbuk National University, Cheongju 28644, Republic of Korea.
  • Ko J; Theragen Etex Bio Institute, Suwon 16229, Republic of Korea.
  • Hong CP; Theragen Etex Bio Institute, Suwon 16229, Republic of Korea.
Data Brief ; 15: 454-458, 2017 Dec.
Article em En | MEDLINE | ID: mdl-29845094
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.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Data Brief Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Data Brief Ano de publicação: 2017 Tipo de documento: Article