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RNA-seq data comparisons of wild soybean genotypes in response to soybean cyst nematode (Heterodera glycines).
Zhang, Hengyou; Song, Bao-Hua.
Affiliation
  • Zhang H; Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
  • Song BH; Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Genom Data ; 14: 36-39, 2017 Dec.
Article in En | MEDLINE | ID: mdl-28856099
ABSTRACT
Soybean [Glycine max (L.) Merr.] is an important crop rich in vegetable protein and oil, and is a staple food for human and animals worldwide. However, soybean plants have been challenged by soybean cyst nematode (SCN, Heterodera glycines), one of the most damaging pests found in soybean fields. Applying SCN-resistant cultivars is the most efficient and environmentally friendly strategy to manage SCN. Currently, soybean breeding and further improvement in soybean agriculture are hindered by severely limited genetic diversity in cultivated soybeans. G. soja is a soybean wild progenitor with much higher levels of genetic diversity compared to cultivated soybeans. In this study, transcriptomes of the resistant and susceptible genotypes of the wild soybean, Glycine soja Sieb & Zucc, were sequenced to examine the genetic basis of SCN resistance. Seedling roots were treated with infective second-stage juveniles (J2s) of the soybean cyst nematode (HG type 2.5.7) for 3, 5, 8 days and pooled for library construction and RNA sequencing. The transcriptome sequencing generated approximately 245 million (M) high quality (Q > 30) raw sequence reads (125 bp in length) for twelve libraries. The raw sequence reads were deposited in NCBI sequence read archive (SRA) database, with the accession numbers SRR5227314-25. Further analysis of this data would be helpful to improve our understanding of the molecular mechanisms of soybean-SCN interaction and facilitate the development of diverse SCN resistance cultivars.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Genom Data Year: 2017 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Genom Data Year: 2017 Document type: Article Affiliation country: United States