4.4 Article

Construction of a Full-Length cDNA Library and Analysis of Expressed Sequence Tags from Inflorescence of Apomictic Sabaigrass (Eulaliopsis binata)

Journal

PLANT MOLECULAR BIOLOGY REPORTER
Volume 30, Issue 1, Pages 46-54

Publisher

SPRINGER
DOI: 10.1007/s11105-011-0316-3

Keywords

Apomixis; cDNA library; ESTs; Eulaliopsis binata; Inflorescence; SSRs

Funding

  1. National Natural Science Foundation of China [30670127]

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Eulaliopsis binata, which is a close relative of cereal crops, was recognized as an important research material owing to its high frequency of apospory and autonomous endosperm formation. However, little information is known about its genomics and regulatory pathway participating in reproductive development. For the first step to understand molecular basis in sabaigrass (E. binata), a SMART complementary DNA library from the inflorescence tissue was constructed and characterized. The titers of original and amplified libraries were 5.53 x 10(6) and 1.49 x 10(10) pfu/ml, respectively. The percentage of recombinants was 96% in the original library. Analysis of sequencing results of 398 out of 437 randomly picked clones showed that 271 (68.1%) expressed sequence tags (ESTs) exhibited significant similarity with known putative functional nucleotide sequences in the GenBank databases, 25 (6.3%) ESTs have significant matches with hypothetical proteins, putative proteins, and unknown proteins, and the other 25.6% ESTs had no significant similarity to sequences in the public databases. Based on molecular function of GO annotation, the four most abundant terms are nucleotide binding, hydrolase activity, ion binding, and protein binding, and these genes were involved in 61 different pathways using the Kyoto Encyclopedia of Genes and Genomes pathway analysis. Besides, simple sequence repeats detection in 398 ESTs was carried out, and several genes were chosen to perform expression analysis. This report represents a first step in expanding molecular-genetic analyses in E. binata and can be used to optimally mine useful information from a relatively small data set.

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