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HEREDITAS ›› 2010, Vol. 32 ›› Issue (11): 1175-1186.doi: 10.3724/SP.J.1005.2010.01175

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Identification of known microRNAs in root and leaf of maize by deep sequencing

CHEN Jie1, 2, LIN Hai-Jian1, PAN Guang-Tang1, ZHANG Zhi-Ming1, ZHANG Biao2, SHEN Ya-Ou1, QIN Cheng1, ZHANG Qian3, ZHAO Mao-Jun4   

  1. 1. Maize Research Institute, Sichuan Agricultural University, Ya’an 625014, China
    2. Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China
    3. Institute of Agricultural Products Processing, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China;
    4. Life and Science Department, Sichuan Agricultural University, Ya’an 625014, China
  • Received:2010-04-24 Revised:2010-06-29 Online:2010-11-20 Published:2010-11-25
  • Contact: PAN Guang-Tang E-mail:pangt1956@yahoo.com.cn

Abstract: microRNA (miRNAs) is a newly identified class of 20~24 nt non-protein-coding and endogenous small RNA, which plays an important role in plant growth, development and response to environmental stresses. Combined with bioinformatic method, the types, abundance, and targets of known miRNAs in root and leaf of maize (Zea mays L.) were analyzed by small RNA deep sequencing technology, which was based on Illumina/Solexa principium. The results indicated that 92 known miRNAs were detected in maize root, which were attributed to 18 miRNA families and their abundance ranged from 1 to 105 943 reads. Synchronously, 86 known miRNAs were detected in maize leaf, which were attributed to 17 miRNA families and their abundance ranged from 1 to 85 973 reads. The target gene prediction showed that 54 putative target genes as these known miRNAs were predicted. Some of them were involved in the following processes, such as tran-scription regulation, substance and energy metabolism, electron transport, stress response, and signal transduction through further function prediction. In conclusion, there were obvious differences in both types and abundance of known miRNAs between root and leaf in maize.

Key words: Maize, microRNA, Deep sequencing, Abundance analysis, Target prediction