Deep sequencing-based transcriptome analysis of the oil-bearing plant Physic Nut (Jatropha curcas L.) under cold stress
Deep sequencing-based transcriptome analysis of the oil-bearing plant Physic Nut (Jatropha curcas L.) under cold stress
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基于深度测序的冷胁迫下油料植物麻疯树转录组分析
DOI:
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发表时间:
2014-05
期刊:
影响因子:
--
通讯作者:
Ming Gong
中科院分区:
文献类型:
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作者:
Haibo Wang;Zhurong Zou;Shasha Wang;Ming Gong
Nowadays Jatropha curcas L. has gained an increased attention in scientific and commercial fields as an important renewable bioenergy plant, aiming to prevent the possible energy crisis of fossil fuels. However, the studies on cold resistance of this biofuel shrub are still scarce, giving limited information for its genetic improvement and other biotechnological applications. In this work, the newly developed Illumina Hiseq™ 2000 RNA-seq, which is a deep high-throughput sequencing approach, preferentially was used for cold-resistance related transcriptome analysis of J. curcas. From the sequencing results, the total length of non-redundant sequences obtained was 4,960,092,780 bp, consisting of 106,749 contigs and 45,251 unigenes assembled by clean data. A total of 35,791 unigenes (79.09%) can be annotated to numerous databases (Nr, Swiss-Prot, GO, COG, KEGG) for functional classification. The 33,361 and 912 complete or partial CDSs are deduced by database alignment and ESTscan prediction, respectively. Among these unigenes, 27,293 can be categorized into 61 functional groups of GO, 11,887 of COG-annotated putative proteins were classified functionally into at least 25 molecular families, 18,787 were possibly involved in approximately 128 known metabolic or signaling pathways in KEGG. This study provided a comprehensive cold-resistance transcriptome analysis of J. curcas with remarkably more number of EST sequences than all previous relevant deposits in public databases. The results allowed us to decipher the key genes related to those coding for cold tolerance in this sequence library.
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影响因子:
5.8
作者:
Tong, Mark Y.;Cassa, Christopher A.;Kohane, Isaac S.
通讯作者:
Kohane, Isaac S.
DOI:
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发表时间:
2005
期刊:
--
影响因子:
--
作者:
Ana Conesa;Stefan Götz;Juan Miguel García-Gómez;Javier Terol;Manuel Talón;Montserrat Robles
通讯作者:
Ana Conesa;Stefan Götz;Juan Miguel García-Gómez;Javier Terol;Manuel Talón;Montserrat Robles
影响因子:
5.2
作者:
Tang, Lin;Wu, Jun;Zhang, Fu-Li;Chen, Fang;Xu, Ying;Jiang, Lu-Ding;Jia, Yong-Rong;Wang, Ying-Chun;Wang, Sheng-Hua;Gao, Shun;Niu, Bei
通讯作者:
Niu, Bei
影响因子:
4.4
作者:
Natarajan P;Parani M
通讯作者:
Parani M
影响因子:
5.1
作者:
Mingjuan Tang;Jingwen Sun;Yun Liu;Fan Chen;S. Shen
通讯作者:
Mingjuan Tang;Jingwen Sun;Yun Liu;Fan Chen;S. Shen