首页> 美国卫生研究院文献>Scientific Data >De novo transcriptome assembly databases for the butterfly orchid Phalaenopsis equestris
【2h】

De novo transcriptome assembly databases for the butterfly orchid Phalaenopsis equestris

机译:蝴蝶兰花蝴蝶兰的从头转录组装配数据库

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。
获取外文期刊封面目录资料

摘要

Orchids are renowned for their spectacular flowers and ecological adaptations. After the sequencing of the genome of the tropical epiphytic orchid Phalaenopsis equestris, we combined Illumina HiSeq2000 for RNA-Seq and Trinity for de novo assembly to characterize the transcriptomes for 11 diverse P. equestris tissues representing the root, stem, leaf, flower buds, column, lip, petal, sepal and three developmental stages of seeds. Our aims were to contribute to a better understanding of the molecular mechanisms driving the analysed tissue characteristics and to enrich the available data for P. equestris. Here, we present three databases. The first dataset is the RNA-Seq raw reads, which can be used to execute new experiments with different analysis approaches. The other two datasets allow different types of searches for candidate homologues. The second dataset includes the sets of assembled unigenes and predicted coding sequences and proteins, enabling a sequence-based search. The third dataset consists of the annotation results of the aligned unigenes versus the Nonredundant (Nr) protein database, Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Clusters of Orthologous Groups (COG) databases with low e-values, enabling a name-based search.
机译:兰花因其壮观的花朵和生态适应而闻名。对热带附生兰花蝴蝶兰花植物的基因组进行测序后,我们将Illumina HiSeq2000用于RNA-Seq,将Trinity进行从头组装,以表征11种不同马鞭草组织的转录组,这些组织分别代表根,茎,叶,花蕾,种子的圆柱,唇,花瓣,萼片和三个发育阶段。我们的目的是有助于更好地理解驱动所分析的组织特征的分子机制,并丰富可用于P. equestris的数据。在这里,我们介绍了三个数据库。第一个数据集是RNA-Seq原始读数,可用于使用不同的分析方法执行新的实验。其他两个数据集允许对候选同源物进行不同类型的搜索。第二个数据集包括组装的单基因,预测的编码序列和蛋白质的集合,从而可以进行基于序列的搜索。第三个数据集由对齐的单基因与非冗余(Nr)蛋白质数据库,京都市基因与基因组百科全书(KEGG)和直系同源族群(COG)数据库的注释结果组成,这些数据库的e值较低,从而可以基于名称搜索。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号