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Co-expression Gene Network Analysis and Functional Module Identification in Bamboo Growth and Development

机译:竹生长发育中共表达基因网络分析与功能模块鉴定

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摘要

Bamboo is one of the fastest-growing non-timber forest plants. Moso bamboo (Phyllostachys edulis) is the most economically valuable bamboo in Asia, especially in China. With the release of the whole-genome sequence of moso bamboo, there are increasing demands for refined annotation of bamboo genes. Recently, large amounts of bamboo transcriptome data have become available, including data on the multiple growth stages of tissues. It is now feasible for us to construct co-expression networks to improve bamboo gene annotation and reveal the relationships between gene expression and growth traits. We integrated the genome sequence of moso bamboo and 78 transcriptome data sets to build genome-wide global and conditional co-expression networks. We overlaid the gene expression results onto the network with multiple dimensions (different development stages). Through combining the co-expression network, module classification and function enrichment tools, we identified 1,896 functional modules related to bamboo development, which covered functions such as photosynthesis, hormone biosynthesis, signal transduction, and secondary cell wall biosynthesis. Furthermore, an online database () was built for searching the moso bamboo co-expression network and module enrichment analysis. Our database also includes cis-element analysis, gene set enrichment analysis, and other tools. In summary, we integrated public and in-house bamboo transcriptome data sets and carried out co-expression network analysis and functional module identification. Through data mining, we have yielded some novel insights into the regulation of growth and development. Our established online database might be convenient for the bamboo research community to identify functional genes or modules with important traits.
机译:竹子是增长最快的非木材森林植物之一。毛竹(Phyllostachys edulis)是亚洲,尤其是中国最具经济价值的竹子。随着毛竹全基因组序列的发布,对竹基因的精细注释的需求不断增加。近来,可获得大量的竹转录组数据,包括有关组织的多个生长阶段的数据。现在,我们可以构建共表达网络来改善竹基因注释,并揭示基因表达与生长性状之间的关系。我们整合了毛竹的基因组序列和78个转录组数据集,以建立全基因组的全局和条件共表达网络。我们将基因表达结果叠加到具有多个维度(不同的开发阶段)的网络上。通过组合共表达网络,模块分类和功能丰富工具,我们确定了1,896个与竹子发育相关的功能模块,涵盖了光合作用,激素生物合成,信号转导和二次细胞壁生物合成等功能。此外,建立了一个在线数据库(),用于搜索毛竹共表达网络和模块富集分析。我们的数据库还包括顺式元素分析,基因集富集分析和其他工具。总之,我们整合了公共和内部竹转录组数据集,并进行了共表达网络分析和功能模块识别。通过数据挖掘,我们对增长和发展的调节产生了一些新颖的见解。我们建立的在线数据库可能对竹子研究社区方便,以鉴定具有重要特征的功能基因或模块。

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