HpeNet: Co-expression Network Database for de novo Transcriptome Assembly of Paeonia lactiflora Pall

文献类型： 外文期刊

第一作者： Sheng, Minghao

作者： Sheng, Minghao;Hong, Yan;Zhang, Xiaodong;Sheng, Minghao;She, Jiajie;Xu, Wenying;Su, Zhen

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关键词： Paeonia lactiflora Pall; de novo assembly; functional annotation; co-expression network; database

期刊名称：FRONTIERS IN GENETICS （ 影响因子：4.599； 五年影响因子：4.888 ）

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年卷期： 2020 年 11 卷

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收录情况： SCI

摘要： The herbaceous peony (Paeonia lactiflora Pall.) is a well-known ornamental flowering and pharmaceutical plant found in China. Its high medicinal value has long been recognized by traditional Chinese medicine (as Radix paeoniae Alba and Radix paeoniae Rubra), and it has become economically valued for its oilseed in recent years; like other Paeonia species, it has been identified as a novel resource for the alpha-linolenic acid used in seed oil production. However, its genome has not yet been sequenced, and little transcriptome data on Paeonia lactiflora are available. To obtain a comprehensive transcriptome for Paeonia lactiflora, RNAs from 10 tissues of the Paeonia lactiflora Pall. cv Shaoyou17C were used for de novo assembly, and 416,062 unigenes were obtained. Using a homology search, it was found that 236,222 (approximately 57%) unigenes had at least one BLAST hit in one or more public data resources. The construction of co-expression networks is a feasible means for improving unigene annotation. Using in-house transcriptome data, we obtained a co-expression network covering 95.13% of the unigenes. Then we integrated co-expression network analyses and lipid-related pathway genes to study lipid metabolism in Paeonia lactiflora cultivars. Finally, we constructed the online database HpeNet () to integrate transcriptome data, gene information, the co-expression network, and so forth. The database can also be searched for gene details, gene functions, orthologous matches, and other data. Our online database may help the research community identify functional genes and perform research on Paeonia lactiflora more conveniently. We hope that de novo transcriptome assembly, combined with co-expression networks, can provide a feasible means to predict the gene function of species that do not have a reference genome.

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