AUTHOR=Sheng Minghao , She Jiajie , Xu Wenying , Hong Yan , Su Zhen , Zhang Xiaodong 

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

JOURNAL=Frontiers in Genetics

VOLUME=11

YEAR=2020

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.570138

DOI=10.3389/fgene.2020.570138

ISSN=1664-8021

ABSTRACT=<p>The herbaceous peony (<italic>Paeonia lactiflora</italic> 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 <italic>Radix paeoniae Alba</italic> and <italic>Radix paeoniae Rubra</italic>), and it has become economically valued for its oilseed in recent years; like other <italic>Paeonia</italic> species, it has been identified as a novel resource for the α-linolenic acid used in seed oil production. However, its genome has not yet been sequenced, and little transcriptome data on <italic>Paeonia lactiflora</italic> are available. To obtain a comprehensive transcriptome for <italic>Paeonia lactiflora</italic>, RNAs from 10 tissues of the <italic>Paeonia lactiflora</italic> Pall. cv Shaoyou17C were used for <italic>de novo</italic> 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 <italic>Paeonia lactiflora</italic> cultivars. Finally, we constructed the online database HpeNet (<ext-link ext-link-type="uri" xlink:href="http://bioinformatics.cau.edu.cn/HpeNet" xmlns:xlink="http://www.w3.org/1999/xlink">http://bioinformatics.cau.edu.cn/HpeNet</ext-link>) 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 <italic>Paeonia lactiflora</italic> more conveniently. We hope that <italic>de novo</italic> 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.</p>