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Journal of Integrative Agriculture  2021, Vol. 20 Issue (1): 191-200    DOI: 10.1016/S2095-3119(20)63231-2
Special Issue: 昆虫合辑Plant Protection—Entomolgy
Plant Protection Advanced Online Publication | Current Issue | Archive | Adv Search |
InvasionDB: A genome and gene database of invasive alien species
HUANG Cong1, 2*, LANG Kun3*, QIAN Wan-qiang2*, WANG Shu-ping4, CAO Xiao-mei5, HE Rui6, ZHAN An-ran3, CHEN Meng-yao3, YANG Nian-wan1, LI Fei3 
1 Key Laboratory for Biology of Plant Disease and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, P.R.China
2 Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120,
P.R.China
3 Minstry of Agriculture and Rural Affairs Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Sciences, Zhejiang University, Hangzhou 310058, P.R.China
4 Technical Centre for Animal Plant and Food Inspection and Quarantine, Shanghai Customs, Shanghai 200135, P.R.China
5 Chinese Academy of Inspection and Quarantine, Beijing 100176, P.R.China
6 China National Center for Biotechnology Development, Beijing 100039, P.R.China
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摘要  

外来入侵物种是指被引入到其原产地范围以外的地区,并对当地的经济、生物多样性和生态环境造成了严重危害的物种。明确入侵性的遗传机制对于开发生态友好型的方法来防控外来入侵物种至关重要,同时,揭示外来物种的基因组特征有利于准确预测其入侵性潜能。然而,尽管已经有大量外来入侵物种的基因组被测序,但这些数据存放零散,缺少一个综合的外来入侵物种基因组数据管理与分析平台。因此,我们通过文献调研和数据库检索,收集了已完成基因组测序的外来入侵物种的组学相关数据,构建了InvasionDB数据库。该数据库包含131个外来入侵物种(100种入侵动物和31种入侵植物)的基因组和转录组数据,其中,76个物种的基因组被详细注释,并提供基因功能(包括Pfam、KEGG和NR注释信息)查询、序列比对和基因组可视化,以及全部数据下载功能。为了提供更多与入侵性相关的信息,我们进一步分析了19个与入侵昆虫的入侵性相关的基因家族,以及非编码RNAs(包括135494 个miRNAs, 89294 个rRNAs和2671941 个tRNAs)。因此,InvasionDB对于从基因组水平来研究外来入侵物种的入侵性,以及发展新的防控技术具有重要指导意义。




Abstract  
Invasive alien species (IAS) are species whose introduction to areas outside of their native range cause harm to economics, biodiversity, and the environment.  Understanding the genetic basis of invasiveness is critical for preventing invasion by an alien species and managing IAS with eco-friendly control methods.  In addition, uncovering the genomic features of IAS is essential for accurately predicting invasiveness.  However, even though increasing efforts have been devoted to sequencing the genomes of IAS, there is still not an integrated genome database for the invasive biology community.  Here, we first determined a list of invasive plants and animals by mining references and databases.  Then, we retrieved the genomic and gene data of these IAS, and constructed a database, InvasionDB.  InvasionDB encompasses 131 IAS genomes, 76 annotated IAS assemblies, and links these data to conventional functions such as searching for gene coding sequences and Pfam, KEGG, NR annotations, BLAST server, JBrowse, and downloads services.  Next, we analyzed 19 invasiveness-related gene families which confer invasiveness in insects.  To study the roles of noncoding RNA in invasiveness, we also annotated 135 494 miRNAs, 89 294 rRNAs, and 2 671 941 tRNAs from these IAS.  In summary, InvasionDB is useful for studying the invasiveness at the genomic level, and thus helps to develop novel management strategies to control IAS.
 
Keywords:  database        invasive alien species        genomic        gene family        non-coding RNA  
Received: 30 December 2019   Accepted:
Fund: This work was supported by the National Key Research and Development Program of China (2017YFC1200600 and 2016YFC1200602), the Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences (caascx-2017-2021-IAS) and the Shenzhen Science and Technology Program, China (KQTD20180411143628272).
Corresponding Authors:  Correspondence YANG Nian-wan, Tel: +86-10-82109572, E-mail: yangnianwan@caas.cn    
About author:  HUANG Cong, Mobile: +86-18040634335, E-mail: huangcong16 @163.com; LANG Kun, Mobile: +86-18715170767, E-mail: langkun1994@foxmail.com; QIAN Wan-qiang, E-mail: qianwanqiang@caas.cn; * These authors contributed equally to this study.

Cite this article: 

HUANG Cong, LANG Kun, QIAN Wan-qiang, WANG Shu-ping, CAO Xiao-mei, HE Rui, ZHAN An-ran, CHEN Meng-yao, YANG Nian-wan, LI Fei. 2021. InvasionDB: A genome and gene database of invasive alien species. Journal of Integrative Agriculture, 20(1): 191-200.

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