HortGenome Search Engine, a universal genomic search engine for horticultural crops

Author:

Wang Sen12,Wei Shangxiao12,Deng Yuling12,Wu Shaoyuan12,Peng Haixu12,Qing You12,Zhai Xuyang12,Zhou Shijie12,Li Jinrong12,Li Hua12,Feng Yijian12,Yi Yating12,Li Rui12,Zhang Hui12,Wang Yiding3,Zhang Renlong3,Ning Lu24,Yao Yuncong1,Fei Zhangjun56,Zheng Yi12ORCID

Affiliation:

1. Beijing University of Agriculture Beijing Key Laboratory for Agricultural Application and New Technique, College of Plant Science and Technology, , Beijing 102206, China

2. Beijing University of Agriculture Bioinformatics Center, , Beijing 102206, China

3. Beijing University of Agriculture College of Intelligent Science and Engineering, , Beijing 102206, China

4. Beijing University of Agriculture Library, , Beijing 102206, China

5. Cornell University Boyce Thompson Institute, , Ithaca, NY 14853, USA

6. USDA-ARS, Robert W. Holley Center for Agriculture and Health , Ithaca, NY 14853, USA

Abstract

Abstract Horticultural crops comprising fruit, vegetable, ornamental, beverage, medicinal and aromatic plants play essential roles in food security and human health, as well as landscaping. With the advances of sequencing technologies, genomes for hundreds of horticultural crops have been deciphered in recent years, providing a basis for understanding gene functions and regulatory networks and for the improvement of horticultural crops. However, these valuable genomic data are scattered in warehouses with various complex searching and displaying strategies, which increases learning and usage costs and makes comparative and functional genomic analyses across different horticultural crops very challenging. To this end, we have developed a lightweight universal search engine, HortGenome Search Engine (HSE; http://hort.moilab.net), which allows for the querying of genes, functional annotations, protein domains, homologs, and other gene-related functional information of more than 500 horticultural crops. In addition, four commonly used tools, including ‘BLAST’, ‘Batch Query’, ‘Enrichment analysis’, and ‘Synteny Viewer’ have been developed for efficient mining and analysis of these genomic data.

Publisher

Oxford University Press (OUP)

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