Flu‐CED: A comparative transcriptomics database of influenza virus‐infected human and animal models - Yue Wu; Jue Wang; Jing Xue; Zhiguang Xiang; Jianguo Guo; Lingjun Zhan; Qiang Wei; Qi Kong (Animal Models and Experimental Medicine)
Abstract Background The continuing emergence of influenza virus has highlighted the value of public databases and related bioinformatic analysis tools in investigating transcriptomic change caused by different influenza virus infections in human and animal models. Methods We collected a large amount of transcriptome research data related to influenza virus‐infected human and animal models in public databases (GEO and ArrayExpress), and extracted and integrated array and metadata. The gene expression matrix was generated through strictly quality control, balance, standardization, batch correction, and gene annotation. We then analyzed gene expression in different species, virus, cells/tissues or after antibody/vaccine treatment and imported sample metadata and gene expression datasets into the database. Results Overall, maintaining careful processing and quality control, we collected 8064 samples from 103 independent datasets, and constructed a comparative transcriptomics database of influenza virus named the Flu‐CED database (Influenza comparative expression database, https://flu.com‐med.org.cn/). Using integrated and processed transcriptomic data, we established a user‐friendly website for realizing the integration, online retrieval, visualization, and exploration of gene expression of influenza virus infection in different species and the biological functions involved in differential genes. Flu‐CED can quickly query single and multi‐gene expression profiles, combining different experimental conditions for comparative transcriptome analysis, identifying differentially expressed genes (DEGs) between comparison groups, and conveniently finding DEGs. Conclusion Flu‐CED provides data resources and tools for analyzing gene expression in human and animal models infected with influenza virus that can deepen our understanding of the mechanisms underlying disease occurrence and development, and enable prediction of key genes or therapeutic targets that can be used for medical research.