Graphic summary
A large amount of high-throughput sequencing data generated in ecology, medicine, and pharmacy has increasingly raised the complexity of data analysis and interpretation. However, the microbiome and virome field still lacks a convenient and programming-free desktop application for the comprehensive analysis in microbiome and virome data, particularly in virome analysis and ‘Dark Matter’ exploration. Therefore, a plugin development mode desktop service, MicroWorldOmics, is proposed with a convenient and one-stop pipeline for life sciences and biomedical fields. Plugin development allows users to analyze data in parallel and interactively, while these tasks usually require advanced bioinformatician to achieve. MicroWorldOmics is a software widely used in microbiome and virome, containing 90 sub-applications, including four main functions: epidemiological analysis, metagenomic/amplicons and virome comprehensive analysis, and exploring ‘Dark matter’. More than 80 Python modules and 600 R packages are invoked in all aspects of bioinformatics analysis, statistical analysis, deep learning, and visualization. At the same time, the software supports multiple input and output results, such as GFF3, FASTA, CSV, PNG, JPG, JSON, and TXT, etc. Besides, to improve the efficiency for users, MicroWorldOmics supports being deployed in three mainstream systems: Windows, Linux, and Mac and also sets the demo/example data for users to test the benchmark easily. In summary, the development of MicroWorldOmics greatly facilitates the process of analyzing microbiome and virome data for researchers who have no programming foundation and are devoted to life science and biomedicine data analysis.
Integrating the epidemiology, metagenomic and amplicon data analysis, explaining the epidemic trend and change rule of the bacterial community.
整合病原菌流行病学、宏基因组和扩增子数据分析手段，解释菌群的流行趋势与变化规律。
The deep learning model was used to assist the analysis of virome (phage group) data.
利用深度学习模型辅助病毒组(噬菌体组)数据的分析。
Different algorithms were used to construct the interactions of microbial communities to explore the survival modes among different microbial communities.
通过不同算法构建微生物菌群的互作，以探究各菌群之间的生存模式。
