Motivation: At present Docker technology has received increasing level of attention throughout the bioinformatics community. However, its implementation details have not yet been mastered by most biologists and applied widely in biological researches. In order to popularizing this technology in the bioinformatics and sufficiently use plenty of public resources of bioinformatics tools (Dockerfile and image of scommunity, officially and privately) in Docker Hub Registry and other Docker sources based on Docker, we introduced full and accurate instance of a bioinformatics workflow based on Docker to analyse and visualize pan-genome and biosynthetic gene clusters of a bacteria in this article, provided the solutions for mining bioinformatics big data from various public biology databases. You could be guided step-by-step through the workflow process from docker file to build up your own images and run an container fast creating an workflow. Results: We presented a BGDMdocker (bacterial genome data mining docker-based) workflow based on docker. The workflow consists of three integrated toolkits, Prokka v1.11, panX, and antiSMASH3.0. The dependencies were all written in Dockerfile, to build docker image and run container for analysing pan-genome of total 44 Bacillus amyloliquefaciens strains, which were retrieved from public ??? database. The pangenome totally includes 172,432 gene, 2,306 Core gene cluster. The visualized pangenomic data such as alignment, phylogenetic trees, maps mutations within that cluster to the branches of the tree, infers loss and gain of genes on the core-genome phylogeny for each gene cluster were presented. Besides, 997 known (MIBiG database) and 553 unknown (antiSMASH-predicted clusters and Pfam database) genes of biosynthesis gene clusters types and orthologous groups were mined in all strains. This workflow could also be used for other species pan-genome analysis and visualization. The display of visual data can completely duplicated as well as done in this paper. All result data and relevant tools and files can be downloaded from our website with no need to register. The pan-genome and biosynthetic gene clusters analysis and visualization can be fully . CC-BY 4.0 International license not peer-reviewed) is the author/funder. It is made available under a

This study focuses on the potential application of Azure cloud computing platforms in Cross-border E-commerce operations, especially in Cross-border E-commerce from China to Southeast Asia. First, we review the basic concepts and characteristics of the Azure cloud computing platform and its potential advantages in e-commerce operations, including improving efficiency, reducing costs, and supporting innovation. Then, we analyze the specific application of Azure's leading cloud technology tools, such as Azure Data Factory, Azure Machine Learning, Azure Databricks, and Azure Bot Service, in Cross-border E-commerce operations. And how to use these tools to optimize Cross-border E-commerce operations. In addition, we build an application framework of Azure in Cross-border E-commerce operation optimization, aiming to help Cross-border E-commerce enterprises make reasonable decisions in cloud technology adoption. In the final section, we summarize the main findings and contributions of this study, point out the limitations of the study, and make recommendations for future research. We expect this study to provide a reference for Cross-border E-commerce enterprises to adopt cloud technology, improve operational efficiency and reduce operating costs, and provide theoretical support for future related research.