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Background: The number of metagenomes is increasing rapidly.Computational analysis and comparison across a large number of metagenomes will lead to valuable biological information.However current methods ofmetagenomic analysis are limited by their capability for in-depth taxonomical and functional data mining among a large number of samples that each carries a complex community structure.Moreover,the complexity of configuring and operating the computational pipeline also hinders efficient data processing for the end users.