Background: With the advent of low cost, fast sequencing technologies metagenomic analyses are made possible. The large data volumes gathered by these techniques and the unpredictable diversity captured in them are still,

however, a challenge for computational biology.

Results: In this paper we address the problem of rapid taxonomic assignment with small and adaptive data models (< 5 MB) and present the accelerated k-mer explorer (AKE). Acceleration in AKE's taxonomic assignments is achieved

by a special machine learning architecture, which is well suited to model data collections that are intrinsically hierarchical. We report classification accuracy

reasonably well for ranks down to order, observed on a study on real world data (Acid Mine Drainage, Cow Rumen).

Conclusion: We show that the execution time of this approach is orders of magnitude shorter than competitive approaches and that accuracy is comparable.

The tool is presented to the public as a web application.