A genetic algorithm for classifying metagenomic data

The goal of metagenomic analysis is to extract relevant information concerning the organisms that have left their genetic traces in an environmental sample. Each sample is subject to nucleotide sequencing, and obtained DNA fragments are decomposed into k-mers - -short sequences of k nucleotides. Based on the found k-mers and their occurrence frequencies, it is possible to identify the organisms present in the sample - -this allows for further analysis, but requires using large taxonomic datasets. Alternatively, depending on the specific goal of the analysis, the whole sample may be classified directly based on its k-mer profile. However, this is challenging due to a large number of possible k-mers, and choosing the most valuable ones remains an open research problem. In this paper, we propose a new technique that exploits a genetic algorithm for selecting a subset of k-mer features that are used for classification. We report our initial, yet promising results obtained for the problem of detecting the type 2 diabetes from human gut metagenomic samples. We expect that the proposed classification framework will enhance the capabilities of metagenomic analysis, without the need for performing costly taxonomic classification.