Reference-free k-mer based dissimilarity measures for metagenomes comparison

Metagenomics plays a crucial role in unraveling the relationship between microbial communities and the environment in which they live, allowing the development of food and environmental control techniques. Similarly, the study of microbial environments within the human body plays a crucial role towards precision medicine. In these contexts, the problem of metagenomic samples comparison is among the most challenging from the computational point of view due to the size of the datasets and to the incompleteness of microbial databases. Thus, the ability to define and efficiently compute reference-free dissimilarity measures is key to the development of effective and practical tools for metagenomes comparison.

In this work, we present a systematic experimental validation of reference-free $k$ -mer-based dissimilarity measures. To this purpose, we investigate the correlation between two popular ecological dissimilarity measures, Bray-Curtis and Jaccard, computed using reference-free and reference-based $k$ -mer approaches, for $12\le k\le 31$ . Our experiments cover both simulated and real metagenomics settings (samples from the human body and the oceans), and consider both linear and ranking correlation between the computed values. Our results support the hypothesis that the two definitions are indeed correlated for a wide range of values of $k$ , and promote the development of efficient reference-free computational tools based on $k$ -mer statistics for metagenomes comparison.