Spaced seeds improve k-mer-based metagenomic classification

TL;DR

This paper demonstrates that using spaced seeds instead of contiguous k-mers significantly enhances the accuracy of metagenomic classification, supported by extensive computational experiments and simulations.

Contribution

It introduces the use of spaced seeds in k-mer-based metagenomic classification, showing improved accuracy over traditional methods.

Findings

Spaced seeds outperform contiguous k-mers in classification accuracy.

Computational experiments confirm the effectiveness of spaced seeds.

The approach is validated through large-scale simulations.

Abstract

Metagenomics is a powerful approach to study genetic content of environmental samples that has been strongly promoted by NGS technologies. To cope with massive data involved in modern metagenomic projects, recent tools [4, 39] rely on the analysis of k-mers shared between the read to be classified and sampled reference genomes. Within this general framework, we show in this work that spaced seeds provide a significant improvement of classification accuracy as opposed to traditional contiguous k-mers. We support this thesis through a series a different computational experiments, including simulations of large-scale metagenomic projects. Scripts and programs used in this study, as well as supplementary material, are available from http://github.com/gregorykucherov/spaced-seeds-for-metagenomics.