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arXiv · 1502.06256

Spaced seeds improve k-mer-based metagenomic classification

Karel Brinda·Maciej Sykulski·Gregory Kucherov
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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.

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Karel Brinda, Maciej Sykulski, Gregory Kucherov. 2015-07-09. Spaced seeds improve k-mer-based metagenomic classification. https://doi.org/10.1093/bioinformatics%2Fbtv419

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