# Spaced seeds improve k-mer-based metagenomic classification

arXiv: https://arxiv.org/abs/1502.06256 · HTML: https://mlsanity.com/paper/1502.06256/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2015.

## Built on by (most cited first, 1 of 1)

- [Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data](https://mlsanity.com/paper/2211.08267/) (2022)

## Abstract (arXiv)

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.