PT - JOURNAL ARTICLE AU - Camille Marchet AU - Christina Boucher AU - Simon J Puglisi AU - Paul Medvedev AU - Mikaël Salson AU - Rayan Chikhi TI - Data structures based on <em>k</em>-mers for querying large collections of sequencing datasets AID - 10.1101/866756 DP - 2020 Jan 01 TA - bioRxiv PG - 866756 4099 - http://biorxiv.org/content/early/2020/12/17/866756.short 4100 - http://biorxiv.org/content/early/2020/12/17/866756.full AB - High-throughput sequencing datasets are usually deposited in public repositories, e.g. the European Nucleotide Archive, to ensure reproducibility. As the amount of data has reached petabyte scale, repositories do not allow to perform online sequence searches; yet such a feature would be highly useful to investigators. Towards this goal, in the last few years several computational approaches have been introduced to index and query large collections of datasets. Here we propose an accessible survey of these approaches, which are generally based on representing datasets as sets of k-mers. We review their properties, introduce a classification, and present their general intuition. We summarize their performance and highlight their current strengths and limitations.Competing Interest StatementThe authors have declared no competing interest.