Abstract
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 Statement
The authors have declared no competing interest.
Footnotes
Text and figures have been subsequently improved for clarity.
