Data structures based on k-mers for querying large collections of sequencing data sets

High-throughput sequencing data sets 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 one to perform online sequence searches, yet, such a feature would be highly useful to investigators. Toward this goal, in the last few years several computational approaches have been introduced to index and query large collections of data sets. Here, we propose an accessible survey of these approaches, which are generally based on representing data sets 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.

Automated summary

High-throughput sequencing data sets are deposited in public repositories for reproducibility, but limitations exist in performing online sequence searches due to the large data size. In recent years, computational approaches based on representing data sets as sets of k-mers have been introduced to address this issue, each with its own performance and limitations.