Matchtigs: minimum plain text representation of kmer sets

Abstract

Kmer-based methods are widely used in bioinformatics, which raises the question of what is the smallest practically usable representation (i.e. plain text) of a set of kmers. We propose a polynomial algorithm computing a minimum such representation (which was previously posed as a potentially NP-hard open problem), as well as an efficient near-minimum greedy heuristic. When compressing genomes of large model organisms, read sets thereof or bacterial pangenomes, with only a minor runtime increase, we decrease the size of the representation by up to 60% over unitigs and 27% over previous work. Additionally, the number of strings is decreased by up to 97% over unitigs and 91% over previous work. Finally we show that a small representation has advantages in downstream applications, as it speeds up queries on the popular kmer indexing tool Bifrost by 1.66x over unitigs and 1.29x over previous work.