Masked superstrings as a unified framework for textualk-mer set representations

Abstract

AbstractThe popularity ofk-mer-based methods has recently led to the development of compactk-mer-set representations, such as simplitigs/Spectrum-Preserving String Sets (SPSS), matchtigs, and eulertigs. These aim to representk-mer sets via strings that contain individualk-mers as substrings more efficiently than the traditional unitigs. Here, we demonstrate that all such representations can be viewed as superstrings of inputk-mers, and as such can be generalized into a unified framework that we call the masked superstring ofk-mers. We study the complexity of masked superstring computation and prove NP-hardness for bothk-mer superstrings and their masks. We then design local and global greedy heuristics for efficient computation of masked superstrings, implement them in a program called KmerCamel, and evaluate their performance using selected genomes and pan-genomes. Overall, masked superstrings unify the theory and practice of textualk-mer set representations and provide a useful framework for optimizing representations for specific bioinformatics applications.