Prefix-free graph construction is hard but reveals new genome graph insights
Towards Optimal Prefix-Free Graph Construction: NP-Hardness and Structural Insights
Computational Complexity
Summary
Making very compact graphs to represent many genome sequences is a tricky puzzle. The authors show that even simple choices to build these special graphs are really hard to solve perfectly—it's an NP-hard problem. However, they found deep connections between these prefix-free graphs and other known genome graph structures, which helps understand their shape and size better. They also provide a way to find perfect solutions when the problem is small enough to handle with smart algorithms.
What this means in practice
- •For genome data engineers: Design compact genome graphs knowing exact limits of how small prefix-free graphs can be made and their relation to other genome graph types.
- •For bioinformatics software developers: Improve algorithms for representing pangenomes by using the fixed-parameter method to find minimal prefix-free graphs when candidate segments are few.
A theory result. No direct application yet.
Authors
Andrej Baláž, Alexandru Popa
Abstract
Prefix-free parsing provides an efficient way to construct compressed representations of large and repetitive pangenomes and naturally induces a graph representation known as a prefix-free graph. In this work, we initiate a theoretical study of the problem of constructing prefix-free graphs of minimum size, where the size accounts for both the total length of distinct segment labels and the paths representing the input sequences. We show that selecting an optimal set of trigger words is NP-hard, already when triggers consist of single characters. Using a synchronized-code reduction, we extend this hardness result to every fixed trigger length and further show that the problem remains NP-hard over an alphabet of size three. We then establish a structural connection between prefix-free graphs and de Bruijn graphs. In particular, we show that every compacted de Bruijn graph can be realized as a prefix-free graph and derive a hierarchy relating the sizes of minimum pangenomic graphs, minimum prefix-free graphs, compacted de Bruijn graphs, and de Bruijn graphs. Finally, we give an exact fixed-parameter algorithm running in $O(2^q n)$ time, where $q$ is the number of distinct candidate trigger words and $n$ is the total pangenome length. Our results characterize both the computational limitations and the structural properties of optimizing prefix-free graph representations and provide a theoretical foundation for the design of compact graph representations of repetitive pangenomic data.