Compressive Pangenomics Using Mutation-Annotated Networks

Abstract

Pangenomics is an emerging field that uses a collection of genomes of a species instead of a single reference genome to overcome reference bias and study the within-species genetic diversity. Future pangenomics applications will require analyzing large and ever-growing collections of genomes. Therefore, the choice of data representation is a key determinant of the scope, as well as the computational and memory performance of pangenomic analyses. Current pangenome formats, while capable of storing genetic variations across multiple genomes, fail to capture the shared evolutionary and mutational histories among them, thereby limiting their applications. They are also inefficient for storage, and therefore face significant scaling challenges. In this manuscript, we propose PanMAN, a novel data structure that is information-wise richer than all existing pangenome formats - in addition to representing the alignment and genetic variation in a collection of genomes, PanMAN represents the shared mutational and evolutionary histories inferred between those genomes. By using "evolutionary compression", PanMAN achieves 5.2 to 680-fold compression over other variation-preserving pangenomic formats. PanMANs relative performance generally improves with larger datasets and it is compatible with any method for inferring phylogenies and ancestral nucleotide states. Using SARS-CoV-2 as a case study, we show that PanMAN offers a detailed and accurate portrayal of the pathogens evolutionary and mutational history, facilitating the discovery of new biological insights. We also present panmanUtils, a software toolkit that supports common pangenomic analyses and makes PanMANs interoperable with existing tools and formats. PanMANs are poised to enhance the scale, speed, resolution, and overall scope of pangenomic analyses and data sharing.